>>live from Stanford University. It's the queue covering Stanford women in data science 2020. Brought to you by Silicon Angle Media. >>Hi! And welcome to the Cube. I'm your host, Sonia, to guard. And we're live at Stanford University covering Woods Women in Data Science Conference The fifth annual one And joining us today is Daphne Koller, who is the co founder who sorry is the CEO and founder of In Citro that Daphne. Welcome to the Cube. >>Nice to be here, Sonia. Thank you for having me. So >>tell us a little bit about in Citro how you how you got founded and more about your >>role. So I've been working in the intersection of machine learning and biology and health for quite a while, and it was always a bit of an interesting journey and that the data sets were quite small and limited. We're now in a different world where there's tools that are allowing us to create massive biological data sense that I think can help us solve really significant societal problems. And one of those problems that I think is really important is drug discovery and development, where despite many important advancements, the costs just keep going up and up and up. And the question is, can we use machine learning to solve that problem >>better? And you talk about this more in your keynote, so give us a few highlights of what you talked about. So in the last, you can think of >>drug discovery development in the last 50 to 70 years as being a bit of a glass half full glass, half empty. The glass half full is the fact that there's diseases that used to be a death sentence or of sentenced, a lifelong of pain and suffering that >>are now >>addressed by some of the modern day medicines. And I think that's absolutely amazing. The >>other side of >>it is that the cost of developing new drugs has been growing exponentially and what's come to be known as the Rooms law being the inverse of Moore's law, which is the one we're all familiar with because the number of drugs approved per 1,000,000,000 U. S. Dollars just keeps going down exponentially. So the question is, can we change that curve? >>And you talked in your keynote about the interdisciplinary culture to tell us more about that? I think in >>order to address some of the critical problems that we're facing. One needs to really build a culture of people who work together at from different disciplines, each bringing their own insights and their own ideas into the mix. So and in Citro, we actually have a company. That's half life scientists, many of whom are producing data for the purpose of driving machine learning models and the other Halford machine learning people in data scientists who are working on those. But it's not a handoff where one group produces that they then the other one consumes and interpreted. But really, they start from the very beginning to understand. What are the problems that one could solve together? How do you design the experiment? How do you build the model and how do you derive insights from that that can help us make better medicines for people? >>And, um, I also wanted to ask you the you co founded coursera, so tell us a little bit more about that platform. So I found that >>coursera as a result of work that I've been doing at Stanford, working on how technology can make education better and more accessible. This was a project that I did here, number of my colleagues as well. And at some point in the fall of 2011 there was an experiment of Let's take some of the content that we've been we've been developing within within Stanford and put it out there for people to just benefit from, and we didn't know what would happen. Would it be a few 1000 people, but within a matter of weeks with minimal advertising Other than one New York Times article that went viral, we had 100,000 people in each of those courses. And that was a moment in time where, you know, we looked at it at this and said, Can we just go back to writing more papers or is there an incredible opportunity to transform access to education to people all over the world? And so I ended up taking a what was supposed to be to really absence from Stanford to go and co found coursera, and I thought I'd go back after two years, but the But at the end of that two year period, the there was just so much more to be done and so much more impact that we could bring to people all over the world, people of both genders, people of different social economic status, every single country around the world. We just felt like this was something that I couldn't not dio. >>And how did you Why did you decide to go from an educational platform to then going into machine learning and biomedicine? >>So I've been doing Corsair for about five years in 2016 and the company was on a great trajectory. But it's primarily >>a >>a content company, and around me, machine learning was transforming the world, and I wanted to come back and be part of that. And when I looked around, I saw machine learning being applied to e commerce and the natural language and to self driving cars. But there really wasn't a lot of impact being made on the life science area. I wanted to be part of making that happen, partly because I felt like coming back to your earlier comment that in order to really have that impact, you need to have someone who speaks both languages. And while there's a new generation of researchers who are bilingual in biology and machine learning, there's still a small group in there, very few of those in kind of my age cohort and I thought that I would be able to have a real impact by bullying company in the space. >>So it sounds like your background is pretty varied. What advice would you give to women who are just starting college now who may be interested in the similar field? Would you tell them they have to major in math? Or or do you think that maybe, like there's some other majors that may be influential as well? I think >>there is a lot of ways to get into data science. Math is one of them. But there's also statistics or physics. And I would say that especially for the field that I'm currently in, which is at the intersection of machine learning data science on the one hand, and biology and health on the other one can, um, get there from biology or medicine as well. But what I think is important is not to shy away from the more mathematically oriented courses in whatever major you're in, because that foundation is a really strong one. There is ah lot of people out there who are basically lightweight consumers of data science, and they don't really understand how the methods that they're deploying, how they work and that limits thumb in their ability to advance the field and come up with new methods that are better suited, perhaps, of the problems of their tackling. So I think it's totally fine. And in fact, there's a lot of value to coming into data science from fields other than now third computer science. But I think taking courses in those fields, even while you're majoring in whatever field you're interested in, is going to make you a much better person who lives at that intersection. >>And how do you think having a technology background has helped you in in founding your companies and has helped you become a successful CEO in companies >>that are very strongly R and D, focused like like in Citro and others? Having a technical co founder is absolutely essential because it's fine to have and understanding of whatever the user needs and so on and come from the business side of it. And a lot of companies have a business co founder. But not understanding what the technology can actually do is highly limiting because you end up hallucinating. Oh, if we could only do this and that would be great. But you can't and people end up often times making ridiculous promises about what's technology will or will not do because they just don't understand where the land mines sit. And, um, and where you're going to hit reels, obstacles in the path. So I think it's really important to have a strong technical foundation in these companies. >>And that being said, Where do you see in Teacher in the future? And how do you see it solving, Say, Nash, that you talked about in your keynote. >>So we hope that in Citro will be a fully integrated drug discovery and development company that is based on a completely different foundation than a traditional pharma company where they grew up. In the old approach of that is very much a bespoke scientific um, analysis of the biology of different diseases and then going after targets are ways of dealing with the disease that are driven by human intuition. Where I think we have the opportunity to go today is to build a very data driven approach that collects massive amounts of data and then let analysis of those data really reveal new hypotheses that might not be the ones that accord with people's preconceptions of what matters and what doesn't. And so hopefully we'll be able to overtime create enough data and applying machine learning to address key bottlenecks in the drug discovery development process that we can bring better drugs to people, and we can do it faster and hopefully it much lower cost. >>That's great. And you also mention in your keynote that you think the 20 twenties is like a digital biology era, so tell us more about that. So I think if >>you look, if you take a historical perspective on science and think back, you realize that there's periods in history where one discipline has made a tremendous amount of progress in relatively short amount of time because of a new technology or a new way of looking at things in the 18 seventies, that discipline was chemistry with the understanding of the periodic table, and that you actually couldn't turn lead into gold in the 19 hundreds. That was physics with understanding the connection between matter and energy in between space and time. In the 19 fifties that was computing where silicon chips were suddenly able to perform calculations that up until that point, only people have been able to >>dio. And then in 19 nineties, >>there was an interesting bifurcation. One was three era of data, which is related to computing but also involves elements, statistics and optimization of neuroscience. And the other one was quantitative biology. In which file do you move from a descriptive signs of taxonomy izing phenomenon to really probing and measuring biology in a very detailed on high throughput way, using techniques like micro arrays that measure the activity of 20,000 genes at once, or the human genome sequencing of the human genome and many others. But >>these two fields kind of >>evolved in parallel, and what I think is coming now, 30 years later, is the convergence of those two fields into one field that I like to think of a digital biology where we are able using the tools that have and continue to be developed, measure biology, an entirely new levels of detail, of fidelity of scale. We can use the techniques of machine learning and data signs to interpret what we're seeing and then use some of the technologies that are also emerging to engineer biology to do things that it otherwise wouldn't do. And that will have implications and bio materials in energy and the environment in agriculture. And I think also in human health. And it's a incredibly exciting space toe to be in right now, because just so much is happening in the opportunities to make a difference and make the world a better place or just so large. >>That sounds awesome. Stephanie. Thank you for your insight. And thanks for being on the Cube. Thank you. I'm Sonia. Taqueria. Thanks for watching. Stay tuned for more. Okay? Great. Yeah, yeah, yeah.

>>live from Stanford University. It's the queue covering Stanford women in Data Science 2020. Brought to you by Silicon Angle Media >>and welcome to the Cube. I'm your host Sonia category, and we're live at Stanford University, covering the fifth annual Woods Women in Data Science conference. Joining us today is Tilapia Williams, who's the associate professor of mathematics at Harvey Mudd College and host of Nova Wonders at PBS to leave a welcome to the Cappy to be here. Thanks for having me. So you have a lot of rules. So let's first tell us about being an associate professor at Harvey Mudd. >>Yeah, I've been at Harvey Mudd now for 11 years, so it's been really a lot of fun in the math department, but I'm a statistician by training, so I teach a lot of courses and statistics and data science and things like that. >>Very cool. And you're also a host of API s show called Novo Wonders. >>Yeah, that came about a couple of years ago. Folks at PBS reached out they had seen my Ted talk, and they said, Hey, it looks like you could be fund host of this science documentary shows So, Nova Wonders, is a six episode Siri's. It kind of takes viewers on a journey of what the cutting edge questions and science are. Um, so I got to host the show with a couple other co host and really think about like, you know, what are what are the animals saying? And so we've got some really fun episodes to do. What's the universe made of? Was one of them what's living inside of us. That was definitely a gross win. Todo figure out all the different micro organisms that live inside our body. So, yeah, it's been funded in hopes that show as well. >>And you talk about data science and AI and all that stuff on >>Yeah. Oh, yeah, yeah, one of the episodes. Can we build a Brain was dealt with a lot of data, big data and artificial intelligence, and you know, how good can we get? How good can computers get and really sort of compared to what we see in the movies? We're a long way away from that, but it seems like you know we're getting better every year, building technology that is truly intelligent, >>and you gave a talk today about mining for your own personal data. So give us some highlights from your talk. Yeah, >>so that talks sort of stemmed out of the Ted talk that I gave on owning your body's data. And it's really challenging people to think about how they can use data that they collect about their bodies to help make better health decisions on DSO ways that you can use, like your temperature data or your heart rate. Dina. Or what is data say over time? What does it say about your body's health and really challenging the audience to get excited about looking at that data? We have so many devices that collect data automatically for us, and often we don't pause on enough to actually look at that historical data. And so that was what the talk was about today, like, here's what you can find when you actually sit down and look at that data. >>What's the most important data you think people should be collecting about themselves? >>Well, definitely not. Your weight is. I don't >>want to know what that >>is. Um, it depends, you know, I think for women who are in the fertile years of life taking your daily waking temperature can tell you when your body's fertile. When you're ovulating, it can. So that information could give women during that time period really critical information. But in general, I think it's just a matter of being aware of of how your body is changing. So for some people, maybe it's your blood pressure or your blood sugar. You have high blood pressure or high blood sugar. Those things become really critical to keep an eye on. And, um, and I really encourage people whatever data they take, too, the active in the understanding of an interpretation of the data. It's not like if you take this data, you'll be healthy radio. You live to 100. It's really a matter of challenging people to own the data that they have and get excited about understanding the data that they are taking. So >>absolutely put putting people in charge of their >>own bodies. That's >>right. >>And actually speaking about that in your Ted talk, you mentioned how you were. Your doctor told you to have a C section and you looked at the data and he said, No, I'm gonna have this baby naturally. So tell us more about that. >>Yes, you should always listen to your medical pressures. But in this case, I will say that it was It was definitely more of a dialogue. And so I wasn't just sort of trying to lean on the fact that, like, I have a PhD in statistics and I know data, he was really kind of objectively with the on call doctor at the time, looking at the data >>and talking about it. >>And this doctor was this is his first time seeing me. And so I think it would have been different had my personal midwife or my doctor been telling me that. But this person would have only looked at this one chart and was it was making a decision without thinking about my historical data. And so I tried to bring that to the conversation and say, like, let me tell you more about you know, my body and this is pregnancy number three like, here's how my body works. And I think this person in particular just wasn't really hearing any of that. It was like, Here's my advice. We just need to do this. I'm like, >>Oh, >>you know, and so is gently as possible. I tried to really share that data. Um, and then it got to the point where it was sort of like either you're gonna do what I say or you're gonna have to sign a waiver. And we were like, Well, to sign the waiver that cost quite a buzz in the hospital that day. But we came back and had a very successful labor and delivery. And so, yeah, >>I think >>that at the time, >>But, >>you know, with that caveat that you should listen to what, your doctors >>Yeah. I mean, there's really interesting, like, what's the boundary between, Like what the numbers tell you and what professional >>tells me Because I don't have an MD. Right. And so, you know, I'm cautious not to overstep that, but I felt like in that case, the doctor wasn't really even considering the data that I was bringing. Um, I was we were actually induced with our first son, but again, that was more of a conversation, more of a dialogue. Here's what's happening here is what we're concerned about and the data to really back it up. And so I felt like in that case, like Yeah, I'm happy to go with your suggestion, but I could number three. It was just like, No, this isn't really >>great. Um, so you also wrote a book called Power In Numbers. The Rebel Women of Mathematics. So what inspired you to write this book? And what do you hope readers take away from it? >>A couple different things. I remember when I saw the movie hidden figures. And, um, I spent three summers at NASA working at JPL, the Jet Propulsion Laboratory. And so I had this very fun connection toe, you know, having worked at NASA. And, um, when this movie came out and I'm sitting there watching it and I'm, like ball in just crying, like I didn't know that there were black women who worked at NASA like, before me, you know, um and so it felt it felt it was just so transformative for me to see these stories just sort of unfold. And I thought, like, Well, why didn't I learn about these women growing up? Like imagine, Had I known about Katherine Johnsons of the world? Maybe that would have really inspired Not just me, but, you know, thinking of all the women of color who aren't in mathematics or who don't see themselves working at at NASA. And so for me, the book was really a way to leave that legacy to the generation that's coming up and say, like, there have been women who've done mathematics, um, and statistics and data science for years, and they're women who are doing it now. So a lot of the about 1/3 of the book are women who were still here and, like, active in the field and doing great things. And so I really wanted to highlight sort of where we've been, where we've been, but also where we're going and the amazing women that are doing work in it. And it's very visual. So some things like, Oh my gosh, >>women in math >>It is really like a very picturesque book of showing this beautiful images of the women and their mathematics and their work. And yes, I'm really proud of it. >>That's awesome. And even though there is like greater diversity now in the tech industry, there's still very few African American women, especially who are part of this industry. So what advice would you give to those women who who feel like they don't belong. >>Yeah, well, a they really do belong. Um, and I think it's also incumbent of people in the industry to sort of recognize ways that they could be advocate for women, and especially for women of color, because often it takes someone who's already at the table to invite other people to the table. And I can't just walk up like move over, get out the way I'm here now. But really being thoughtful about who's not representative, how do we get those voices here? And so I think the onus is often mawr on. People who occupy those spaces are ready to think about how they can be more intentional in bringing diversity in other spaces >>and going back to your talk a little bit. Um uh, how how should people use their data? >>Yeah, so I mean, I think, um, the ways that we've used our data, um, have been to change our lifestyle practices. And so, for example, when I first got a Fitbit, um, it wasn't really that I was like, Oh, I have a goal. It was just like I want something to keep track of my steps And then I look at him and I feel like, Oh, gosh, I didn't even do anything today. And so I think having sort of even that baseline data gave me a place to say, Okay, let me see if I hit 10 stuff, you know, 10,000 >>steps in a day or >>and so, in some ways, having the data allows you to set goals. Some people come in knowing, like, I've got this goal. I want to hit it. But for me, it was just sort of like, um and so I think that's also how I've started to use additional data. So when I take my heart rate data or my pulse, I'm really trying to see if I can get lower than how it was before. So the push is really like, how is my exercise and my diet changing so that I can bring my resting heart rate down? And so having the data gives me a gold up, restore it, and it also gives me that historical information to see like, Oh, this is how far I've come. Like I can't stop there, you know, >>that's a great social impact. >>That's right. Yeah, absolutely. >>and, um, Do you think that so in terms of, like, a security and privacy point of view, like if you're recording all your personal data on these devices, how do you navigate that? >>Yeah, that's a tough one. I mean, because you are giving up that data privacy. Um, I usually make sure that the data that I'm allowing access to this sort of data that I wouldn't care if it got published on the cover of you know, the New York Times. Maybe I wouldn't want everyone to see what my weight is, but, um, and so in some ways, while it is my personal data, there's something that's a bit abstract from it. Like it could be anyone's data as opposed to, say, my DNA. Like I'm not going to do a DNA test. You know, I don't want my data to be mapped it out there for the world. Um, but I think that that's increasingly become a concern because people are giving access to of their information to different companies. It's not clear how companies would use that information, so if they're using my data to build a product will make a product better. You know we don't see any world from that way. We don't have the benefit of it, but they have access to our data. And so I think in terms of data, privacy and data ethics, there's a huge conversation to have around that. We're only kind >>of at the beginning of understanding what that is. Yeah, >>well, thank you so much for being on the Cube. Really having you here. Thank you. Thanks. So I'm Sonia to Gary. Thanks so much for watching the cube and stay tuned for more. Yeah, yeah, yeah.

>>live from Stanford University. It's the queue covering Stanford women in data science 2020. Brought to you by Silicon Angle Media. >>Yeah, yeah, and welcome to the Cube. I'm your host Sonia Category and we're live at Stanford University, covering the fifth annual Woods Women in Data Science Conference. Joining us today is new Sha Ajami, who's the director of urban water policy for Stanford. You should welcome to the Cube. Thank you for having me. Absolutely. So tell us a little bit about your role. So >>I directed around water policy program at Stanford. We focused on building solutions for resilient cities to try to use data science and also the mathematical models to better understand how water use is changing and how we can build a future cities and infrastructure to address the needs of the people in the US, in California and across the world. >>That's great. And you're gonna give a talk today about how to build water security using big data. So give us a preview of your talk. >>Sure. So the 20th century water infrastructure model was very much of a >>top down model, >>so we built solutions or infrastructure to bring water to people, but people were not part of the loop. They were not the way that they behaved their decision making process. What they used, how they use it wasn't necessarily part of the process and the assume. There's enough water out there to bring water to people, and they can do whatever they want with it. So what we're trying to do is you want to change this paradigm and try to make it more bottom up at to engage people's decision making process and the uncertainty associated with that as part of the infrastructure planning process. Until I'll be talking, I'll talk a little bit about that. >>And where is the most water usage coming from? So, >>interestingly enough, in developed world, especially in the in the western United States, 50% of our water is used outdoors for grass and outdoor spacing, which we don't necessarily are dependent on. Our lives depend on it. I'll talk about the statistics and my talk, but grass is the biggest club you're going in the US while you're not really needing it for food consumption and also uses four times more water >>than than >>corn, which is which is a lot of water. And in California alone, if you just think about some of the spaces that we have grass or green spaces, we have our doors in the in. The in the malls are institutional buildings or different outdoor spaces. We have some of that water. If we can save, it can provide water for about a 1,000,000 or two million people a year. So that's a lot of water that we can be able to we can save and use, or you are actually a repurpose for needs that you really half. >>So does that also boil down to like people of watering their own lawns? Or is the problem for a much bigger grass message? >>Actually, interestingly enough, that's only 10% of that water out the water use. The rest of it is actually the residential water use, which is what you and I, the grass you and I have in our backyard and watering it so that water is even more than that amount that I mentioned. So we use a lot of water outdoors and again. Some of these green spaces are important for community building for making sure everybody has access to green spaces and people. Kids can play soccer or play outdoors, but really our individual lawns and outdoor spaces. If there are not really a native you know landscaping, it's not something that views enough to justify the amount of water you use for that purpose. >>So taking longer showers and all the stuff is very minimal compared to no, not >>at all. Sure, those are also very, very important. That's another 50% of our water. They're using that urban areas. It is important to be mindful the baby wash dishes. Maybe take shower the baby brush rt. They're not wasting water while you're doing that. And a lot of other individual decisions that we make that can impact water use on a daily basis. >>Right, So So tell us a little bit more about right now in California, We just had a dry February was the 1st 150 years, and you know, this is a huge issue for cities, agriculture and for potential wildfires. So tell us about your opinion about that. So, >>um, the 20th century's infrastructure model I mentioned at the beginning One of the flaws in that system is that it assumes that we will have enough snow in the mountains that would melt during the spring and summer time and would provide us water. The problem is, climate change has really, really impacted that assumption, and now you're not getting as much snow, which is comes back to the fact that this February we have not received any snow. We're still in the winter and we have spring weather and we don't really have much snow on the mountain. Which means that's going to impact the amount of water we have for summer and spring time this year. We had a great last year. We got enough water in our reservoirs, which means that you can potentially make it through. But then you have consecutive years that are dry and they don't receive a lot of water precipitation in form of snow or rain. That will become a very problematic issue to meet future water demands in California. >>And do you think this issue is along with not having enough rainfall, but also about how we store water, or do you think there should be a change in that policy? >>Sure, I think that it definitely has something also in the way we store water and be definitely you're in the 21st century. We have different problems and challenges. It's good to think about alternative ways off a storing water, including using groundwater sources. Groundwater as a way off, storing excess water or moving water around faster and making sure we use every drop of water that falls on the ground and also protecting our water supplies from contamination or pollution. >>And you see it's ever going to desalination or to get clean water. So, interestingly >>enough, I think desalination definitely has worth in other parts of the world, and then they have. Then you have smaller population or you have already tapped out of all the other options that are available to you. Desalination is expensive. Solution costs a lot of money to build this infrastructure and also again depends on you know, this centralized approach that we will build something and provide resources to people from from that location. So it's very costly to build this kind of solutions. I think for for California we still have plenty of water that we can save and repurpose, I would say, and also we still can do recycling and reuse. We can capture our stone water and reuse it, so there's so many other, cheaper, more accessible options available before you go ahead and build a desalination plants >>and you're gonna be talking about sustainable water resource management. So tell us a little bit more about that, too. So the thing with >>water mismanagement and occasionally I use also the word like building resilient water. Future is all about diversifying our water supply and being mindful of how they use our water, every drop of water that use its degraded on. It needs to be cleaned up and put back in the environment, so it always starts from the bottom. The more you save, the less impact you have on the environment. The second thing is you want to make sure every trouble wanted have used. We can use it as many times possible and not make it not not. Take it, use it, lose its right away, but actually be able to use it multiple times for different purposes. Another point that's very important, as actually majority of the water they've used on a daily basis is it doesn't need to be extremely clean drinking water quality. For example, if you tell someone that you're flushing down our toilets. Drinkable water would surprise you that we would spend this much time and resources and money and energy to clean that water to flush it down the toilet video using it. So So basically rethinking the way we built this infrastructure model is very important, being able to tailor water to the needs that we have and also being mindful of Have you use that resource? >>So is your research focus mainly on California or the local community? We actually >>are solutions that we built on our California focus. Actually, we try to build solutions that can be easily applied to different places. Having said that, because you're working from the bottom up, wavy approach water from the bottom up, you need to have a local collaboration and local perspective to bring to their to this picture on. A lot of our collaborators have been so far in California, we have had data from them. We were able to sort of demonstrate some of the assumptions we had in California. But we work actually all over the world. We have collaborators in Europe in Asia and they're all trying to do the same thing that we dio on. You're trying to sort of collaborate with them on some of the projects in other parts of the world. >>That's awesome. So going forward, what do you hope to see with sustainable water management? So, to >>be honest with you, I would often we think about technology as a way that would solve all our problems and move us out of the challenges we have. I would say technology is great, but we need to really rethink the way we manager resource is on the institutions that we have on there. We manage our data and information that we have. And I really hope that became revolutionized that part of the water sector and disrupt that part because as we disrupt this institutional part >>on the >>system, provide more system level thinking to the water sector, I'm hoping that that would change the way we manage our water and then actually opens up space for some of these technologies to come into play as >>we go forward. That's awesome. So before we leave here, you're originally from Tehran. Um and and now you're in this data science industry. What would you say to a kid who's abroad, who wants to maybe move here and have a career in data science? >>I would say Study hard, Don't let anything to disk or do you know we're all equal? Our brains are all made the same way. Doesn't matter what's on the surface. So, um so I and encourage all the girls study hard and not get discouraged and fail as many times as you can, because failing is an opportunity to become more resilient and learn how to grow. And, um and I have, and I really hope to see more girls and women in this in these engineering and stem fields, to be more active on, become more prominent. >>Have you seen a large growth within the past few years? Definitely, >>the conversation is definitely there, and there are a lot more women, and I love how Margot and her team are sort of trying to highlight the number of people who are out there. And working on these issues because that demonstrates that the field wasn't necessarily empty was just not not highlighted as much. So for sure, it's very encouraging to see how much growth you have seen over the years for sure >>you shed. Thank you so much. It's really inspiring all the work you do. Thank you for having me. So no, Absolutely nice to meet you. I'm Senator Gary. Thanks for watching the Cube and stay tuned for more. Yeah, yeah, yeah.

>> Reporter: Live from Stanford University, it's theCUBE, covering Stanford Women in Data Science 2020. Brought to you by SiliconANGLE media. >> Hi, and welcome to theCUBE. I'm your host, Sonia Tagare, and we're live at Stanford University covering the fifth annual WiDs, Women in Data Science conference. Joining us today is Emily Glassberg Sands, the Head of Data Science at Coursera, Emily, welcome to theCUBE. >> Thanks, so great to be on. >> So, tell us a little bit more about what you do at Coursera. >> Yeah, absolutely, so Coursera is the world's largest platform for higher education. We partner with about 160 universities and 20 industry partners and we provide top learning content from data science to child nutrition to about 50 million learners around the world. I lead the end to end data team so spanning data engineering, data science and machine learning. >> Wow, and we just had Daphne Koller on earlier this morning who is the co-founder of Coursera and she's also the one who hired you. >> Yeah. >> So tell us more about that relationship. >> Well, I love Daphne, I think the world of her, as I will talk about shortly, she actually didn't hire me from the start. The first answer I got one from Coursera was a no, that the company wasn't quite ready for someone who wasn't a full blown coder. But I eventually talked to her into bringing me on board, and she's been an inspiration ever since. I think one of my first memories of Daphne was when she was painting the vision of what's possible with online education, and she said, "think about the first movie." The first movie was literally just filming a play on stage. You'll appreciate this, given your background in film, and then fast forward to today and think about what's possible in movies that could never be possible on the brick-and-mortar stage. And the analog she was creating was the first MOOC, the first Massive Open Online Course was very simply filming a professor in a classroom. But she was thinking forward to today and tomorrow and five years from now, and what's possible in terms of how data and technology can transform, how educators teach and how learners learn. >> That's very cool. So, how has Coursera changed from when she started it to now? >> So, it's evolved a lot. So, I've been at Coursera about six years, when I joined the company, it had less than 50 people. Today we're 10 times that size, we have 500. I think there have been obviously dramatic growth in the platform over all the three main changes to our business model. The first is we've moved from partnering exclusively with universities to recognizing that actually, a lot of the most important education for folks in the labor market is being taught within companies. So, Google is super incentivized to train people in Google Cloud, Amazon and AWS. Folks need to learn Tableau and a whole host of other software's. So, we've expanded to including education that's provided not just by top institutions like Stanford, but also by top institutions that are companies like Amazon and Google. The second big change is we've recognized that while for many learners and individual course or a MOOC is sufficient, some learners need access to full degree, a diploma bearing credential. So we've moved to the degree space we now have 14 degrees live on the platform masters in computer science and data science but also in business, accounting, and so on. And the third major changes, I think just sort of as the world has evolved to recognize that folks need to be learning throughout their lives. There's also general consensus that it's not just on the individuals to learn, but also on their companies to train them and governments as well, and so we launched Coursera enterprise, which is about providing learning content through employers and through governments so we can reach a wider swath of individuals who might not be able to afford it themselves. >> And how are you able to use data science to track individual, user preferences and user behavior? >> Yeah, that's a great question so you can imagine right? 50 million learners, they're from almost every country in the world from a range of different backgrounds have a bunch of different goals, And so I think what you're getting out is that so much of creating the right learning experience for each person is about personalizing that experience. And we personalized throughout the learner journey so in discovery up-front, when you first joined the platform, we ask you, what's your career goal? What role are you in today? And then we help you find the right content to close the gap. As you're moving through courses we predict whether or not you need some additional support. Whether it's a fully automated intervention like a behavioral nudge, emphasizing growth mindset, or a pedagogical nudge like recommending the right review material and provide it to you, and then we also do the same to accelerate support staff on campus. So, we identify for each individual what type of human touch might they need, and we serve up to support staff recommendations for who they should reach out to, whether it's a counselor reaching out to degree student who hasn't logged in for a while, or a TA reaching out to a degree student who's struggling with an assignment. So, data really powers all of that, understanding someone's goals, their backgrounds, the content that's going to close the gap, as well as understanding where they need additional support and what type of help we can provide. >> And how are you able to track this data, are you using AV testing? >> Yeah, great question, so the, we call it a venting level data, which basically tracks what every learner is doing as they're moving through the platform. And then we use AV testing to understand the influence of kind of our big feature. So, say we roll out a new search ranking algorithm or a new learning experience we would AV-Test that, yes to understand how learners in the new variant compared to learners in the old variant. But for many of our machine learn systems, we're actually doing more of a multi-armed bandit approach where on the margin, we're changing a little bit the experience people have to understand what effect that has on their downstream behavior, separate from this mass hold-in or hold-out AV-Test. >> And so today, you're giving a talk about Coursera's latest data products so give us a little insight about that. >> So, I'm covering three data products that we've launched over the last couple of years. The first two are oriented around really helping learners be successful in the learning experience. So the first is predicting when learners are going to need additional nudges and intervening in fully automated ways to get them back on track. The second is about identifying learners who need human support and serving up really easily interpretable insights to support staff so they can reach out to the right learner with the right help. And then the third is a little bit different. It's about once learners are out in the labor market, how can they credibly signal what they know, so that they can be rewarded for that learning on the job. And this is a product called skill scoring, where we're actually measuring what skills each learner has up to what level so I can for example, compare that to the skills required in my target career or show it to my employer so I can be rewarded for what I know. >> That can be really helpful when people are creating resumes, by ranking how much of a skill that they have. >> Absolutely. So, it's really interesting when you talk about resumes, so many of what, so much of what's shown on resumes are traditional credentials, things like What school did you go to? what did you major in? what jobs have you had? And as you and I both know, there's unequal access to the school you go to or the early jobs you get. And so, part of the motivation behind skill scoring is to create more equitable or fair or accessible signals for the labor market. So, we're really excited about that direction. >> And do you think companies are taking that into consideration when they're hiring people who say have like a five out of five skills in computer science, but they didn't go to Stanford? >> Yeah. >> Think they're taking that >> Absolutely, I think companies are hungry to find more diverse talent and the biggest challenge is, when you look at people from diverse backgrounds, it's hard to know who has what skills. And so skill scoring provides a really valuable input, we're actually seeing it in use already by many of our enterprise customers who are using it to identify who have their internal employees is well positioned for new opportunities or new roles. For example, I may have a bunch of backend engineers, if I know who's good in math and machine learning and statistics, I can actually tap those folks to transition over to machine learning roles. And so it's used both as an external signal and external labor market, as well as an internal signal within companies. >> And just our last question here, what advice would you give to young women who are either out of college or just starting college who are interested in data science? Who maybe, don't haven't majored in a typical data science major? What advice would you give to them? >> So, I love that you asked you haven't made it, majored in a typical data science major. I'm actually an economist by training. And I think that's probably the reason why I was at first rejected from Coursera because an economist is a very strange background to go into data science. I think my primary advice to those young women would be to really not get too lost in the data science, in the math, in the algorithms and instead to remember that those are a means to an end, and the end is impact. So, think about the problems in the world that you care about. For me, it's education. For others, it's health care, or personal finance or a range of other issues. And remember that data science provides this vast set of tools that you can use to solve the problems you care about most. >> That's great, thank you so much for being on theCUBE. >> Thank you. I'm Sonia Tagare, thank you so much for watching theCUBE and stay tuned for more. (upbeat music)

>> Narrator: Live from Stanford University, it's theCUBE! Covering Stanford Women in Data Science 2020, brought to you by SiliconAngle Media. >> Hi, and welcome to the cube, I'm your host, Sonia Tagare. And we're live at Stanford University, covering the fifth annual WiDS, Women in Data Science Conference. Joining us today is Ya XU, the head of data science at LinkedIn. Ya Welcome to the cube. >> Thank you for having me. >> So tell us a little bit about your role and about LinkedIn. >> So LinkedIn is, first of all, the biggest professional social network, where we have a massive economic graph that we have been creating with millions actually close to 700 million members and millions of companies and jobs and of course, you know, with students of skills and also schools as well as part of it. And, and I lead the data science team at LinkedIn. And my team really spans across the global presence that LinkedIn offices have. And yeah really working on various different areas. That's both thinking about how we can iterate and understand and improve our products, that we deliver to our members and our customers. And also at the same time thinking about how we can make our infrast6ructure more efficient, and thinking about how we can make our sales and marketing more efficient as well, so we really span across. >> And how has the use of data science evolved to deliver a better user experience for users of LinkedIn? >> Yeah, so first of all, I think we LinkedIn in general, we truly believe that everybody can benefit from better data, better data access, in general. So we're certainly using data to continuously understand better of what our members are looking for. As a simple example, is that whenever we launch new feature, we're not just  blindly deciding ourselves what is the better feature for our members, but we actually understand how our users are reacting to it. Right? So we use data to understand that, and then certainly making decisions, and whether we should be eventually launching this feature to all members or not. So that's a very prominent way for us to use data. And obviously, we also use data to understand and just even before we build certain features. Is this sort of feature that's right feature to build. We do both survey and understand the survey data, but also at the same time understanding just user behavior data for us to be able to come up with better features for users. >> And do you use AB testing as well? >> Oh absolutely, Yeah. So we do a lot of AV experiments. That's what, I was not trying to use that word by that like that terminology, but this is what we use to have an understanding of user features that we are developing, that we are putting in front of our users. Is that what they enjoy as much as we think they will enjoy? >> Right, so you had a talk today about creating global economic opportunities with responsible data. So give us some highlights from your talk. >> So, first of all, at LinkedIn we we truly believe in the vision that we are working towards, which is really creating economic opportunity for every member of the global workforce. And if you're kind of starting from that, and thinking about that is our sort of the axiom that we're working towards, and then thinking about how you can do that, and obviously, the sort of the table stake or just the fundamental thing that we have to start with is to be able to preserve the privacy of our members as we are leveraging the data that our members entrust with us. Right, so how can we do that? We have some early effort in using and developing differential privacy as a technique for us to do a lot better. Always regarding preserving their privacy as we're leveraging the data, but also at the same time, it doesn't ends there, right? Because you're thinking about creating opportunity. It's not just about to preserve their privacy, but also, when we are leveraging the data, how can we leverage the data in a way that is able to create opportunity in a fair way? So here is also a lot of effort that we're having with regarding, how can we do that? And what does fairest mean? What are the ways we can actually turn some of the key concepts that we have into action that is really able to drive the way we develop product, the way that we think about responsible design, and the way that we build our algorithms, the way that we measure in every single dimension. >> And and speaking about that bias, at the opening address, they mentioned that diversity is really great because it provides many perspectives, and also helps reduce this bias. So how have you at LinkedIn been able to create a more diverse team? >> So first of all, I think it's certain we all believe that diversity is certainly better as we building product. Thinking about if you have a diverse team that is really a representation of the customer and some members that you're serving, then definitely you're able to come up with better features that is able to serve the needs of the population of our members. But also at the same time, that's just the right thing to do as well. Right, thinking about we all have had experiences we may not you know, feel as much belonging when we walk into a room that we are the only person that we identify with to be in that room. And, we certainly wanted to be able to create that environment for all the employees as well. And and thinking about, I think there is also studies that has done as what makes a high performing team. Some of the studies has done I google with the psychological safety aspects of it, which is really there's a lot of brain science that says when you make people feel they belong, that they will actually be so much more creative and innovative and everything right. So we have that belief. But tactically, there are many things that we're doing from all the divs aspect, right? How can you bring diversity, inclusion and belonging? Starting from and hiring, right? So we certainly are very much emphasized how can we increase the diversity of individuals that we're bringing to LinkedIn? And when they are at LinkedIn, can we make them feel more belonging, and feel more included in every aspects? We have different inclusion groups, right? We have I mean, obviously, I'm very much involved in Women tech. At LinkedIn we have both money efforts that we do to help women at LinkedIn in engineering, and in other groups as well to feel they belong to this community. At the same time, there is concrete actions that we're taking too. Right, that we are helping women to have a much better understanding, and aware of some of the ways that we operate that is slightly different from maybe our male colleagues will operate, right? There are certain things that we're doing to change the current processes, hiring processes, promotion process, that we are able to bring more equal footing to the way that we're thinking about gender gap and gender diversity. >> Right, that's great. And what advice would you give to women who are just starting college or who are just out of college who are interested in going into data science. >> So I want to say the biggest learning for me, is just have that can do attitude. I, you know, the woman biologically and all just like in every way, we're not any less than men. And that you certainly have seen many strong and very talented women that we have in the field. So don't let people's perceptions or biases around you to bring you down. And then thinking about what you wanted, and then just go for it, and then go for the the advice that you can get from people. And then there are so many as you can see in the conference today, so many talented women that you can reach out to who are winning and very willing to help you as well. >> And in this age of AI and ML, where do you see data science going in the future? >> That's a really interesting question. So in the way that, you know, data science I want to say is a field that is really broad, right? So if you're thinking about things that I would consider to be part of data science may not necessarily part of AI, but some of the course of influence that is extremely popular and important. And then I think the fields will continue to evolve, there are going to be and then the fields are continually overlapping with each other as well. You cannot do data science without understanding or have a strong skill in AI and machine learning. And you also can't do great machine learning without understanding the data science either. Right? So thinking about some of the talk that definitely colder earlier was sharing, as in you know, you can blind in the wrong algorithm and without realizing the bias. That all the algorithm is really just detecting the machines that's using the images versus you know, actually detecting the difference between broken bones or not right, like so. So I think having, I do see there is a continuously big overlap and I think the individuals who are involved in both communities should continue to be very comfortable being in that way too. >> Right, great. Thank you so much for being on theCUBE and thank you for your insight. >> Of course, thank you for having me. >> I'm your host, Sonia Takari. Thank you for watching theCUBE and stay tuned for more. (Upbeat music)

>>live from Stanford University. It's the queue covering Stanford women in data science 2020. Brought to you by Silicon Angle Media. Yeah. >>Hi. And welcome to the Cube. I'm your host Sonia Category. And we're live at Stanford University for the fifth annual Woods Women in Data Science Conference. Joining us today is none. Ho, the director of data Science at Intuit None. Welcome to the Cube. >>Thank you for having me here, so yeah, >>so tell us a little bit about your role at Intuit. So I leave the >>applied Machine Learning teams for our QuickBooks product lines and also for our customer success organization within my team. We do applied machine learning. So what? We specialize in building machine learning products and delivering them into our products for >>our users. Great. Today. Today you're giving a talk. You talked about how organizations want to achieve greater flexibility, speed and cost efficiencies on. And you're giving it a technical vision. Talk today about data science in the cloud world. So what should data scientists know about data science in a cloud world? >>Well, I'll just give you a little bit of a preview into my talk later because I don't want to spoil anything. Yeah, but I think one of the most important things being a data scientist in a cloud world is that you have to fundamentally change the way you work a lot of a start on our laptops or a server and do our work. But when you move to the cloud, it's like all bets are off. All the limiters are off. And so how do you fully take advantage of that? How do you change your workflow? What are some of the things that are available to you that you may not know about? And in addition to that, some some things that you have to rewire in your brain to operate in this new environment. And I'm going to share some experiences that I learned firsthand and also from my team in into its cloud migration over the past six years. >>That's great. Excited to hear that on DSO you were getting into it into it has sponsored Woods for many years now. Last year we spoke with could be the San Juan from Intuit. So tell us about this Intuit's sponsorship. Yeah, >>so into it. We are a champion of gender diversity and also all sorts of diversity. And when we first learned about which we said, We need to be a champion of the women in data science conference because for me personally, often times when I'm in a room, um, going over technical details I'm often the only woman and not just I'm often the only woman executive and so part of the sponsorship is to create this community of women, very technical women in this field, to share our work together to build this community and also to show the great diversity of work that's going on across the field of data science. >>And so Intuit has always been really great for embracing diversity. Tell us a little bit about about bad experience, about being part of Intuit and also about the tech women part. Yeah, >>so one of the things that into it that I really appreciate is we have employees groups around specific interests, and one of those employees groups is tech women at Intuit and Tech women at Intuit. The goal is to create a community of women who can provide coaching, mentorship, technical development, leadership development and I think one of the unique things about it is that it's not just focused on the technical development side, but on helping women develop into leadership positions. For me, When I first started out, there were very few women in executive positions in our field and data science is a brand new field, and so it takes time to get there. Now that I'm on the other side, one of the things that I want to do is be able to give back and coach the next generation. And so the tech women at Intuit Group allows me to do that through a very strong mentorship program that matches me and early career mentees across multiple different fields so that I can provide that coaching in that leadership development >>and speaking about like diversity. In the opening address, we heard that diversity creates perspectives, and it also takes away bias. So why gender diversity is so important into it, and how does it help take away that bias? Yeah, >>so one of the important things that I think a lot of people don't realize is when you go and you build your products, you bring in a lot of biases and how you build the product and ultimately the people who use your products are the general population for us. We serve consumer, small businesses and self employed. And if you take a look at the diversity of our customers, it mirrors the general population. And so when you think about building products, you need to bring in those diverse perspectives so you could build the best products possible because of people who are using those products come from a diverse background as well, >>right? And so now at Intuit like instead of going from a desktop based application, we're at a cloud based application, which is a big part of your talk. How do you use data Teoh for a B testing and why is it important? >>Yeah, a B testing That is a personal passion of mine, actually, because as a scientist, what we like to do is run a lot of experiments and say, Okay, what is the best thing out there so that ultimately, when you ship a new product or feature, you send the best thing possible that's verified by data, and you know exactly how users are going to react to it. When we were on desktop, they made it incredibly difficult because those were back in the days. And I don't know if you remember those put back in the days when you had a floppy disk, right or even a CD ROM's. That's how we shipped our products. And so all the changes that you wanted to make had to be contained. In the end, you really only ship it once per year. So if there's any type of testing that we did, we're bringing our users and have them use our products a little bit and then say Okay, we know exactly what we need to dio ship that out. So you only get one chance now that we're in the cloud. What that allows us to do is to test continuously via a B, testing every new feature that comes out. We have a champion Challenger model, and we can say Okay, the new version that we're shipping out is this much better than the previous one. We know it performs in this way, and then we got to make the decision. Is this the best thing to do for a customer? And so you turn what was once a one time process, a one time change management process. So one that's distributed throughout the entire year and at any one time we're running hundreds of tests to make sure that we're shipping exactly the best things for our customers. >>That's awesome. Um, so, um, what advice would you give to the next generation of women who are interested in stem but maybe feel like, Oh, I might be the only woman. I don't know if I should do this. Yeah, I think that the biggest >>thing for me was finding men's ownership, and initially, when I was very early career and even when I was doing my graduate studies for me, a mentor with someone who was in my field. But when I first joined into it, an executive in another group who is a female, said, Hey, I'd like to take your side, provide you some feedback, and this is some coaching I want to give you, And that was when I realized you don't actually need to have that person be in your field to actually guide you through to the next up. And so, for women who are going through their journey and early on, I recommend finding a mentor who is at a stage where you want to go, regardless of which field there in, because everybody has diverse perspectives and things that they can teach you as you go along. >>And how do you think Woods is helping women feel like they can do data science and be a part of the community? Yeah, I think >>what you'll see in the program today is a huge diversity of our speakers, our Panelists through all different stages of their career and all different fields. And so what we get to see is not only the time baseline of women who are in their PhDs all the way to very, very well established women. The provost of Stanford University was here today, which is amazing to see someone at the very top of the career who's been around the block. But the other thing is also the diversity and fields. When you think about data science, a lot of us think about just the tech industry. But you see it in healthcare. You see it in academia and there's a scene that wide diversity of where data science and where women who are practicing data science come from. I think it's really empowering because you can see yourself in the representation does matter quite a bit. >>Absolutely. And where do you see data science going forward? >>Oh, that is a, uh, tough and interesting question, actually. And I think that in the current environment today, we could talk about where it could go wrong or where it could actually open the doors. And for me, I'm an eternal optimist on one of the things that I think is really, really exciting for the future is we're getting to a stage where we're building models, not just for the general population. We have enough data and we have enough compute where we can build a model. Taylor just for you, for all of your life's on for me. I think that that is really, really powerful because we can build exactly the right solution to help our customers and our users succeed. Specifically, me working in the personal friend, Small business finance lease. That means I can hope that cupcake shop owner actually manage her cash flow and help her succeed to me that I think that's really powerful. And that's where data science is headed. >>None. Thank you so much for being on the Cube and thank you for your insight. Thank you so much. I'm so sorry. Thanks for watching the Cube. Stay tuned for more. Yeah, Yeah, yeah, yeah, yeah, yeah.

>>live from Stanford University. It's the queue covering Stanford women in data science 2020. Brought to you by Silicon Angle Media. >>Yeah, yeah. Hi. And welcome to the Cube. I'm your host, Sonia Atari. And we're live at Stanford University, covering the fifth annual Woods Women in Data Science Conference. Joining us today is Lillian Kearse. Keo, who's the Insights manager at Spotify. Slowly and welcome to the Cube. Thank you so much for having me. So tell us a little bit about your role at a Spotify. >>Yeah, So I'm actually one of the few insights managers in the personalization team. Um, and within my little group, we think about data and algorithms that help power the larger personalization experiences throughout Spotify. So, from your limits to discover weekly to your year and wrap stories to your experience on home and the search results, that's >>awesome. Can you tell us a little bit more about the personalization? Um, team? >>Yes. We actually have a variety of different product areas that come together to form the personalization mission, which is the mission is like the term that we use for a big department at Spotify, and we collaborate across different product areas to understand what are the foundational data sets and the foundational machine learning tools that are needed to be able to create features that a user can actually experience in the app? >>Great. Um, and so you're going to be on the career panel today? How do you feel about that? I'm >>really excited. Yeah, Yeah, the would seem is in a great job of bringing together Diverse is very, uh, it's overused term. Sometimes they're a very diverse group of people with lots of different types of experiences, which I think is core. So how I think about data science, it's a wide definition. And so I think it's great to show younger and mid career women all of the different career paths that we can all take. >>And what advice would you would you give to? Women were coming out of college right now about data science. >>Yeah, so my my big advice is to follow your interests. So there's so many different types of data science problems. You don't have to just go into a title that says data scientists or a team that says Data scientist, You can follow your interest into your data science. Use your data science skills in ways that might require a lot of collaboration or mixed methods, or work within a team where there are different types of different different types of expertise coming together to work on problems. >>And speaking of mixed methods, insights is a team that's a mixed methods research groups. So tell us more about that. Yes, I >>personally manage a data scientist, Um, user researcher and the three of us collaborate highly together across their disciplines. We also collaborate across research science, the research science team right into the product and engineering teams that are actually delivering the different products that users get to see. So it's highly collaborative, and the idea is to understand the problem. Space deeply together, be able to understand. What is it that we're trying to even just form in our head is like the need that a user work and human and user human has, um, in bringing in research from research scientists and the product side to be able to understand those needs and then actually have insights that another human, you know, a product owner you can really think through and understand the current space and like the product opportunities >>and to understand that user insight do use a B testing. >>We use a lot of >>a B testing, so that's core to how we think about our users at Spotify. So we use a lot of a B testing. We do a lot of offline experiments to understand the potential consequences or impact that certain interventions can have. But I think a B testing, you know, there's so much to learn about best practices there and where you're talking about a team that does foundational data and foundational features. You also have to think about unintended or second order effects of algorithmic a B test. So it's been just like a huge area of learning in a huge area of just very interesting outcomes. And like every test that we run, we learn a lot about not just the individual thing. We're testing with just the process overall. >>And, um, what are some features of Spotify that customers really love anything? Anything >>that's like we know use a daily mix people absolutely love every time that I make a new friend and I saw them what they work on there like I was just listening to my daily makes this morning discover weekly for people who really want >>to stay, >>you know, open to new music is also very popular. But I think the one that really takes it is any of the end of year wrapped campaigns that we have just the nostalgia that people have, even just for the last year. But in 2019 we were actually able to do 10 years, and that amount of nostalgia just went through the roof like people were just like, Oh my goodness, you captured the time that I broke up with that, you >>know, the 1st 5 years ago, or just like when I discovered that I love Taylor Swift, even though I didn't think I like their or something like that, you know? >>Are there any surprises or interesting stories that you have about, um, interesting user experiences? Yeah. >>I mean, I could give I >>can give you an example from my experience. So recently, A few a few months ago, I was scrolling through my home feed, and I noticed that one of the highly rated things for me was women in >>country, and I was like, Oh, that's kind of weird. I don't consider >>myself a country fan, right? And I was like having this moment where I went through this path of Wait, That's weird. Why would Why would this recommend? Why would the home screen recommend women in country, country music to me? And then when I click through it, um, it would show you a little bit of information about it because it had, you know, Dolly Parton. It had Margo Price and it had the high women and those were all artistes. And I've been listening to a lot, but I just had not formed an identity as a country music. And then I click through It was like, Oh, this is a great play list and I listen to it and it got me to the point where I was realizing I really actually do like country music when the stories were centered around women, that it was really fun to discover other artists that I wouldn't have otherwise jumped into as well. Based on the fact that I love the story writing and the song, writing these other country acts that >>so quickly discovered that so you have a degree in industrial mathematics, went to a liberal arts college on purpose because you want to try out different classes. So how is that diversity of education really helped >>you in your Yes, in my undergrad is from Smith College, which is a liberal arts school, very strong liberal arts foundation. And when I went to visit, one of the math professors that I met told me that he, you know, he considers studying math, not just to make you better at math, but that it makes you a better thinker. And you can take in much more information and sort of question assumptions and try to build a foundation for what? The problem that you're trying to think through is. And I just found that extremely interesting. And I also, you know, I haven't undeclared major in Latin American studies, and I studied like neuroscience and quantum physics for non experts and film class and all of these other things that I don't know if I would have had the same opportunity at a more technical school, and I just found it really challenging and satisfying to be able to push myself to think in different ways. I even took a poetry writing class I did not write good poetry, but the experience really stuck with me because it was about pushing myself outside of my own boundaries. >>And would you recommend having this kind of like diverse education to young women now who are looking >>and I absolutely love it? I mean, I think, you know, there's some people believe that instead of thinking about steam, we should be talking instead of thinking about stem. Rather, we should be talking about steam, which adds the arts education in there, and liberal arts is one of them. And I think that now, in these conversations that we have about biases in data and ML and AI and understanding, fairness and accountability, accountability bitterly, it's a hardware. Apparently, I think that a strong, uh, cross disciplinary collaborative and even on an individual level, cross disciplinary education is really the only way that we're gonna be able to make those connections to understand what kind of second order effects for having based on the decisions of parameters for a model. In a local sense, we're optimizing and doing a great job. But what are the global consequences of those decisions? And I think that that kind of interdisciplinary approach to education as an individual and collaboration as a team is really the only way. >>And speaking about bias. Earlier, we heard that diversity is great because it brings out new perspectives, and it also helps to reduce that unfair bias. So how it Spotify have you managed? Or has Spotify managed to create a more diverse team? >>Yeah, so I mean, it starts with recruiting. It starts with what kind of messaging we put out there, and there's a great team that thinks about that exclusively. And they're really pushing all of us as managers. As I seizes leaders to really think about the decisions in the way that we talk about things and all of these micro decisions that we make and how that creates an inclusive environments, it's not just about diversity. It's also about making people feel like this is where they should be. On a personal level, you know, I talk a lot with younger folks and people who are trying to just figure out what their place is in technology, whether it be because they come from a different culture, >>there are, >>you know, they might be gender, non binary. They might be women who feel like there is in a place for them. It's really about, You know, the things that I think about is because you're different. Your voice is needed even more. You know, like your voice matters and we need to figure out. And I always ask, How can I highlight your voice more? You know, how can I help? I have a tiny, tiny bit of power and influence. You know, more than some other folks. How can I help other people acquire that as well? >>Lilian, thank you so much for your insight. Thank you for being on the Cube. Thank you. I'm your host, Sonia today. Ari. Thank you for watching and stay tuned for more. Yeah, yeah.

>> Announcer: Live from Stanford University. It's theCUBE, covering Stanford Women in Data Science 2020, brought to you by SiliconANGLE Media. (upbeat music) >> Hi, and welcome to theCUBE. I'm your host, Sonia Tagare. And we're live at Stanford University covering the fifth annual WiDS Women in Data Science Conference. Joining us today is Lucy Bernholz, who is the Senior Research Scholar at Stanford University. Lucy, welcome to theCUBE. >> Thanks for having me. >> So you've led the Digital Civil Society Lab at Stanford for the past 11 years. So tell us more about that. >> Sure, so the Digital Civil Society Lab actually exists because we don't think digital civil society exists. So let me take that apart for you. Civil society is that weird third space outside of markets and outside of government. So it's where we associate together, it's where we as people get together and do things that help other people could be the nonprofit sector, it might be political action, it might be the eight of us just getting together and cleaning up a park or protesting something we don't like. So that's civil society. But what's happened over the last 30 years really is that everything we use to do that work has become dependent on digital systems and those digital systems, some tier, I'm talking gadgets, from our phones, to the infrastructure over which data is exchanged. That entire digital system is built by companies and surveilled by governments. So where do we as people get to go digitally? Where we could have a private conversation to say, "Hey, let's go meet downtown and protest x and y, or let's get together and create an alternative educational opportunity 'cause we feel our kids are being overlooked, whatever." All of that information that get exchanged, all of that associating that we might do in the digital world, it's all being watched. It's all being captured (laughs). And that's a problem because both history and political science, history and democracy theory show us that when there's no space for people to get together voluntarily, take collective action, and do that kind of thinking and planning and communicating it just between the people they want involved in that when that space no longer exists, democracies fall. So the lab exists to try to recreate that space. And in order to do that, we have to first of all recognize that it's being closed in. Secondly, we have to make real technological process, we need a whole set of different kind of different digital devices and norms. We need different kinds of organizations, and we need different laws. So that's what the lab does. >> And how does ethics play into that. >> It's all about ethics. And it's a word I try to avoid actually, because especially in the tech industry, I'll be completely blunt here. It's an empty term. It means nothing the companies are using it to avoid being regulated. People are trying to talk about ethics, but they don't want to talk about values. But you can't do that. Ethics is a code of practice built on a set of articulated values. And if you don't want to talk about values, you don't really having conversation about ethics, you're not having a conversation about the choices you're going to make in a difficult situation. You're not having a conversation over whether one life is worth 5000 lives or everybody's lives are equal. Or if you should shift the playing field to account for the millennia of systemic and structural biases that have been built into our system. There's no conversation about ethics, if you're not talking about that thing and those things. As long as we're just talking about ethics, we're not talking about anything. >> And you were actually on the ethics panel just now. So tell us a little bit about what you guys talked about and what were some highlights. >> So I think one of the key things about the ethics panel here at WiDS this morning was that first of all started the day, which is a good sign. It shouldn't be a separate topic of discussion. We need this conversation about values about what we're trying to build for, who we're trying to protect, how we're trying to recognize individual human agency that has to be built in throughout data science. So it's a good start to have a panel about it, the beginning of the conference, but I'm hopeful that the rest of the conversation will not leave it behind. We talked about the fact that just as civil society is now dependent on these digital systems that it doesn't control. Data scientists are building data sets and algorithmic forms of analysis, that are both of those two things are just coated sets of values. And if you try to have a conversation about that, at just the math level, you're going to miss the social level, you're going to miss the fact that that's humanity you're talking about. So it needs to really be integrated throughout the process. Talking about the values of what you're manipulating, and the values of the world that you're releasing these tools into. >> And what are some key issues today regarding ethics and data science? And what are some solutions? >> So I mean, this is the Women and Data Science Conference that happens because five years ago or whenever it was, the organizers realize, "Hey, women are really underrepresented in data science and maybe we should do something about that." That's true across the board. It's great to see hundreds of women here and around the world participating in the live stream, right? But as women, we need to make sure that as you're thinking about, again, the data and the algorithm, the data and the analysis that we're thinking about all of the people, all of the different kinds of people, all of the different kinds of languages, all of the different abilities, all of the different races, languages, ages, you name it that are represented in that data set and understand those people in context. In your data set, they may look like they're just two different points of data. But in the world writ large, we know perfectly well that women of color face a different environment than white men, right? They don't work, walk through the world in the same way. And it's ridiculous to assume that your shopping algorithm isn't going to affect that difference that they experience to the real world that isn't going to affect that in some way. It's fantasy, to imagine that is not going to work that way. So we need different kinds of people involved in creating the algorithms, different kinds of people in power in the companies who can say we shouldn't build that, we shouldn't use it. We need a different set of teaching mechanisms where people are actually trained to consider from the beginning, what's the intended positive, what's the intended negative, and what is some likely negatives, and then decide how far they go down that path? >> Right and we actually had on Dr. Rumman Chowdhury, from Accenture. And she's really big in data ethics. And she brought up the idea that just because we can doesn't mean that we should. So can you elaborate more on that? >> Yeah well, just because we can analyze massive datasets and possibly make some kind of mathematical model that based on a set of value statements might say, this person is more likely to get this disease or this person is more likely to excel in school in this dynamic or this person's more likely to commit a crime. Those are human experiences. And while analyzing large data sets, that in the best scenario might actually take into account the societal creation that those actual people are living in. Trying to extract that kind of analysis from that social setting, first of all is absurd. Second of all, it's going to accelerate the existing systemic problems. So you've got to use that kind of calculation over just because we could maybe do some things faster or with larger numbers, are the externalities that are going to be caused by doing it that way, the actual harm to living human beings? Or should those just be ignored, just so you can meet your shipping deadline? Because if we expanded our time horizon a little bit, if you expand your time horizon and look at some of the big companies out there now, they're now facing those externalities, and they're doing everything they possibly can to pretend that they didn't create them. And that loop needs to be shortened, so that you can actually sit down at some way through the process before you release some of these things and say, in the short term, it might look like we'd make x profit, but spread out that time horizon I don't know two x. And you face an election and the world's largest, longest lasting, stable democracy that people are losing faith in. Set up the right price to pay for a single company to meet its quarterly profit goals? I don't think so. So we need to reconnect those externalities back to the processes and the organizations that are causing those larger problems. >> Because essentially, having externalities just means that your data is biased. >> Data are biased, data about people are biased because people collect the data. There's this idea that there's some magic debias data set is science fiction. It doesn't exist. It certainly doesn't exist for more than two purposes, right? If we could, and I don't think we can debias a data set to then create an algorithm to do A, that same data set is not going to be debiased for creating algorithm B. Humans are biased. Let's get past this idea that we can strip that bias out of human created tools. What we're doing is we're embedding them in systems that accelerate them and expand them, they make them worse (laughs) right? They make them worse. So I'd spend a whole lot of time figuring out how to improve the systems and structures that we've already encoded with those biases. And using that then to try to inform the data science we're going about, in my opinion, we're going about this backwards. We're building the biases into the data science, and then exporting those tools into bias systems. And guess what problems are getting worse. That so let's stop doing that (laughs). >> Thank you so much for your insight Lucy. Thank you for being on theCUBE. >> Oh, thanks for having me. >> I'm Sonia Tagare, thanks for watching theCUBE. Stay tuned for more. (upbeat music)

>>live from Stanford University. It's the queue covering Stanford women in data Science 2020. Brought to you by Silicon Angle Media. >>Hi, and welcome to the Cube. I'm your host, Sonia today, Ari. And we're live at Stanford University covering wigs, Women in Data Science Conference 2020 And this is the fifth annual one. Joining us today is John Hoegger, who is the principal data scientist manager at Microsoft. John. Welcome to the Cube. Thanks. So tell us a little bit about your role at Microsoft. >>I manage a central data science team for myself. 3 65 >>And tell us more about what you do on a daily basis. >>Yeah, so we look at it across all the different myself. 365 products Office Windows security products has really try and drive growth, whether it's trying to provide recommendations to customers to end uses to drive more engagement with the products that they use every day. >>And you're also on the Weeds Conference Planning Committee. So tell us about how you joined and how that experience has been like, >>Yeah, actually, I was at Stanford about a week after the very first conference on. I got talking to Karen, one of this co organizers of that that conference and I found out there was only one sponsor very first year, which was WalMart Labs >>on. >>The more that she talked about it, the more that I wanted to be involved on. I thought that makes it really should be a sponsor, this initiative. And so I got details. I went back and my assessment sponsor. Ever since I've been on the committee trying it help with. I didn't find speakers on and review and the different speakers that we have each year. And it's it's amazing just to see how this event has grown over the four years. >>Yeah, that's awesome. So when you first started, how many people attended in the beginning? >>So it started off as we're in this conference with 400 people and just a few other regional events, and so was live streamed but just ready to a few universities. And ever since then it's gone with the words ambassadors and people around the world. >>Yes, and outwits has is over 60 countries on every continent except Antarctica has told them in the Kino a swell as has 400 plus attendees here and his life stream. So how do you think would has evolved over the years? >>Uh, it's it's term from just a conference to a movement. Now it's Ah, there's all these new Our regional events have been set up every year and just people coming together, I'm working together. So, Mike, self hosting different events. We had events in Redmond. I had office and also in New York and Boston and other places as well. >>So as a as a data scientist manager for many years at Microsoft, I'm I'm sure you've seen it increase in women taking technical roles. Tell us a little bit about that. >>Yeah, And for any sort of company you have to try and provide that environment. And part of that is even from recruiting and ensuring that you've got a diverse into s. So we make sure that we have women on every set of interviews to be able to really answer the question. What's it like to be a woman on this team and your old men contents of that question on? So you know that helps as faras we try, encourage more were parented some of these things demos on. I've now got a team of 30 data scientists, and half of them are women, which is great. >>That's also, um So, uh, um, what advice would you give to young professional women who are just coming out of college or who just starting college or interested in a stem field? But maybe think, Oh, I don't know if they'll be anyone like me in the room. >>Uh, you ask the questions when you interview I go for those interviews and asked, like Like, say, What's it like to be a woman on the team? All right. You're really ensuring that the teams that you're joining the companies you joined in a inclusive on and really value diversity in the workforce >>and talking about that as we heard in the opening address that diversity brings more perspectives, and it also helps take away bias from data science. How have you noticed that that bias becoming more fair, especially at your time at Microsoft? >>Yeah, and that's what the rest is about. Is just having those diverse set of perspectives on opinions in heaven. More people just looking like a data and thinking through your holiday to come. Views on and ensure has been used in the right way. >>Right. Um and so, um, what do you going forward? Do you plan to still be on the woods committee? What do you see with is going how DC woods in five years? >>Ah, yeah. I live in for this conference I've been on the committee on. I just expected to continue to grow. I think it's just going right beyond a conference. Dossevi in the podcasts on all the other initiatives that occurring from that. >>Great. >>John, Thank you so much for being on the Cube. It was great having >>you here. Thank you. >>Thanks for watching the Cube. I'm your host, Sonia, to worry and stay tuned for more. Yeah.

live from Stanford University it's the hue covering Stanford women in data science 2020 brought to you by Silicon angle media hi and welcome to the cube I'm your host Sonia - Garrett and we're live at Stanford University covering wigs women in data science conference the fifth annual one and joining us today is Daphne Koller who is the co-founder who sari is the CEO and founder of in seat row that Daphne welcome to the cube nice to be here Sonia thank you for having me so tell us a little bit about in seat row how you how it you got it founded and more about your role so I've been working in the intersection of machine learning and biology and health for quite a while and it was always a bit of a an interesting journey in that the data sets were quite small and limited we're now in a different world where there's tools that are allowing us to create massive biological data sets that I think can help us solve really significant societal problems and one of those problems that I think is really important is drug discovery development where despite many important advancements the costs just keep going up and up and up and the question is can we use machine learning to solve that problem better and you talk about this more in your keynote so give us a few highlights of what you talked about so in the last you can think of drug discovery and development in the last 50 to 70 years as being a bit of a glass half-full glass half-empty the glass half-full is the fact that there's diseases that used to be a death sentence or of the sentence still a life long of pain and suffering that are now addressed by some of the modern-day medicines and I think that's absolutely amazing the other side of it is that the cost of developing new drugs has been growing exponentially in what's come to be known as Arun was law being the inverse of Moore's Law which is the one we're all familiar with because the number of drugs approved per billion u.s. dollars just keeps going down exponentially so the question is can we change that curve and you talked in your keynote about the interdisciplinary cold to tell us more about that I think in order to address some of the critical problems that were facing one needs to really build a culture of people who work together at from different disciplines each bringing their own insights and their own ideas into the mix so and in seat row we actually have a company that's half-life scientists many of whom are producing data for the purpose of driving machine learning models and the other half are machine learning people and data scientists who are working on those but it's not a handoff where one group produces the data and the other one consumes and interpreted but really they start from the very beginning to understand what are the problems that one could solve together how do you design the experiment how do you build the model and how do you derive insights from that that can help us make better medicines for people and I also wanted to ask you you co-founded Coursera so tell us a little bit more about that platform so I founded Coursera as a result of work that I'd been doing at Stanford working on how technology can make education better and more accessible this was a project that I did here a number of my colleagues as well and at some point in the fall of 2011 there was an experiment let's take some of the content that we've been we've been developing within it's within Stanford and put it out there for people to just benefit from and we didn't know what would happen would it be a few thousand people but within a matter of weeks with minimal advertising other than one New York Times article that went viral we had a hundred thousand people in each of those courses and that was a moment in time where you know we looked at this and said can we just go back to writing more papers or is there an incredible opportunity to transform access to education to people all over the world and so I ended up taking a what was supposed to be a teary leave of absence from Stanford to go and co-found Coursera and I thought I'd go back after two years but the but at the end of that two-year period the there was just so much more to be done and so much more impact that we could bring to people all over the world people of both genders people of the different social economic status every single country around the world we I just felt like this was something that I couldn't not do and how did you why did you decide to go from an educational platform to then going into machine learning and biomedicine so I've been doing Coursera for about five years in 2016 and the company was on a great trajectory but it's primarily a Content company and around me machine learning was transforming the world and I wanted to come back and be part of that and when I looked around I saw machine learning being applied to ecommerce and the natural language and to self-driving cars but there really wasn't a lot of impact being made on the life science area and I wanted to be part of making that happen partly because I felt like coming back to our earlier comment that in order to really have that impact you need to have someone who speaks both languages and while there's a new generation of researchers who are bilingual in biology and in machine learning there's still a small group and there very few of those in kind of my age cohort and I thought that I would be able to have a real impact by building and company in the space so it sounds like your background is pretty varied what advice would you give to women who are just starting college now who may be interested in a similar field would you tell them they have to major in math or or do you think that maybe like there are some other majors that may be influential as well I think there's a lot of ways to get into data science math is one of them but there's also statistics or physics and I would say that especially for the field that I'm currently in which is at the intersection of machine learning data science on the one hand and biology and health on the other one can get there from biology or medicine as well but what I think is important is not to shy away from the more mathematically oriented courses in whatever major you're in because that found the is a really strong one there's a lot of people out there who are basically lightweight consumers of data science and they don't really understand how the methods that they're deploying how they work and that limits them in their ability to advance the field and come up with new methods that are better suited perhaps to the problems that they're tackling so I think it's totally fine and in fact there's a lot of value to coming into data science from fields other than a third computer science but I think taking courses in those fields even while you're majoring in whatever field you're interested in is going to make you a much better person who lives at that intersection and how do you think having a technology background has helped you in in founding your companies and has helped you become a successful CEO in companies that are very strongly Rd focused like like in C tro and others having a technical co-founder is absolutely essential because it's fine to have an understanding of whatever the user needs and so on and come from the business side of it and a lot of companies have a business co-founder but not understanding what the technology can actually do is highly limiting because you end up hallucinating oh if we could only do this and yet that would be great but you can't and people end up oftentimes making ridiculous promises about what technology will or will not do because they just don't understand where the land mines sit and and where you're gonna hit real obstacles and in the path so I think it's really important to have a strong technical foundation in these companies and that being said where do you see an teacher in the future and and how do you see it solving say Nash that you talked about in your keynote so we hope that in seat row we'll be a fully integrated drug discovery and development company that is based on a slightly different foundation than a traditional pharma company where they grew up in the old approach of that is very much bespoke scientific analysis of the biology of different diseases and then going after targets or our ways of dealing with the disease that are driven by human intuition where I think we have the opportunity to go today is to build a very data-driven approach that collects massive amounts of data and then let analysis of those data really reveal new hypotheses that might not be the ones that the cord with people's preconceptions of what matters and what doesn't and so hopefully we'll be able to over time create enough data and apply machine learning to address key bottlenecks in the drug discovery development process so we can bring better drugs to people and we can do it faster and hopefully at much lower cost that's great and you also mentioned in your keynote that you think that 2020s is like a digital biology era so tell us more about that so I think if you look if you take a historical perspective on science and think back you realize that there's periods in history where one discipline has made a tremendous amount of progress in a relatively short amount of time because of a new technology or a new way of looking at things in the 1870s that discipline was chemistry was the understanding of the periodic table and that you actually couldn't turn lead into gold in the 1900s that was physics with understanding the connection between matter and energy and between space and time in the 1950s that was computing where silicon chips were suddenly able to perform calculations that up until that point only people have been able to do and then in 1990s there was an interesting bifurcation one was the era of data which is related to computing but also involves elements statistics and optimization of neuroscience and the other one was quantitative biology in which biology moved from a descriptive science of techsan amaizing phenomena to really probing and measuring biology in a very detailed and a high-throughput way using techniques like microarrays that measure the activity of 20,000 genes at once Oh the human genome sequencing of the human genome and many others but these two feels kind of evolved in parallel and what I think is coming now 30 years later is the convergence of those two fields into one field that I like to think of as digital biology where we are able using the tools that have and continue to be developed measure biology in entirely new levels of detail of fidelity of scale we can use the techniques of machine learning and data science to interpret what we're seeing and then use some of the technologies that are also emerging to engineer biology to do things that it otherwise wouldn't do and that will have implications in biomaterials in energy in the environment in agriculture and I think also in human health and it's an incredibly exciting space to be in right now because just so much is happening and the opportunities to make a difference and make the world a better place are just so large that sounds awesome Daphne thank you for your insight and thank you for being on cute thank you I'm so neat agario thanks for watching stay tuned for more great

>> Narrator: Live from Stanford University in Palo Alto, California. It's theCUBE. Covering Women in Data Science Conference 2018. Brought to you by Stanford. (upbeat music) >> Welcome back to theCUBE. We are live at Stanford University for the Third Annual Women in Data Science WiDS Conference. I'm Lisa Marten and we've had a great morning so far talking with a lot the speakers and participants at this event here at Stanford, which of course is going on globally as well. Very excited to be joined by one of the Keynotes this morning at WiDS, Latanya Sweeney, the Professor of Government and Technology from Harvard. Latanya, thank you so much for stopping by theCUBE. >> Well thank you for having me. >> Absolutely. So you are a computer scientist by training. WiDS as a mentioned is in its third year, they're expecting a 100,000 people to engage. There's a 177 I think, Margot said, regional WiDS events going on right now. In 53 countries. >> Isn't that amazing? >> It is! >> It's so exciting. >> Incredible in such a short period of time. What is it about WiDS that was attraction to you saying, "Yes, I want to participate in this event." >> Well one of the issues is just simply the idea the data science represents this sort of wave of change, of how do I analyze data? How do I make it different? And the conference itself celebrating the fact that women are taking the step, is hugely important. I mean, when I was a graduate student at MIT, I was the first black woman to get a PhD in Computer Science from MIT. And sort of, no women you really just didn't see women in this area at all. So when I come to a conference like WiDS, it's huge. It's just huge to see all these walls broken down. >> I love that walls breaking down, barriers kind of evaporating. In your time though at MIT, I'd love to understand a little bit more. Were you very conscience, "Hey I'm one of the very "few females here?" (Latanya laughs) Did it bother you or were you just, "You know what, "this is my passion, and I don't care. "I'm going to keep going forward." What was that experience like? >> Well, at first I was very naive, in a belief that you know all that really mattered was the work I did. And, I never had problems with the students, but I did have lots of problems with the professors, with this idea that you had to be like them in ways that was beyond your brain or your work, in order to really be exalted by them. And so, so whether I wanted to admit it, or whether I just wanted to ignore it, it just sort of came crashing down. >> Did you have mentors at that time, or did you think, "You know what, I'm not finding anybody "that I can really follow. "I've got to by my own mentor right now." >> Right, I mean I don't think my experience is really that uncommon for women in my generation. Very difficult to find mentors who would be complete mentors, complete see themselves in you and really try to exalt you and navigate you. What women often have found is that they can find a partial person here, and a partial person there. One who can help them in this regard, or that regard, but not the same kind of idea that you would be the superstar of one of these mentors. And it's not to take away from the fact that there have been these angels in my life, who made a big difference, and so I don't want to take away from that that somehow I did this all by myself. That's not true. >> So with the conference today, one of the things that Maria Klawe said in her welcome remarks was encouraging this generation, "Don't be worried if there's something "that you're not good at." So I loved how she was sort of encouraging people to sort of, women sort of, let go of maybe some of those preconceived notions that, "I can't do this. "I'm not good at that." I think that it's very liberating and still in 2018 with the fact there is such a diversity gap, it's still so needed. What were maybe some of the three takeaways, if you will, of your Keynote this morning that you imparted on the audience? >> Was that technology design is the new policy maker. That they're making policy, the design itself is making policy, but nobody's like monitoring it. But we could in fact use data science to monitor, to show the unforeseen consequences, and in the examples that we've done that, we've had big impact on the world. >> So share some of that with us, because that's your focus. You're in... What department in Harvard? You said government? >> So I sit in the government department. >> Unforeseen consequences of technology? >> Yes. >> Tell us about that. >> Well, you know, so in the Keynote, I talked about examples where technology is basically challenging every democratic value that we have. And sort of like no one's really aware, we kind of think about it here and there, but by doing simple data science experiments, we can quantify that. We can demonstrate it, and by doing that we shore up sort of those who can help us the most; the advocates, the regulators, and journalists. And so I gave examples from my own work and from the work of my students. >> Tell me a little bit about your students actually. Are they undergrads? Do you also have graduate students as well? >> I have both. >> You have both. >> Both. The talk was about, I teach a class called Data Science to Save the World, and we tackle three to four real world problems within the semester, that we solve. And then the students love to do their own independent projects, and at the end many of those go on to be published papers. >> Wow! I feel like you need to have a cape or some sort of superhero emblem. We can work on that later. But tell me about the diversity within the student body at Harvard in your classes. Are you finding, what's maybe the ratio of men to women, for example? >> Well you know many of the universities from my time have really changed. So when I was an undergraduate the typical classroom of Harvard undergrads would be all white men, or mostly all white men. >> Lisa: Sounds like a lot of STEM's still. (Latanya laughs) >> Yeah, but now if you walk into Harvard we see a lot more diversity within the university. I'm also a faculty dean at one of the residential houses, and so the diversity is huge. However, when you start getting into computer science, you start seeing, you don't see as much diversity. But in the Data Sciences of the World course, we get students from all over. They come from different backgrounds. They come in different colors, shapes, and sizes. Each with a skillset and a desire to learn how to have impact. >> I think that desire is key. How do you help them sort of build their own confidence in terms of, regardless of what color, flavor, you know my peer group is, I like this. I want to be in this. How do you help ignite that confidence within someone that's quite new into this? >> So if you're 20 something or almost 20, and you do something that a regulator changes their laws, or a newspaper article picks up, or you're on the Today Show, that pretty much changes the course of your life, and that's what we found with the students. That some of them have done just some remarkable work that's really been picked up and exalted, and it's stayed with them. It would change the direction in which they've gone. So what we do in the course, is we teach them that there's just so many problems that are low hanging, and how to spot a problem, an issue that they can solve, and how to solve it in a way that can be have impact. And that's really what the course focus is on. >> That impact is so important to just continue to fuel someones fire, and for that person to then be empowered to be able to ignite a fire under somebody else. I think one of the things that you mentioned sort of speaks to some of the things that we're seeing in these boundaries and lines are blurring. Not just so much even on from a gender perspective, but even career path A, B, C, D, now it's data is fueling the world. Every company is becoming a company because they have to be, right, to make consumer demands and just grow and be profitable as a business. But I also I like the parallel there that these rigid maybe, more rigid lines of careers are now opening up, because like you're saying, you can make impact being a data scientist. In every sector you can influence policy and wow, what a huge opportunity. It's almost like it's infinite, right? >> Yeah. I mean if you look at even the range of talks in the conference today, you get a great sense of not only new tools in different areas, but just the sheer spectrum of areas in which data science is playing. And that these women are already working it, already have the impact. >> So, speaking of the conference today, one of the things that I think is that we're hearing, is it's not just about inspiring, I think, Maria Klawe had said in theCUBE previous to today, that she found that young women in their first semester of university college courses, are probably like the right age and time in their lives to really ignite a spark, but I think there's also sort of a reinvigoration of the women that have been in technology and STEM fields for a while. Are you feeling and hearing kind of some of the same things from your peers and colleagues here? >> Definitely. We see it at the two levels. It's really important to try to get them in freshman year before they have a discipline defined for themselves, or how they see themselves. So that you can sort of ignite that spark and keep that spark alive. But then later women who, women or others, who are already in a field and looking for a way to sort of release and redefine themselves, data science is definitely giving them that opportunity. >> It really is. So what are some of the things that you're looking forward to for your career at Harvard as 2018 moves forward? >> Well, we, you know, the students we try to tackle the big problems. Election vulnerabilities has been a big one for us, on our agenda. The privacy of publicly available data is another big one that we've been working on. Well I think that's enough for awhile. (laughs) >> Lisa: That's pretty big. >> Yeah. >> I think so. >> Yeah, we'll get those done! >> Well that and you know, designing the logo for the t-shirt cause you definitely need to have a superpower t-shirt. So last question for you, if you could give young Latanya advice, when you were just starting out college, not knowing any of this was going to happen in terms of this movement that is WiDS and 2018, what would some of those key advice points for you, for your younger self be? >> To believe in yourself. To believe in yourself and that it's going to work out. One of the things that I grew to learn was how to turn lemons into lemonade, and that turns out to be very, very powerful, because it's a way to bounce back when you're faced with things that you can't control, that people are trying to put obstacles in your way, you just sort of find another way to keep going. And the world sort of bended towards me, so that was really cool. >> And also that failure is not a bad F word, right? (Latanya laughs) >> That's absolutely correct. >> It's part of a natural course and I think any leader and whatever and just you're in whatever, country whatever ethnicity, gender, everybody has I wouldn't even say missteps, it's just part of life, but I think... >> Yeah it's just part of the what... And Harvard like I said, I am the dean in one of the faculty houses, and one of the main things that we do each, throughout the year, is invite speakers and who're accomplished in whatever area they're in, but the one thing that they all have in common is they took this really roundabout way to get where they are. And a lot of that was because failures and blocks came in the way, and that's really important I think for young adults to really understand. >> I agree. Well, Latanya, thank you so much for carving out some time to stop by and chat with us on theCUBE. We are excited to have your wisdom shared to our audience and we wish you a great rest of the conference. >> Alright, thank you very much. >> We'll see you next time on theCUBE. >> Okay. >> We want to thank you for watching theCUBE. I'm Lisa Marten. We are live from the Third Annual Women in Data Science Conference at Stanford University. Stick around after this short break, I'll be back with my next guest. (upbeat music)

>> Announcer: Live, from Stanford University, it's the Cube, covering The Women in Data Science Conference, 2017. >> Welcome back to The Cube, we are live at Stanford University, at the 2nd annual Women in Data Science Conference, this great, fantastic one day technical conference. And we are so excited to be joined by Yael Garten, who was one of the career panelists. Yael, you are the Director of Data Science at LinkedIn, welcome to the cube. >> Yeah, thank you, thanks for having me. So excited to have you here, everybody knows LinkedIn. My parents even have probably multiple LinkedIn accounts, but they do. You've served, what 400 and plus million accounts, I'd love to understand, what is the role, what's the data scientist's role in the business overall? >> Yeah, so I guess when people ask me about data science, what I love to kind of start with is there are a couple different types of data science. And so I would basically say that there are two main categories by which we use data science at LinkedIn. If you think about it, there is really data science where a product of your work is for a human to consume. So using data to help inform business or product strategy, to make better products, make more informed decisions about how you're investing your resources. So that's one side, which is often called decision sciences, or advanced analytics. Another type of data science is where the consumer of the output is a machine. Alright so rather than a human, a machine. So basically they these are things like machine learning models and recommendation systems. So we have really both of those. The second category is what we call data products. And so we use those in virtually everything we do. So on the data products, much of LinkedIn is a data product, it's really based on date. Right, our profiles, our connection graph, the way that people are engaging with LinkedIn helps us improve the product for our members and clients. And then we use that data internally, to really make better decisions, to understand, you know how can we better serve the world's professionals, and make them more productive and successful? >> Right, fantastic, so tell us a little bit about your team. It sounds like it's sort of broken into those two domains. You must have quite a, a large team, or a lean team? >> So yeah, we have, the way we have our team is that we work really closely within all of our product verticals, and we embed closely with the business, to really understand kind of what are the needs. And then we work very cross-functionally. So we will typically have in any group, sort of a product manager, and engineer, a designer, a data scientist, often it's from both kinds of data scientists. So sort of one on the analytic side, one on the machine learning side. Right, marketing, business operation, so really very cross-functional teams working together, using this data. >> Very smart, it sounds very integrated from the beginning, where they kind of by design-- >> Yes. >> So that collaboration is really sort of natural within LinkedIn? >> Yes. >> That's fantastic, very progressive. And certainly it's something that everybody benefits from. >> Yes. >> Right because as whether you're on the advanced analytic side, or on the machine learning side, you're getting exposure to the business side, vice versa, which, that's really a great environment for success. >> Yes, yeah and part of, I think, what I love about LinkedIn is actually our data culture, and how kind of data is infused in the culture of how we do things. >> Right, which is really-- >> Right, not always the case. >> It's not, and it's, cultural shifts have, we were talking about that with a number of guests today, and especially the size of the organization, that's tough. >> Yael: Yes. >> So to have that built in and that integration as part of, this is how we do business is, really you can imagine all the potential and possibilities there. So would love to understand, how is LinkedIn using data to recommend ways to evolve products and services to best serve all of it's members? >> Yeah, so maybe two different examples of how we do this, one is, what we do is every launch that we have, so every feature that we generate, we really do it at an online experimentation setting. So we have a certain feature that we're about to roll out to our members. And we want to make sure that it's a better experience for our members. And better, as measured by kind of the metrics that we've defined in terms of measures of success. And so, which is really aligned to what value we believe we're delivering our members and customers. And so when we roll out features, we'll roll it out to a certain percentage of our users, test the downstream impacts of that, and then decide, based on that, whether we actually roll that feature out to 100% of members. And so that's one of the things that my team is heavily involved in, is really helping to use that data to make sure that we are structuring things in a way that's statistically sound, so that we can measure the impacts correctly, of rolling out certain features. So that's kind of one category of work. And the other category is really to, to do sort of opportunity identification, and kind of deep-dive insights into understanding into a certain product area. Where are there opportunities to improve the product? So one, let me give you a high-level example. One of the ways we might use data is to say okay, Are certain members in certain countries accessing via iOS or Android? And if so, should we be developing more in differentiating between iOS and Android apps? It's one simple example right, where we'll actually decide our R&D investments, based on the data that we're seeing in terms of how people are using our products and do we think that that's important enough of an investment to improve the products and invest in that area? >> Wow very, very smart. What are some of the basic ways that data scientists can deliver more value for their stakeholders, whether they're internal stakeholders, across different functions within the organization, or the members, the external stakeholders? >> Yeah, I think one of the most important things is to really embed closely into these kind of functional or domain areas, and understand qualitatively and quantitatively, what's important. Right, so understanding what the business context is and what problem you're trying to solve. And I think one of the most important that data scientists play a role is actually helping to ensure are we even answering the right question? So as an example, a product manager might ask a data scientist to pull certain data, or to do a certain analysis, and a part of the conversation and the culture has to be what are you trying to get at? What are you trying to understand? And really thinking through is that even the right question to be asking? Or could we ask it in a different way? Because that's going to inform what analysis you do, right what, really what, how you're delivering the results of this analysis to make better decisions. So I think that's a big part of it is, having this iterative process of doing data science. >> Really, it sounds like such and innovative culture, and you're right, looking at the data to determine is this the right next step? Is it not? How do we maybe adapt and change based on really what this data is telling us. If we kind of look at collaboration for a second. You talked about the integrated teams, but I'm wondering how do you scale collaboration within LinkedIn across so many businesses and engineering stakeholders? >> Yeah, so the way I kind of like to think about it is, there's really, you have to invest in culture, process, and tools. So let me start from the bottom up. So on the tools or technology, one of the ways to do it, is actually to create self-served tools, to really democratize the data. So first of all investing in foundations of really good data quality, right, whether you're creating that data yourself, or you're collecting that from externally, from different organizations. Once you have really good data quality, making sure that you have foundations that enable self-serve data basically. So for example, some of the things that data scientists are used today in various companies, really doesn't need a data scientist if you've invested in ways where business partners, let's say, can quarry that data themselves. So they don't need a data scientist to be doing this role. So that's an important investment on the technology side. In addition, making data scientists really productive, by using and investing in tools that will enable them to access the data is really important. So once you have that sort of technology, it enables your data scientist to be productive. The process is really important. So just as an example we have a sort of playbook in terms of how do we launch features? And part of that is kind of bring in data insights, in terms of which features we should be building. And then once you've determined how using the data on those insights, it's okay how are we going to launch this in terms of experimental design and setting? And then what are the success metrics? How are we going to know that this actually a good-- (speaker drowned out by crashing sound) And then once we've launched the experiment, analyzing that, where all of the stakeholders are part of this right? The project manager, the executive, the engineer, the data scientist, and then kind of iterating on the results and deciding what the decision is. So having actually a process that the whole team or the company abides by, really helps at having this collaboration where it's clear what everyone is doing and kind of what's the process by which we use data to develop and to innovate? And then finally culture, I think that's such an important part, and that really needs to be sort of bottoms up, top down, everywhere. It really needs to be a community and a culture where data is discussed and where data is expected, and where decision making really is grounded on, on data. I fundamentally believe that any product being developed, or any decision being made really should be data informed if not data driven. >> Right absolutely. One of the things that I'm hearing in what you're doing is enabling some of business users to be self-sufficient. So you're taking that feedback and that input from the business side to be able to determine what tools they need to have and how you need to enable them so that you've got your resources aligned on certain products. >> Yeah, just as an example, one of the things that we do for example, is we realized over time that, this isn't actually productive, and how do we make ourselves scale, so we started doing data boot camps, for example. >> Interviewer: Okay. >> Where we'll actually train new people coming into the company, on data, and on self-serve tools, and on how to run experiments. And so a variety of different kind of aspects, and even how to work with data scientists productively. So we have actually train that >> fantastic. >> So this data boot camp really helps us to instill a data culture, and it rally empowers the team. >> So this is, anybody coming in, whether they're coming in for a marketing role, or a sales ops role, they get this data boot camp? >> Yeah. >> Wow. >> And it's open to anyone and you know, it yeah, typically is going to be a certain subset of those people, but it really is open to anyone, and we're talking about more ways of how do we scale that and maybe how we put that on LinkedIn learning and make that more broadly accessible. >> Yeah. >> Yeah. >> So you have quite a big team, how do you keep all of the data scientists that you've got happy, what are the challenges that they face, how do you evaluate those challenges and move forward so that they have an opportunity to make an impact at LinkedIn? >> Yeah, so part of the things are actually the things that I mentioned right? So a culture of data so a, it's really important when we see that this is not happening, actually addressing that. So data scientists are going to thrive in a community where data is valued, and where data scientists are valued, so that's actually a really important aspect. And you know luckily people come to use because they know that we do value data. But I think that that's very important for any company and so, I advise startups as well, and this is one of the things that I tell people that are founding companies, is you have to have a culture which values data to attract data scientists, because otherwise they have other options. The other thing is having these, these foundations that enable them to be productive. Right, so these tools and these systems that enable them to really do high-value work, and invest in the right areas. So start graduating from doing things that are more, maybe repetitive or low-level and figure out how do you scale that so that you can have data scientists really, efficiently using their time for things that only they can do? >> Right, I love that this culture is sort of grooming them. One of the things that, a couple things I read recently. One, was that, I think it was Forbes that said, 2017, the best job to apply for is data scientist. But, from an trends perspective, it's looking that by 2018, there's going to be a demand so high, there's not going to be enough talent. How are, what's your perspective on LinkedIn? Are you, have you, it sounds like from a foundational perspective, it is a data driven company that really values data, is that something that you see as a potential issue or you really have built a culture of such, not just collaboration and innovation, but education that LinkedIn is in a very good position? >> Yeah, well so one thing is that, I didn't mention in terms of the happiness factor right? Is that it is actually a place where data scientists look for a place where they can also grow and learn and be with other like-minded data scientists. So I think that's something that we strongly support, again for companies that, people that may be viewing this and are not in such environments, there are a lot of ways to do this. So keeping data scientists happy also can be facilitating meetups, right with data scientists from your local region, and so those are ways that people share information and share techniques and share challenges even right? >> Interviewer: Yeah. >> Because this a growing and evolving field. And so that's, having that community and one of the things that's amazing about this conference is that it's creating this community of data scientists that are all sharing successes and failures as data science is evolving. The other thing is that data science draws from so many different backgrounds right? >> Yeah. >> It's a broad field, right, and there's so many different kinds of data science, and even that is getting both more specialized and more broad. So I think that part of it is also looking at different backgrounds, different educational backgrounds and figuring out how can you expand the pool of people that you're looking at, you know that are data scientists? >> Interviewer: Right. >> And how do you augment what skills they may not have yet, you know, on the job or through training or through online education, and so we're looking at all of these ways so. >> That's fantastic, we've heard a lot of that today. The fact that, the core data science skills are still absolutely vital, but there's some other sort of softer skills, you talked about sharing. Communication has come up a number of times today. It's really a key, not only to be able to understand and interpret the data from a creative perspective and communicate what the data say. But to your point, to grow and learn and keep the data scientists happy, that social skill element is quite important. >> Yael: Yes. >> So that was, that was an interesting learning that I heard today, and I'm sure you've heard many interesting things today that have inspired you as well. >> Yeah, and that's something that you know, creating this culture is something that even data science leaders around the world, where we're discussing this and talking about this, you know what are the challenges? And how do we evolve this field? And how do we help define and help kind of groom the next generation of data scientists? >> Interviewer: Right. >> And to be in a more stable and be in a better place than where we were and to help to continue to evolve it, and so it is yeah. >> Evolution, it's a great word. I think that that's another theme that we've heard today and as much as I'm sure you've inspired and educated these women that are here. Not just in person today, but all the what 70, 70 cities and 25 countries it's being live streamed. >> Yael: Yeah, it was 80 cities and six continets. >> It's growing it's amazing. >> And yeah. >> And I'm sure that they'd vote a 10 from you, but it's probably just in the little bit that we've had a time to chat, I'm sure that you're probably gleaning a lot from them as well. >> Yeah, definitely, absolutely. >> And it's the, we're scratching the surface. >> Yes, absolutely and so there are many more years to come. >> Interviewer: Exactly, Yeal thank you so much for joining us on The Cube. >> Thank you, it's pleasure. >> It's a pleasure talking to you, we wish you continued success at LinkedIn. >> Thank you, it's a pleasure. >> And we want to thank you for watching The Cube. We've had a great day at the 2nd annual Women in Data Science conference at Stanford University. Join the conversation #wids2017. Thanks so much for watching, we'll see ya next time. (rhythmic music) >> Voiceover: Yeah.

>> Narrator: Live from Stanford University, it's theCUBE. Covering the Women in Data Science Conference 2017. >> Welcome back to theCUBE, we are live at Stanford at the second annual Women in Data Science Conference. I am Lisa Martin, joined by one of today's speakers from the event, Stephanie Gottlib. Stephanie, welcome to theCUBE. >> Thank you. >> You had a very interesting talk, which we'll get to in a minute, but you are currently the president of Agyleo Sport. We want to talk about that as well. You've been in the software and technology industry with oil and gas for a very long time, you've got a Bachelors, Masters, just a few years. >> Okay, thank you. >> Just you're, you've got expertise. That many people would desire. So we'd love to understand what your talk was about today, with respect to oil and gas. Data, digital transformation in oil and gas. You said "Data is the new oil." Which I just love that. Talk to us about that, what does that mean with respect to digital business transformation, and that industry? >> Yeah, so first of all, I say Data Science is definitely an area in which a woman, which I think is one of the main topic of today, will have a huge opportunity to move the needle. It's, I mean when you look at the, some numbers, I start in my talk with this example. In France, what is the proportion of women entrepreneurs involved in technology startups? And the answer is in the range of 8 to 12 percent. >> Lisa: Wow. >> I mean, in France right, I mean, economic-wise it's not perfect. But we have a long history, I think, human rights are there and so on, we are open. And to still be at this level, it's not dramatic, but to honest a lot remains to be done. And Data Science, it's a fantastic opportunity for women to change that drastically in the future. So that was cool to be invited to this presentation and see the huge potential that all those womans present for the future. So, having said that, now regarding my talk. What I wanted to bring on the table was about to put all the main foundational story to move into this new digital world. I mean, for industries which have been very conservative for a long time with old legacy aspect in it, moving to this digital world is not trivial. And you have three main components to handle with, which they have to address a bit differently. Which are about the goals, they have to adapt the way to think about, what are the new goals now? Which is mainly about asset utilization and maximizing the efficiency, the cost efficiency, the effectiveness, the safety and reliability and so on. How to integrate all of those technical new stuff, I mean, we are talking about Internet of Things, with plenty of new sensors everywhere in the field. HPC, High Performance Computing, for heavy computation, et cetera, et cetera. So that's some big topic, right? To digest for those industrial guys, and the last pillar which is, for me, the most crucial one is about the control change. Because beyond everything, you know, technical stuff. It's a matter of time, it's easy. But the control aspect is really essential. If you don't get the control right to instill some change management, you will likely fail. And a successful and valuable transformation comes with organization that have learned how to involve all of the entities, not just technical but legal, HR, accounting, sales marketing, all together to be aligned and to go to it. >> That's such a great point. Cultural evolution is critical, it's so hard. >> Stephanie: Absolutely. >> Right? You talk about whether it's a big oil company, or a big tech company, or another company that's large in another industry. Are you saying, though, I completely agree with you that cultural transit is the essential component. In oil and gas industry, how have you seen Data Science drive or influence cultural transformation? >> For sure, I mean the data now is in the center of everything. When I said, and you repeated, "Data is the new oil." Until recent past, we were driven by product centric approach. Today it's all about services and it's all about data. And that is a different paradigm that we need to integrate in the industry and in the oil and gas that I know better. To get the best benefit from it. It's a challenge but it's a fantastic and very passionate challenge to handle in the future. So that's why we have opened a center actually here, for example, in the Bay Area, to be close to the heart of what is happening in Data Science. >> Oh, fantastic, one of the things that you also said in your talk was that transformation through data analytics is equally as relevant on the operational side of a business as it is on the financial side. Expand upon that a little bit. >> Yeah, actually on the financial side, so the operational exploration prediction aspect I think it's more or less understandable. On the financial side it's a bit more hidden. But for too long our industry, I mean the oil and gas industry, have been substantially blind by not understanding how to best choose their commercial data in a holistic way. And now new startups, actually, have instilled some new way to think about that. Instill and develop new products based on machine learning combining machine learning, financial analysis. Et cetera, et cetera. Together to gain in accuracy, to gain in predictability, and a key factor is to... Get access to this information in a much faster time. And you know in our, in any industry, but in oil and gas industry time and precision cost a lot of money. >> Absolutely. What are some of the things that you would recommend to some of the young girls that are here, young women that are here, in terms of being able to influence an industry and elicit cultural change from an education perspective, is it just Data Science or what are some of the other skills and backgrounds do you think they need to be able to drive such change? >> Yeah, I think the conference was touching this point since this morning, and there is no clear answer obviously. There is no recipe, but for sure, I think many industrial today are still mirrored in the old ways. And they really need some fresh input, some fresh... Insight to really drive the culture right, the strategy right, that is necessary to move on the valuable and the successful transformation. And this fresh input, this fresh insight, I think can be completely an opportunity for woman to jump into this... This jobs or this, this aspect of the story. And with either the technical angle or the managerial angle I think it can be both right? And it's not exactly the same sort of skills that are behind. So skill wise, you know, let's be passionate. If you love the data, if you enjoy playing with the data, I think you will be perfect, doesn't matter if you are a man, a woman, I mean you are just a data scientist at the end. With skills and it's all about what you can bring and value to the company that you will work for. >> Lisa: Right. >> So go for it, I mean the Data Science world is an oyster, right? >> Absolutely. >> So go for it! >> Yes. >> I mean, really. It's a fantastic opportunity. >> It is, and some of the things that we heard today from the skills perspective is kind of opening it up or maybe broadening it a bit, absolutely the core Data Science skills are essential. The blend of hacker, statistician, mathematician, scientist, but also looking at some of the softer skills, creativity. Communication. >> Stephanie: Correct. >> And being able to understand enough of the business. >> Stephanie: Correct. >> To bring and really marry those two together. Have you seen that trend in kind of this ideal background coming up in the oil and gas industry? >> Yeah, of course, at the end of the day you've perfectly summarized all the skill set that a good data scientist needs to have. And this curiosity for the domain of application because Data Science either you can work for university then you can approach Data Science from an academic and fundamental thinking, but to be honest most of the time and most of the jobs are using Data Science for a purpose and for an application, so then you need to adapt yourself and be sure that you will have this curiosity, you need to adapt yourself to the knowledge world. And not the opposite, so this ability of adaptation, of curiosity, of passion for the type of problems or challenges, issues, that you will have to address through the Data Science world will be key, and it's really up to everybody to analyze if they want to go for it or not. >> I think that's a great point that you brought up, that adaptation. We have actually heard that a number of times today, that person needs to have the skills but also the adaptation, the flexibility. >> Stephanie: Correct. >> Along those lines, adaptation maybe, talk to us about what your current role is at Agyleo Sport. >> Yeah, with not real transition. (laughter) I moved, I quit Schlumberger a few months ago. My job, I loved my job, but I still live in France. It was difficult to be abroad so often. Anyway, I decided to change life but still I tried to stop working and I almost died. (laughter) So I decided to move forward to another challenge, really. And the new challenge is to combine and reconciliate my two passions, which are digital and sports. >> I love that, tell me more about that. >> So the idea is to raise a fund which would be the first independent fund in France, venture capital fund I mean. Addressing the sport and technology vertical. So domain, market, industry. You know sport, to make the link with what I express today, in fact sport is almost an industry like any other one. And the transformation of sport with integration of all this new tech have to be addressed and everything has to be done. So when you think how to revolutionize the way sport is handling either on the professional side or amateur side. You know, and the more I am digging into this new market for me, it's amazing. The opportunities are tremendous. And so we are pretty close to close our fund and to be, to get ready to invest in some passionating startups. Dynamic statups on this topic. I've just closed some partnership as well with, in LA, where sport tech is already booming. So it's going on and it's quite an exciting new, different, but, challenge that I am taking right now. >> It sounds so interesting. And wrapping things up, you bring up a great point that you've adapted but you've also been able to recognize the linkage between your favorite passion, sports, and technology and digital. And these days especially, we're a bit biased living in Silicon Valley where every company is a tech company, car companies et cetera. It's a really great message for the younger generation to understand, follow your passion. And there's technology there, and were going to need those diverse perspectives to help bring it to life and evolve it. >> Absolutely, so I think I realize that it's a luxury. At the point to have a choice to decide what you like to do in life, but it's also true that you have to address one in your early stage, early years, and giving you the maximum opportunities for the future is important. And then you can have this luxury, effectively to decide for your passion and to be driven by your passion. >> There's the Nirvana exactly. Well Stephanie thank you for those wise words of wisdom. Thanks so much for, >> Thank you very much. >> Stopping by theCUBE today, it's been a pleasure having you on. >> Me too, thank you. >> And we are going to be right back. We are live at the Women in Data Science Conference. Stick around, coming right back. (gentle electronic music)

>> Announcer: Live from Stanford University. It's theCUBE. Covering the Women in Data Science conference, 2017. >> Hi, welcome back to theCUBE, live from Stanford University at the second annual Women in Data Science tech conference. We are here with the COO of Products & Innovation at SAP, Sinead Kaiya. Sinead, welcome to theCUBE! >> Thanks very much! It's great to be here. >> It's great to have you. You were one of the keynote speakers today. >> Sinead: I was. >> Talk to us about your role at SAP and some of the topics that you discussed to the large audience here today. >> Yeah, absolutely. So one of the things I was happy to open my keynote with was letting them know that I'm actually not a data scientist. Because while I think it's important that that community gets together and shares their knowledge, I'm actually coming from the industry business angle. And for the young women who are here starting out in data science, I thought it's also very interesting and important for them to also hear the business perspective on data science. So that was my main contribution to the talk today. And I got a lot of great feedback, that they really appreciated getting that perspective. >> I can't imagine that you wouldn't, because data science is a boardroom conversation now. You report to the CEO. Talk to us about the connection that you help the CEO understand about the value that data science can bring to organizations like SAP. >> Right. It's actually funny. We have recently re-equipped some of our major boardrooms in SAP with huge digital touchscreens. They're absolutely phenomenal, and the reason is because the CEO truly understands, as do the board members, that the power of many of their decisions are lying today in the data. And what they don't want is a static printout on some slides or some chart that somebody hands to them. They want to be able to touch the data and explore the data, and really try to dig into it themselves. So when it comes to the question of the data, I think for CEO's this is a no-brainer. Right, they're drowning in data. They have a lot of data. They understand that. But the point of my talk today was more about the science. So I think where CEO's need to go next, is understanding that just having reams of data and being able to slice and dice it is not going to cut it anymore. You need the young women in these professions that bring the scientific discipline to that data, which is incredibly technical, around machine learning algorithms, to actually start to make sense of that data. So this is a switch for CEO's. The data is a no-brainer, but the science is a new thing that's starting to creep into the boardroom. And they're starting to learn that machine learning and these technologies are going to be very important in how they drive their businesses. >> What's the perception of that at SAP, and what are some of the things that are going on on the technology side to bring that data science in, to make sense of this data and extract value for SAP? >> So obviously SAP has a very strong portfolio of analytics products as well as our SAP HANA in-memory data platform, but where the power of it, is when we start co-innovating with our customers, because it all comes to life once it reaches the customer. So I gave a couple of examples in my keynote today, on how we're co-innovating with, for example, our customer Trenitalia. So Trenitalia is the largest provider of train service in Italy. They move about two million passengers a day. >> Wow. >> And about 80 million tons of freight a year. And they're collaborating with SAP to not only, how do you say, equip all their trains with sensors and be able to be getting that real-time data, how do they connect that with the IT data in their maintenance systems, so that when a train, let's say we know before it's going to break, before it does, and the machine already has triggered the maintenance technician, has already scheduled it, and everything happens in a very smooth and automated way. So it's once we go to the real problems that our customers are having, and we can apply our in-memory technology to their problems, that we get the real value. >> Right. That's such an interesting example. Like, intelligent train, digital train, how do those come together to enable them to meet their customers' objectives. >> Absolutely. Another interesting topic that I talked about was business without bias. So this is a new feature set that we're building into our HR systems. So SAP SuccessFactors has systems that people use for recruiting, and then taking you through the whole HR life cycle from promotions to talent management to compensation. But obviously, anybody who's been through these processes know that there's a certain element of human bias along the way. So, one of the things I talked about is how we're using machine learning to enhance our HR product, so we can try to at least identify some of the bias, if not start to remove it from the system. So... >> This is, sorry. We actually were speaking with someone on the show earlier today, who was looking at how to remove bias from the recruiting process, and creating technology for college campuses and students to be able to use. It's game-based technology, and I thought it was really interesting, because oftentimes recruiting, looking at GPA's, test scores, maybe some of those other hard factors, but now with data science and the ability to understand and add some of the behavioral insights in, really interesting applicability and how that can influence the next generation of people working for lots of different industries and companies, including SAP. >> And it's not just because it's technically interesting, or because it's the right thing to do. To take it from the CEO angle, CEO's today recognize that if they want to solve the big challenges that are on their plate, they not only need the best talent, they need the most diverse talent. But I can see from my experience, just because the CEO decides that diversity should be a corporate priority, and just because people say "yeah, we think that's a good idea," how do you actually codify that in the systems that your employees are using in the business? So the question of, do we need diversity in business, is no longer on the table. But it's rather, how do we actually start to implement that in a more systematic way, so that it's not just wishful thinking. It's actually something that's built in. >> Right. Talk to us about who your collaborators are within SAP, on things like that. Who do you work with, departmentally, function-group-wise, to help make that "yes, we understand, we need to do this" into actually real-world applicability? >> Well, one of the things I talk to, and some advice I gave the young women today, which is true for software in general, is they have to collaborate with the end user. So if you want to build in these bias checks into the HR system, do not sit alone in your laboratory. Do not sit in front of your computer and try to guess what you think is needed. Go out and shadow a recruiter for a week. Go and sit with the end user. Go and understand and truly see what their problems are, and then really involve them in the solution. So, I think that will also help when we talk about how do the young women here take all the academics and all of the, how do you say, theory that they're creating, and start to apply that in a real business context. If you haven't involved the end user, that's going to be quite hard to do. So one of the things I told them is, go to the user. >> That's great advice. I'm curious though, your perspective, coming from the business side, you know we look at data science, Forbes said it's going to be the best job to apply for in 2017. We're also seeing statistics that show, by 2018 there's going to be a shortage. The demand will be so high for data scientists that there will be a shortage. If we kind of look at the evolution of data science and where we are now, you look at the traditional skills. Stats, math, sciences, computing, maybe former hackers. Some of the things that we've heard today that I'd love to get your opinion on, being a businesswoman, is people are now saying, you know, it's the ability to be creative, to analyze and interpret, but also to communicate the information. Another thing that came up that I thought was really interesting was the factor of empathy when you're evaluating different types of data. I thought that was really interesting. I'd love to get your advice for a young woman who might be thinking about majoring in computer science, but maybe her interests really lie in sports or something that you think, is there a technology there? Well yeah. What advice would you give, and what are some of the additional core skills that you see a successful data scientist of the future needs to have? >> Right. So I love that you brought up the topic of communication, because I see in the business world, this is so important. So when you talk about competitive advantage, all of the companies can go out and hire people with, let's say, equivalent technical skills. So we can all get to the same level of technical prowess, let's say, in an industry. But do you have the people who, like you said, can apply the creativity and then find a way to communicate the results back in a superior way? So I think they are going to find that just having the technical skills in business is never enough to really break that ceiling. You have to have absolutely phenomenal communication skills. >> Definitely. >> I also gave them the advice to take a couple of business courses. It really helps to understand how the decision-makers, who you're trying to influence, what are the strategies that they use? What are the challenges that they face? And how do you actually look at some of the problems of data science more from a business perspective? I told them, what I thought is, absolutely the most hireable data scientist would be someone with some domain expertise, someone with the technical background, but somebody who also knows about business. So we need the full package. >> Absolutely! Well and that's an important point, because technology evolves. It's also the catalyst for our evolution, and naturally, any role will change and evolve. I think communication is a core, a very horizontal skill. But I definitely also would agree with your recommendations that having some business acumen in some form or fashion is really going to be key. Tell us a little bit about, what are some of the things, when somebody's coming on to SAP as a data scientist, if they maybe don't have that business background, are they able to get that within, because the culture at SAP kind of supports sort of, cross-collaboration, cross-pollination, so that they might be able to just start to learn different perspectives, to become that package that we talked about. >> Right. So in SAP, of course we have multiple opportunities for employees to either move between departments and see different areas of the company, but as a data scientist at SAP, the best experience you're going to have is working with our customers. It's one of our greatest assets and our greatest pride, is the wonderful relationship we have with hundreds of thousands of leading businesses around the world. So by joining SAP, you get to collaborate with some of the really top companies and industries. And that is when it doesn't become business theory in books. You actually get to go to the customer and see how it touches their business, and where it becomes real. And I think this is what attracts so many people to SAP, and gets them to really engage and stay at SAP, is that phenomenal customer base that we have. >> That's fantastic. Well, that real-world applicability, there isn't anything better than that. You can learn a lot of theory in textbooks, and maybe obviously be able to apply some of it, but having that expertise when something doesn't go the way that it's printed, is really really key to helping shape someone. Speaking of shaping, I'm interested in how you've been at SAP for quite some time, you've had posts in Germany and France, which is amazing. Now you're based in New York. Tell us how you've seen, because you really clearly understand the business side and you understand the importance of the business side and the data science side, the needs there and how they need to work together to drive more value, innovation, drive products, drive revenue. How have you seen SAP's culture evolve to become open to, for example, business and data science merging and being core collaborators? >> Yeah, so I mean, SAP's industry has changed a lot over the recent years. And we've done that along with our customers. So our customers are obviously in a much more tight competitive situation in the whole digitization side of things. So we've been evolving along together with them. But to go back to my other point, one of the major changes or cultural shifts that I've seen in SAP is this tight collaboration with the end user. It used to be that we were only given access to the IT departments of our customers. So we literally had to work through the filter of the IT department to find out what it is we should build. Suddenly, the IT departments are realizing that the end user in companies have quite a bit of power these days, you know. >> Lisa: Yes they do. >> And they're now opening the doors and asking us to collaborate with them, and that shift has allowed our engineers to get even closer to the end users in our customers. >> Fantastic, and I'm sure that's really a key for driving innovation. Last question for you. We're at the second annual WiDS conference. I mean, what an amazing event. Live streamed, reaching so many people. You yourself were a keynote this afternoon. Diane Greene was a keynote this morning. As you look around this very energetic atmosphere that we're in, what has inspired you? What are you going to take away from WiDS 2017 that you're like, wow, that was really fantastic? >> Well, one of the things is the diversity of the speakers. I mean, the breadth of this topic is amazing. Being a woman in tech, of course it's wonderful to see so many highly intelligent and engaged women in one room, which is something we don't usually get to see. So that's one of the other key takeaways for me. >> Fantastic. Well Sinead, we so appreciate you stopping by theCUBE. We wish you continued success as COO of Products & Innovation, and we look forward to seeing you next time on the program. >> Thanks so much! >> And we want to thank you for watching theCUBE. We are live at the second annual Women in Data Science conference, #WiDS2017, but stick around. We'll be right back.

>> Commentator: Live from Stanford University it's theCUBE covering the Women in Data Science Conference 2017. (jazzy music) >> Hi, welcome back to theCUBE. I'm Lisa Martin live at Stanford University at the second annual Women in Data Science WiDS tech conference. We are here with Ann Rosenberg from SAP. She's the VP head of Global SAP Alliances and SAP Next-Gen. Ann, welcome to the program. >> Thank you so much. >> So SAP is a sponsor of WiDS. Talk to us a little bit about that, and why is it so important for SAP to be involved in this great womens organization. >> So first of all, in my role as working with SAP's relationship to academia and also building up innovation network we see that data science is a very, very key skill set, and we also would like to see many more women get involved into this. Actually (mumbling) right now as we speak we are at the same time in 20 different countries around the world, 24 events we have. So we are both in Berlin, we are in New York, we are all over the world. So it's very important. I call it kind of a movement what we are doing here. It's important that all over the world that we inspire women to go into data science and into tech in general. So it is important thing for SAP. First of all, we need a lot of data science interested people. You also need our entire SAP ecosystem to go out to universities and be able to recruit a data science student both from a diversity perspective, whatever you are a female or a man of course. >> Absolutely, you're right. This is a very inspiring event. It's something that you can really actually feel. You're hearing a lot of applause from the speakers. When you're looking enabling even SAP people to go out and educate and recruit data scientists, what are some of the key skills that you're looking for as the next generation of data scientists? >> This is an interesting thing because you can say that you need like a very strong technical skill set, but we see more and more, and I saw that after I moved to Silicon Valley for two years that also the whole thing about design thinking, the combination of design thinking and data science is becoming something which is extremely important, but also the whole topic about empathy and also, so when you build solution you need to have this whole purpose driven in mindset. So I think what we're seeing more and more is that it's great to be a great data science, but it takes more than that. And that's what I see Stanford and Berkeley are doing a lot, that they're kind of mixing up kind of like the classes. And so you can be a strong data science, but at the same time you also have the whole design thinking background. That's some of the things that we look for at SAP. >> And that's great. We're hearing more and more of that, other skills, critical thinking, being able to not only analyze and interpret the information, but apply it and explain it in a way that really reflects the value. So I know that you have a career, you've been in industry, but you've also been a lecturer. Is this career that you're doing now, this job in alliances and next-gen for SAP sort of a match made in heaven in terms of your background? >> I actually love that question, probably the best question I ever got because it is definitely my dream job. When I was teaching in Copenhagen for some years ago I saw the mind of young people. I saw the thesis, the best of master thesis. I saw what they were able to do, and I'm an old management consultant, and I kept on thinking that the quality of work, the quality of ideas and ideations that the students come with were something that the industry could benefit so much from. So I always wanted to do this matchmaking between the industries and the mind of young people. And it's actually right now I see that it's started kind of, what I at least saw for the last two years that the industries that go to academia, go to universities to educate or to students to work on new ideas. And of course in Silicon Valley this has been going on for some time now, but we see all over the world. And the network that I'm responsible for at SAP, we work in more than 106 countries around the world, with 3,100 universities. And what I really want to do now, I call it the Silicon Valleys of the world where you are mapping the industries with academia with the accelerators and start ups. It's just an incredible innovation network, and this is what I see is just so much growing right now. So it's a great opportunity for academia, but equally also for the industry. >> I love that. Something that caught my eye, I was doing some research, and April 2016 SAP announced a collaboration with the White House's Computer Science for All Initiative. Tell us about that. >> I mean the whole DNA of SAP is in education. And therefore we do support a number of entity around the world. Whatever we talk about building up a skill set within data science, building skill set in design thinking, or in any kind of development skills is really, really important for us. So we do a lot of work together with the governments around the world. Whatever you talk about the host communication, for example, we have programs called Young Thinkers, Beatick, where you go out to high schools or you go into academia, to universities. So when this institute came up, we of course went in and said we want to support this. So if I look at United States, so we have a huge amount of universities part of the network that I'm driving with my team. So we have data curriculums, education material, we have train to train our faculties, boot camps. We do hackathons, coach games. We do around 1,200 to 1,600 hackathon coach games per year around the world. We engage with the industries out to the universities. So therefore it was a perfect match for us to kind of support this institute. >> Fantastic. Are there any things that SAP does as we look at the conference where we are, this Women in Data Science, are there things that you're doing specifically to help SAP, maybe even universities bring in more females into the programs, whether it's a university program or into SAP? >> Yeah, so for SAP in our whole recruiting process we definitely are looking into that. There is a great mix between female and male people who get hired into the company, but we also, it all start with that you actually inspire young women to go into a data science education or into a development education. So my team, we actually go in before SAP recruiting get involved where we, that's why we build up the strong relationships with universities where we inspire young women, like we do at this event here to why should they go in and have a career like this. So therefore you can see there's a lot of pre=work we need to be done for us to be able to go in and go into the recruiting process afterwards. So SAP do a lot of course in the United States, but all over the world to inspire young women to go into tech. And SAP does what we see today all over the world we have huge amount of female from SAP, female speakers at all our events who stand as role models to show that they are women, they are working for SAP, and are very, very strong female speakers and are female role models for all young women to get involved. So we do a lot of stuff to show that to the next generation of data science of whatever it is in tech. >> Yeah, and I can imagine that that's quite symbiotic. It's probably a really nice thing for that female speaker to be able to have the opportunity to share what she's doing, what she's working on, but also probably nice for her to have the opportunity to be a mentor and to help influence someone else's career. So you mentioned accelerators a minute ago, and I wanted to understand a little bit more about SAP Next-Gen Consulting, this collaboration of SAP with accelerators or start ups. How are you partnering to help accelerate innovation, and who is geared towards? Is it geared more towards student? Or is SAP also helping current business leaders to evolve and really drive digital transformation within their companies? >> So the big (mumbling) I'm working on right now too is as mentioned you said SAP Next-Gen is called SAP Next-Gen Innovation With Purpose. So it's linked to the 17 U.N. global goals. We've seen from now in Silicon Valley when you innovate you actually make innovation web purposes included. And that's why we kind of agreed on in SAP why don't we make an innovation network where the main focus is that all the innovation we get out of this is purpose driven linked to the 17 global goals. Like the event here is the goal number five, gender equality. In that network we actually do the matchmaking between academia. We look at all the disrupted new technologies, experience the technologies like machine learning like what's being discussed a lot here, block chain IOT. And then we look at the industry out there because the industries, they need all the new ideas and how to work with all the new opportunities that technology can provide, but then we also look into accelerator start ups. The huge amount, and often when you're in Silicon Valley you kind of think this is the world of the start ups of the world. So when you travel around the world, that's we we looked into a lot the last two years. We call the Silicon Valleys of the world, any big city around the world, or even smaller cities, they have tech hub. So you have Ferline Valley, you have Silicon Roundabout in London, you have Silicon Alley in New York, and that is where there is a huge amount of gravity of start ups and accelerators. And when you begin to link them together with the university network of the world and together with the industry network of the world, you suddenly realize that there is an incredible activity of creativity and ideations and start ups, and you can begin to group that into industries. And that give industries the opportunity not only to develop solution inside the company, but kind of like go in and tap into that incredible innovation network. So we work a lot with seeding in start up, early start ups into corporates, and also crowd source out to academia and the mind of young people all Next-Gen Consulting project where you similar work with students at universities on projects. It could be big data science project. It could be new applications. So I see like as the next generation type of consultancy and research what is happening in that whole network. But that is really what SAP Next-Gen is, but it is linked to the 17 U.N. global goals. It is innovation with purpose, which I'm really happy to see because I think when you build innovation, you really think about in the bigger, the whole (mumbling) thing that we know from singularity. You should think about a bigger purpose of what you're doing. >> Right, right. It sounds like though that this Next-Gen Consulting is built on a foundation of collaboration and sharing. >> It is, it is, and we have three Next-Gen lab types we set up. In this year we built, last year, we are a new year now, we built 20 Next-Gen labs at university campuses and at SAP locations. And here in the new year more labs is being set up. We are opening up a big lab in New York. We just recently opened up one in Valdov at SAP's headquarter. We have one here in Silicon Valley, and then we have a number of universities around the world where SAP's customers go in and work with academia, with educators and students because what do you do today if you're in industry? You need to find students who are strong in machine learning and all the new technologies, right? So there's a huge need for in industry now to engage with academia, an incredible opportunity for both sides. >> Right, and one last question. Who are you, in the spirit of collaboration, who do you collaborate back with at SAP corporate? Who are all the beneficiaries or the influencers of Next-Gen Consulting? >> So I collaborate, inside SAP I collaborate, SAP have a number of, we have ICN, Innovation Center Network. We have our start up focus program. We have a number of innovation, the labs, a number of basically do all our software developments, so they're heavily involved. We have our whole go to market organization with all our SAP customers and industry, I call them clubs. And then externally is of course academia, universities, and then it is the start up communities, accelerators and of course, the industry. So it is really like a matchmaking. That's like, when people ask me what do you do, and I'm a matchmaker. That's really what I am. (Lisa laughs) >> I like that, a matchmaker of technology and people all over. So you're on the planning committee for WiDS. Wrapping things up here, what does this event mean to you in terms of what you've heard today? And what are you excited about for next year's event? >> So for me, one year ago when I heard about this year I kind of said this is important, this is very important. And it's not just an event, it's a movement. And so that was where I went in and said you know, we want to be part of this, but it must be more than just an event here. It's staying for the need to be much more than that. And this is where we all teamed up, all the sponsors together with ISMIE, and we said okay, let us crowd source it out, let us live stream it out much more than ever. And this is also what the assignment is now, that we to so many locations. This is just the beginning. Next year is going to be even bigger, and it's not like that we will wait to next year. We this week announced the SAP Next-Gen global challenges linked to the 17 U.N. global goals. So we are inspiring everybody to go in and work on those global challenges, and one of them is goal number five, which is linked to this event here. So for us and for me this is just the beginning, and next year is going to be even bigger. But we are going to do so many event and activity up to next year. My team in APJ, because of the Chinese New Year, have already been planned coming up here. >> Lisa: Fantastic. >> And we have been doing pre-event, (mumbling) events. So again, it is a movement, and it's going to be big. That's for sure. >> I completely can feel that within you. And you're going to be driving this momentum to make the movement even louder, ever more visible next year. >> Ann: Yeah. >> Well Ann, thank you so much for joining us on The Cube. We're happy to have you. >> Thank you so much for the opportunity. >> And we thank you for watching The Cube. I am Lisa Martin. We are live at Stanford University at the second annual Women in Data Science Conference. Stick around, we'll be right back. (jazzy music)

>> Announcer: Live from Stanford University, it's the Cube, covering the Women in Data Science Conference 2017. (electronic music) >> Hi, and welcome back to the Cube. I'm Lisa Martin live at the Women in Data Science Conference, second annual, here at Stanford University, #WiDS2017. Fortunate to be joined next by Miriah Meyer, who is an Assistant Professor at the University of Utah in the School of Computing. Miriah, welcome to the Cube. >> Thank you. >> It's great to have you here. You're a speaker at this event this year. >> Yes. >> Tell us a little bit about how you got involved in WiDS and what excites you about being able to speak to this very passionate, invigorating audience? >> Yeah, so I got an invitation from one of the organizers, seems like quite some time ago, and when I looked into the conference, it just looked fantastic. I was so impressed with the speakers they had last year and the speakers for this year. It's a really amazing powerhouse of a community here. The fact that it's a great technical conference that, oh, just happens to be all women, it was pretty awesome, I was pretty flattered to get invited. Then the sort of, the energy in there is really awesome. It is different, it feels different than other technical conferences I go to. >> I completely agree. I love that you talked about just the community, because that's really what it is, and I think some of the, just the vibe that you can feel sitting here is one of excitement, it's one of passion of women who have been in this industry for a very long time in computer science, and then those young girls who are looking for inspiration. I think it's very symbiotic, right? They're learning from you, but I think you're probably also learning from them. >> Definitely. I find that every time I present my work to another group of people, a different community, I always have to come up against what my own assumptions are about how easy or not it is to understand the kind of work I do. I personally find it just so important to communicate clearly, it's probably partly why I do the work that I do. But I learn a lot every time I give a talk at a place like this. >> Wow, outstanding. Well, speaking of your talk, your research is in visualization systems. Share with us what you shared with the audience today, goals, outcomes, current outcomes of your visualization research. >> Mm-hmm. My research passion is around helping people make sense of complex data. I've particularly done a lot of work with scientists, particularly that in biology, where there's just been this amazing explosion of data and people are just trying to wrap their heads around what they have and what kinds of amazing discoveries they're sitting on. But it's really interesting, we've gotten so good at creating data, but then, that's wonderful, but if you can't make sense of it, who cares? >> Lisa: Right. >> I have this incredibly privileged position where I get to go and work with people who are at the cutting edge of their field and learn about this amazing work that they've been spending their lifetime on. Then I help them, I design tools with them that sometimes changes even the way that they're thinking about the problem. It's incredibly satisfying and it's very much in the spirit of team science and it's a lot of fun. I was talking about just some of the basics behind how do you create effective visualizations, which, for me, it also draws heavily on the notion of how do we collaborate effectively, how do you get at people's deep needs when it comes to making sense of data, when they often times can't articulate it themselves. I refer to it as data counseling, because it feels very much like, I talk with people who have problems but they can't articulate it, so I ask them lots of questions to help them uncover the root of their problems. >> Lisa: Right. >> That's basically what I do. >> That data counseling. That's fantastic. >> Yeah, and then you use what you discover in order to design tools. >> Share with us a little bit about the courses that you teach in Computer Science at the University of Utah. >> Yeah, so I teach a graduate level visualization course. It is just about the basic foundational principles we have behind perception and cognition and what that means for how we encode information, and then also, the process of how do you evaluate visualizations effectively. It's a really wonderful course where we have people from, actually, all across campus, so a lot of people are bringing problems that they have in other fields and trying to learn how to be more effective in their own exploration with visualizations. Then at the undergrad level, I actually teach our second semester programming course, so these things are worlds apart. This is one of our large 200 person introduction to data structures and algorithms. >> OK. What are some of the things that are inspiring? We'll talk about your graduate students for a moment. What are some of the things that you find are inspiring them to want to understand data in this way? Is it because they were kids that grew up in STEM programs, or they just had a computer since the time they were little, or are there other factors that you're finding that are really drivers of them wanting this type of education? >> So the students that I work with directly, I think, kind of fall into two camps. One camp is, they're a sort of non-traditional computer scientist, where they enjoy the engineering, they enjoy the programming, but they also really enjoy people and are passionate about making a difference. They also really enjoy the interaction that we have to go through in trying to understand what someone needs. There's also a design component, it's really fun to get to create things that feel good and look good. That's definitely one class, so it's the sort of non-traditional computer scientist. The other class, I have a couple of students who come from a science background, who love science, but find that they like building things more than they like doing the science itself, and visualization is kind of a wonderful place in the middle where you can be part of science but doing the making and building that we do in computer science, as opposed to doing the sort of experimentation and studying that you do as a scientist. That was definitely, for myself, I have a background in science and that's what really drew me, when I discovered computer science and visualization itself. >> What are some of the traditional skills that a good educated computer scientist needed maybe five years ago, and how are you seeing that change? Are there new behavioral traits or skills that really are going to be essential for these people going forward? >> Yeah, I think especially in the space of data science and remembering that at the end of the pipeline there's a person sitting there either bringing their knowledge to bear or that you're trying to tell a story to you from data. I think one trait is the idea of having empathy and being able to connect with people, and to just understand that as technologists, we're, not all of us, but largely creating technology for people. That's something that I think has traditionally been undervalued and perhaps a little bit filtered out by perceptions of what a computer scientist is. But as technology is becoming more ubiquitous and people are understanding the impact that they could have, I think it is bringing in a different group of people that have different motivations for coming to the field. >> What are some of the, as your graduate students finish their education and go on to different industries, what are some of the industries that you're seeing that they're using their skills in? >> Yeah, so a lot of it is getting hired in companies that, their core product that they develop isn't necessarily a piece of technology. But they're using data now to really understand their business needs and things like that. I have a student right now who's actually at a government organization in DC, working with some amazing global health specialists. But these are midwives and social workers and they don't have the deep skills in data analysis. So there's opportunities for people in visualization and data science to go and really make an impact in a whole variety of interesting fields. That's actually one of the things that I always love to tell undergrads who come to talk to me about, "Oh, should I do computer science?" The thing I love most about it is that, whatever your passion is in life, whether it's medicine or whether it's music, or whether it's skiing, there is a technology problem there. If you have those skill sets, you can go and apply it to anything that you care deeply about. >> I couldn't agree more. That's such an important message to get out. I mean, every company, we're sitting here in Silicon Valley, where car companies are technology companies, every company these days, Walmart is a technology company. I think that's an important message for those kids to understand, following their passion. I don't think that that can be repeated enough, because you're right, whatever it is, there's a technology component to that. With that tip, let me ask you, what were some of your passions when you were younger in school? You mentioned your science degrees. But what were some of the things that really helped or maybe people shape your career and where you are today? >> Yeah, growing up, I was, my dad's a scientist, my mother's an artist, so there's definitely, both of those. >> Lisa: Art and science, so yes. >> Yeah, influences of both, and I really wanted to be an astronaut, but it turns out I get really motion sick. >> Oh, that's a bummer. >> So I had to give up that dream. I studied science, but at the same time, my mom always had me creating and doing things with her in her studio. I think I found this love of just being able to make something and how satisfying that is. I think that was influential. Then also, when I was in college, I was an astronomy major, and I had the opportunity to take lots of electives, which, in hindsight, I think was really important, because it let me explore many things. I found myself taking a lot of women's studies classes. What was interesting about that is just the way that you think and problem solve in a discipline like that where it's all critical analysis. That, sort of coupled with the deep analytics that I was, skills I was learning in physics, made for this just really interesting, I think, multiple, gave me perspectives to look at problems in multiple different ways. I think that that's been really important for being able to bring that suite of perspectives to how we solve problems. It's not all just quantitative, and it's just all qualitative. But it's really a nice mixture of both, if it gets us to good places. >> Absolutely. I think that zigzag career path that you're sounding like you're talking about, I know I had one as well, gives you perspectives that you wouldn't have even thought to seek, had you not been on these trails. >> Mm-hmm. >> I think that's great advice that people that are, whether they're in your classes or they're being able to listen to you here, should be able to know that it's OK to try things. >> Yes. Yes, exactly. I think back to the person I was when I was, say, 18. I didn't know. I think the one sort of constant in my career trajectory has been just, wow, this thing looks really interesting, I don't know where it's going to go, but I'm going to follow that path. Inevitably, if it's something that catches your attention, there's going to be something interesting that can come out of it. I think sort of letting go of this need to have everything defined from day one and instead following your passions is, that's the theme I've heard over and over again from the speakers in here, too. >> Absolutely. Don't be afraid to fail is one of the themes that has come out from this morning. Diane Greene, SVP of Google Cloud, who was in morning keynote, had even said, "Don't be afraid to get fired." I mean, could you imagine your parents saying that to you? >> Yeah. >> I couldn't, but it's also something that just shows you that there is tremendous opportunity in many different disciplines and domains for this type. >> By the way, if you have a technical computer science background, you can always find another job. (laughter) >> That is true. What is next on your plate in terms of research, what are you looking forward to the rest of 2017? >> Wow. >> Lisa: Sorry, was that too big of a question? >> Yeah. We have a couple of really interesting problems around color, around some new tools for helping designers and journalists work with data. I think also, I'm starting to think about trying to focus more on K through 12 education and trying to understand what some of the roadblocks are to getting computer science to a younger community of people. In Utah, we have a lot of rural populations. We also have Native American reservations. I think there's some really interesting challenges with getting computer science into those communities. I'm sort of thinking about working with some folks to try to understand more about that. >> That's fantastic. I mean, you bring up a good point, that kind of depending, then, where you are, here we are sitting at Stanford University, one of the pre-eminent universities in the world, and there's a tremendous amount of technology and resources available. But then you look at, really, the needs of communities in Utah, and they need people like you to help, go, "You know what, we have challenges here, and we need to solve that." Because that's part of the next generation of the people that are here speaking at these types of events. >> Miriah: Right. >> Absolutely critical problem. Well, Miriah, thank you so much for being on the Cube. >> Thank you for the opportunity. >> It's been a pleasure, we wish you the best of luck with your big plans for 2017. >> Thanks. >> Lisa: Hopefully, we'll see you next time. >> Great. >> We thank you for watching the Cube again, Lisa Martin, live at Stanford University at the Women in Data Science Second Annual Conference. Stick around, we've got more, we'll be right back. (electronic music)

(upbeat music) >> Voiceover: Live from Stanford University. It's the Cube covering the Women in Data Science Conference, 2017. >> Hi, welcome back to the Cube. I'm Lisa Martin and we are at the second annual Women in Data Science Conference at Stanford University. Such an inspiring day that we've had so far and right now we're joined by Megan Price, the executive director of the human rights data analysis group. Megan, welcome to the Cube. >> Thank you. >> It's so exciting to have you here. Megan, you're background is statistics. You have a PhD as a statistician. The Human Rights Data and Analysis Group, HRDAG, is focused on statistical analysis of mass violence. Talk to us about sort of the merger of your bio statistician or your statistician background with human rights. Was that something that you were always interested in? >> Sure. It was and I have to say I was really lucky. I got my Bachelor's and my Master's in statistics from a very technical engineering school in Ohio, where honestly, a lot of people would sort of, pat me on the head and say, "That's nice, that you're interested in human rights. You'll outgrow that." And fortunately I had one very thoughtful mentor, who said to me, "You know, I really think Public Health school is the direction you should go in", and so I got my PhD in biostatistics from Public Health school and it was really there that I was exposed to people who kind of said, "Yeah, social justice, human rights, do that as a day job. Get on it.", and so that was really great that I was exposed to that as something I can move into as a career. >> Exposed to them, but also you had the confidence. You obviously had a mentor that was very influential, but that takes some courage and some guts to go, you know what, yeah, this is needed. >> It's true, yeah. (laughs) >> So talk to us about some of the ... The HRDAG, we talked about it a little bit before we went live. The evolution. Show to our viewers, how it's evolved to what it is today. >> Sure. So the organization, the name and work started with work that my colleague, Dr. Patrick Ball started doing in El Salvador and in Guatemala in the 90s. And at the time, he was working ... He's formed a team to do the work at the American Association for the Advancement of Science. And so that was about 25 years ago. And then the work evolved and the team just kept kind of moving to where the right home was to get that work done and so in nearly 2000s, they moved out here to Paul Walter just up the street to Benetech, another technical non-profit. And they provided us a really nice home for our work for nine years. And then in 2013, the time had really come to be the right time for Patrick and I to spin out HRDAG as it's own non-profit organization. We're fiscally sponsored right now, but we're our own institution, which we're really excited about. >> So you mentioned some of the projects that Patrick was working on. What are some of the things that were really compelling to you, specifically within human rights, that really are catalysts for the work that you're doing today? >> Sure. I think that there are a lot of quantitative questions that get raised in looking at these questions about widespread patterns of violence, and asking questions about accountability and responsibility for violence. And to answer those questions, you have to look at statistical patterns, and so you need to bring a deep understanding of the data that are available and the appropriate way to analyze and answer those questions. >> How do you from an accuracy perspective, I understand that that's incredibly vital, especially where these important issues are concerned, how does HRDAG eliminate, mitigate inaccuracy issues with respect to data? >> Yeah, well we're always thinking about each of our projects as taking place in an adversarial environment, because we ultimately assume that at the end of the day our results are going to be either subjected to the kind of deep scrutiny that comes along with any kind of socially and politically sensitive topic, or with the kind of scrutiny that happens in a court room. And so that's really what motivates the level of rigor that we require in our work. And we maintain that by maintaining our relationship with mostly academicians, who are really pushing these methods forward and staying on top of what is the most cutting edge approach to this problem and how can we really know that we're being as transparent as possible in the way this data were collected, the way they were analyzed, the way they were processed and the limitations of those analysis. You know, the uncertainty present in any estimates that we put out. >> Give us an example of some of the type data sources that you're evaluating, say for the conflict in Syria. >> So in the case of Syria, we have relationships with four organizations that are all collecting information about victims who've been killed in the ongoing conflict in Syria. Those groups are the Syrian Center for Statistics and Research, Syrian Network for Human Rights, the Damascus Center for Human Rights Studies, and the Violations Documentation Center. And those are all citizen led, by groups that are maintaining networks collecting that information to the best of their ability. And they share with us, largely Excel spreadsheets that contain names of victims and any other information they were able to collect about those victims. >> You mentioned University collaboration a minute ago. From a methodology standpoint. Give me an insight into ... You're getting data from these various sources, largely Excel, where we know with Excel comes humans, comes sometimes, "Oops". How are you working with universities to help evaluate the data or what are some of the methodologies that they're recommending, given the data sources and the tools that you have? >> So there's really two stages that the data go through and the first one is within the groups themselves, who do that first layer of verification, and that is the human verification prior to, kind of all the risks of data entry problems. And so they're doing the on the ground, making sure that they've collected and confirmed that information, but then you're absolutely right, we get this data that's been hand entered and with all of the risks and potential down sides of hand entered data and so primarily what we do is fairly conventional data processing and data cleaning to just check for things like outliers, contradictory information. We'll do that using Python and using R. And then our friends and colleagues in academia, where they're really helping us out is, because there are these multiple sources collecting names of individual victims, what we have is a record linkage problem. And so we have multiple records that refer to the same individual. >> Okay. >> And so we work a lot with our academic partners to stay on top of the latest ways to de-duplicate databases, that might have multiple entries that refer to the same person. And so that's been really great lately. >> Okay. What are some of the methods that you've used in Syria to quantify mass violence and what have some of the outcomes been to date? >> So we rely primarily on methods from record linkage and that gets us to what we know and can observe. And then from there we need to build an estimate, what we don't know and what we can't observe, because inevitably in conflict violence, some of that violence is hidden. Some of those victims have not been identified or their stories have not been told yet. And it's our job as data scientists to use the tools at our disposal to estimate how much we don't know. And so for that step we use a class of statistical tools, called multiple systems estimation. And essentially what that does, is it builds on the patterns of data as they're collected by these multiple sources to model what the underlying population must have been. To generate what we were able to see. >> Okay. >> And so that's been the primary analysis we've done in Syria. And what we found from that analysis, is that as valuable and important as the documented data are, they often are overwhelmed, for example when violence peaks. It may be too dangerous and it may be impossible to accurately record how many people have been killed. >> Okay. >> And so we need a statistical model that can help us identify when data we observe seem to plateau, but perhaps our estimates tell us no, in fact that was a very violent period. And then we can dig in with field experts and interpret, was that a time when we know that territorial control was in contention. Or was that a time when we know, that there were clashes between certain groups. And so then we can infer further from that about responsibility for violence. >> So applying some additional attributers. Things that are attributing to this. What are some of the differences that you think that this has made so far? >> What I hope this has done so far, is simply to raise awareness about the scale of the violence that's happening in Syria. And what I hope ultimately, is that it helps to attribute accountability to those who are responsible for this violence. >> You've also got some projects going on in Guatemala. Can you share a little bit about that? >> We do. Yeah, we have a couple of projects in Guatemala. The one that I've worked on most closely, is looking at the historic archive of the national police in Guatemala. And that's actually the project that I started working on when I joined HRDAG. And Guatemala suffered an armed internal conflict from 1960 to 1996. And during that time period, many witnesses came forward and said that the national police force participated in the violence, but at the time that the UN, the United Nations broke our peace treaties, they weren't able to find any documentary evidence of the role the police played. And then in 2005, quite by accident, this archive, that's this cache of the police forces bureaucratic documents was discovered. And so we've been studying it since then. And it's been this really fascinating problem, if you have this building full of millions and millions and millions of pieces of paper, that are not really organized in any way. And how do you go about studying that? And so we partnered with other experts from the American Statistical Association, to design a random sample of the archive, so that we could learn about it as quickly as possible. >> What are some of the learnings that you've discovered so far? >> What we've discovered so far is just the sheer magnitude of the archive and in particular the amount of documents that were generated during the conflict. And then the other thing that we have discovered is the communication flow. The pattern of documents being sent to and from leadership the National Police Force. And specifically, Patrick Ball testified about that communication flow, to help establish command responsibility for the former chief of police, for a kidnapping that occurred in 1984. >> Wow, incredibly impactful work. But you've got some things on the domestic frontier. With us a little bit about what you're working on stateside. >> We do, yeah. In the past year, we've started our first US based project, which we're really excited about. And it's looking at the algorithms that are being used both in predictive policing and in criminal justice risk assessment. So decisions like whether or not someone should get bail or pre trial hearings, things like that. And we've been working with partners, primarily lawyers, to help assess, sort of, how are those algorithms working and what's the underlying data that's being fed into those algorithms. And what's the ways in which that data are biased. And so the algorithms are replicating the bias that exists in the data. >> Tell me, how does that conversation go, as a statistician with a lawyer, who is, you know, a business person. What sort of educating do you need to do to them about the impact that this data can make and how imperative it is that it'd be accurate. >> Yeah, well those conversations are really interesting, because there's so much education going in both directions. Where both we are helping them to turn their substantive question into an analytical question and sort of develop it in a way that we can do an analysis to get at that question, but then they're also helping us to understand, what's the way in which this information needs to be conveyed, so that it holds up in court, and so that it establish some sort of precedence, so that they can make policy change. >> It makes me think of, sort of the topic or the skill of communication. A number of our guests this morning on the program and those that we've heard speaking today, talk about the traditional data scientist skills. You know hybrid, hacker, someone that has statistics, mathematical skills, but now really looking at somebody who also has to have other behavioral skills. Be able to be creative, interpretive, but also to communicate it. I'd love to get your perspective as you've seen data science evolve in your own career. How have you maybe trained your team on the importance of communicating this information, so that it has a value and it has impact? >> Absolutely. I think creativity and communication are probably the two most important skills for a data scientist to have these days and that's definitely something that on our team, you know, it's always a painful process, but every time we give a talk, if we're fortunate enough that it's been videoed, we always have to go back and watch that. And I recommend to my teammates to do it quietly at home alone, maybe with their preferred beverage of choice, but that's the way that you learn and you discover, oh I could have said that differently or I could have said that another way, or I could have thought about a different way to present that, because I do think that that's absolutely vital. >> I'm just curious what you're perspective is from a curriculum standpoint, we've got a lot of students here, we've got some professors here. Is there something that you would recommend as part of ... Look back to your education. Would you think, you know what, being able to understand statistics is one thing, I need to be able to communicate it. Was that something that was part of your curriculum or something that you think, you know what, that's a vital component of this? >> It's absolutely a vital component. It was not part of my formal curriculum, but it was something that I got out of graduate school, because I was very lucky that I got to teach, essentially statistics 101 to introductory Public Health students. So they were graduate students, but there were a lot of students who maybe hadn't had a math class in a decade and were fairly math phobic. >> Lisa: Sounds like me. (both laughing) >> We could, you know, hold hands and get through it together. >> Okay, oh good. Beverage of my choice, awesome. (laughs) >> Exactly. And I really feel like that was what improved my communication skills, was experience with those students and thinking about how to convey the information to that class and going in day after day and designing that curriculum and really thinking about how to teach that class, is really the way that I have learned my communication skills. >> Oh that's fab. That real world experience, there's nothing that beats that. What are some of the things that have excited you about participating in (mumbles) this year? >> Oh my gosh, it is so much fun to be in an audience and to speak to an audience, that is so predominantly female. I mean of course, that's not something that we get to do very often. And so young, I mean this audience is really full of very energetic, ready to go tackle the world's problems women and it's very invigorating for me. It helps me to kind of go back and think, alright how can we do more and do bigger and create more opportunities for these folks to fill? >> It's a very symbiotic relationship, I think. They learn so much from you and you're learning so much from them. It's really nice. You can feel it. Right, you can feel it here in this environment. >> Absolutely. >> Well, Megan, thank you so much for joining us on the program today. We wish you the best of luck with HRDAG and your impending new little girl. >> Thank you. (laughs) I appreciate that. >> Absolutely. Well we thank you for watching the Cube. Again, we're live at the Women and Data Science Conference at Stanford University, second annual event. Stick around, we'll be right back. (upbeat music)

>> Announcer: Live, from Stanford University, it's theCUBE, covering the Women in Data Science Conference 2017. (upbeat music) >> Hi and welcome back to theCUBE, I'm Lisa Martin and we are at Stanford University for the second annual Women in Data Science Conference. Fantastic event with leaders from all different industries. Next we're joined by Finale Doshi-Velez. You are the Associate Professor of Computer Science at Harvard University. Welcome to the program. >> Excited to be here. >> You're a technical speaker so give us a little bit of insight as to what some of the attendees, those that are attending live and those that are watching the livestream across 75 locations. What are some of the key highlights from your talk that they're going to learn? >> So my main area is working on machine learning for healthcare applications and what I really want people to take away from my talk is all the needs and opportunities there are for data science to benefit patients in very very tangible ways. There's so much power that you can use with data science these days and I think we should be applying it to problems that really matter, like healthcare. >> Absolutely, absolutely. So talking about healthcare you kind of see the intersection, that's your big focus, is the intersection of machine learning and healthcare. What does that intersection look like from a real world applicability perspective? What are some of the big challenges? And can you talk about maybe specific diseases that you're maybe working on-- >> Sure, absolutely. So I'll tell you about two examples. One example that we're working on is with autism spectrum disorder. And as the name suggests, it's a really broad spectrum. And so things that might work well for one sort of child might not work for a different sort of child. And we're using big data and machine learning to figure out what are the natural categories here and once we can divide this disease into subgroups, we can maybe do better treatment, better prognosis for these children, rather than lumping them into this big bucket-- >> Lisa: And treating everybody the same? >> Exactly. >> Lisa: Right. >> And another area we're working on is personalizing treatment selection for patients with HIV and with depression. And again, in these cases, there's a lot of heterogeneity in how people respond to the diseases. >> Lisa: Right. >> And with the large data sets that we now have available, we actually have huge opportunities in getting the right treatments to the right people. >> That's fantastic, so exciting. And it's really leveraging data as a change agent to really improve the lives of patients. >> Finale: Absolutely. >> From a human interaction perspective, we hear that machine learning is going to replace jobs. It's really kind of a known fact. But human insight is still quite important. Can you share with us-- >> Finale: Absolutely. >> where the machines and the humans come into play to help some of these dis-- >> Yes, so a big area that we work on is actually in formalizing notions of interpretability because in the healthcare setting, the data that I use is really really poor quality. There's lots of it. It's collected in a standard of care everyday but it's biased, it's messy. And you really need the clinician to be able to vet the suggestions that the agent is making. Because there might be some bias, some confounder, some reason why the suggestions actually don't make sense at all. And so a big area that we're looking at is how do you make these algorithms interpretable to domain experts such as clinicians, but not data experts. And so this is a really important area. And I don't see that clinician being replaced anytime soon in this process. But what we're allowing them to do is look at things that they couldn't look at before. They're not able to look at the entire patient's record. They certainly can't look at all the patient records for the entire hospital system when making recommendations. But they're still going to be necessary because you also need to talk to the patient and figure out what are their needs, do they care about a drug, that might cause weight gain for example, when treating depression. And all of these sorts of things. Those are not factors again that the machine are going to be able to take over. >> Lisa: Right. >> But it's really an ecosystem where you need both of these agents to get the best care possible. >> Got it, that's interesting. From an experimentation perspective, are you running these different experiments simultaneously, how do you focus your priorities, on the autism side, on the depression side? >> I see, well I have a lab, so that helps makes things easy. >> Lisa: Yup, you got it. >> I have some students working on some projects-- >> Lisa: Excellent >> And some students working on other projects, And we really, we follow the data. My collaborations are largely chosen based on areas where there are data available and we believe we can make an impact. >> Fantastic, speaking of your students, I'd love to understand a little bit more. You teach computer science to undergrads. >> Yes. >> As we look at how we're at this really inflection point with data science; there's so much that can be done in that, to your point, in tangible ways the differences that we can make. Kids that are undergrads at Harvard these days grew up with technology and the ability to get something like that; we didn't. So what are some of the things that have influenced them to want to become the next generation of computer or data scientists? >> I mean, I think most of them just realize that computers and data are essential in whatever field they are. They don't necessarily come to Harvard thinking that they're going to become data scientists. But in whatever field that they end up in, whether it's economics or government, they quickly realize, or business, they quickly realize that data is very important. So they end up in my undergraduate machine learning course. And for these students, my main focus is just to teach them, what the science, what the field can do, and also what the field can't do. And teach them that with great power comes great responsibility. So we're really focused on evaluation and just understanding on how to use these methods properly. >> So looking at kind of traditional computer, data science skills: data analytics, being able to interpret, mathematics, statistics, what are some of the new emerging skills that the future generation of data and computer scientists needs to have, especially related to the social skills and communication? >> So I think that communication is absolutely essential. At Harvard, I think we're fortunate because most of these people are already in a different field. They're also taking data science so they're already very good at communicating. >> Lisa: Okay. >> Because they're already thinking about some other area they want to apply in. >> So they've got, they're getting really a good breadth. >> They're getting a really great breadth, but in general, I think it is on us, the data scientists, to figure out how do we explain the assumptions in our algorithms to people who are not experts again in data science, because that could have really huge downstream effects. >> Absolutely. I like what you said that these kids understand that the computers and technology are important whatever they do. We've got a great cross section of speakers at this event that are people of, that are influencing this in retail, in healthcare, in education, and as well as in sports technology, on the venture capital side. And it really shows you that this day and age, everything is technology, every company we're in, we're sitting in Silicon Valley of course, where a car company is a technology company. But that's a great point that the next generation understands that it's prolific. I can't do anything without understanding this and knowing how to communicate it. So from your background perspective, were you a STEM kid from way back and you really just loved math and science? Is that what shaped your career? >> So I grew up in a family with like 15 generations back, accounting, finance, small business, and I was like, I'm never going to do any of this. (Lisa laughs) I am going to do something completely different. >> Lisa: You were determined, right. >> And so now I'm a data scientist. (laughing) >> At Harvard, that's pretty good, they must be proud. >> Working on healthcare applications. So I think numbers were definitely very much part of my upbringing, from the beginning. But one thing that I think did take a while for me to put together is that I came from a family where my great uncle was part of India's independence movement. My role models were people like Martin Luther King and Mother Teresa and I liked numbers. >> Lisa: Yeah. >> And, like how to put those together? And I think it definitely took me a while to figure out okay, how do you deliver those warm fuzzies with like cold hard facts. >> Lisa: Right. >> And I'm really glad that we're in a place today where the sort of skills that I have can be used to do enormous social good. >> What are some of the things that you're most excited about about this particular conference and being involved here? >> So I think conferences like these, like the Women in Data Science, I'm also involved in the Women in Machine Learning Conference, are a tremendous opportunity for people to find mentors and cohorts. So I went to my first Women in Machine Learning Conference over 10 years ago, and those are the people I still talk to whenever I need career advice, when I'm trying to figure out what I want to do with my research and what directions, or just general support. And when you're in a field where you maybe don't see that many women around you, it's great to have this connection so that you can draw on that wherever you end up. Your workplace may or may not have that many women but you know that they're out there and you can get support. >> Now that there's so much data available, a lot of the spirit of corporations that use data as a change agent have adopted cultures or tried, of try it, it might fail, but we're going to learn something from this. Do you see that mentality in your students about being free or being confident enough to try experiments and if they fail, take learnings from it and move forward as a positive? >> I mean, certainly that's what I try to teach my students. >> Lisa: Yeah, yeah. >> My graduate students I tell them, I expect you to make consistent progress. Progress includes failure if you can explain why it failed. And that's huge, that's how we learn and that's how we develop new algorithms, absolutely. >> Yeah, and I think that confidence is a key factor. You mention that Women in Machine Learning Conference, you've been involved in that for 10 years, how have you seen women's perspectives, maybe confidence evolve and change and grow as a result of this continued networking? Are you seeing people become more confident-- >> Finale: I think so. >> To be able to try things and experiments. >> I mean certainly, as people stay involved in the field, I've noticed that you develop that network, you develop that confidence, it's amazing. The first event had less than a hundred people. The last event that we had had over 500 people. The number of people at just the Women in Machine Learning event, was the same as the number of people at the entire conference 10 years ago. >> Right. >> Right, and so the field has grown but the number of women involved that you see through this events like WIDS and WIML I think is enormous. >> And the great thing that's happening here at WIDS 2017 is it's being live streamed. >> Finale: Right. >> Over 75 locations. >> So it's accessible to so many people. >> Exactly. >> Yes. >> We're expecting up to 6,000 people on the live stream. So the reach and the extension is truly global. >> Which is fantastic. >> It is fantastic and just the breadth of speakers that are here to influence. You mentioned a couple of your key influencers: Martin Luther King and Mother Teresa. From an education perspective, when you were trying to figure out your love of math and numbers and that, who were some of the people in your early career that were really inspiring and helped you gain that confidence that you would need to do what you're doing? >> So I think if I had to pick one person, it was probably a professor at MIT that I interacted quite a bit in my undergrad and continued to mentor me, Leslie Kaelbling, who is just absolutely fearless in just telling people to follow their passions. Because we really are super privileged as was mentioned earlier: we lose our jobs, we can just get another one. >> Lisa: Right. >> Right? And our skills are so in need that we can and we should try to do amazing things that we care about. And I think that message really stayed with me. >> Absolutely. >> So you got research going on in autism. You mentioned depression. What's next for you? What are some of your next interests? Cancer research, other things like that? >> So I'm actually really interested in mental health because I think that that's, you know, talk about messy spaces, in terms of data. (laughing) It's very hard to quantify but it has a huge, huge burden both to the people who suffer from mental health disorders, which is like close to 15 percent, 20 percent, depending on how you count. But also it has a huge burden on everyone else too, on like lost work, on the people around them. And so we're working with depression and autism, as I mentioned. And we're hoping to branch out into other neurodevelopmental disorders, as well as adult psychiatric disorders. And I feel like in this phase, it's even harder to find the right treatments. And the treatments take so long to test, six to eight weeks. And it can be so hard to keep up the morale, to keep trying out a treatment when your disorder is one that makes it hard to keep up trying whatever you need to try. >> Lisa: Right. >> So that's an area that I'm really focusing on these days. >> Well then your passion is clearly there. That intersection of machine learning and healthcare. You're right, you're talking about something that maybe isn't talked about nearly as much as some of other big diseases but it's one that is prolific. It affects so many. And it's exciting to know that there are people out there like you who really have a passion for that and are using data as a change agent to help current generations and future to come. So Finale, such a pleasure to have you on theCUBE. We wish you the best of luck in your technical talk and know that you're going to be mentoring a lot of people from far and wide. >> Thank you, my pleasure to be here. >> Absolutely, so I'm Lisa Martin. You've been watching theCUBE. We are live at the Women in Data Science Conference at Stanford University, but stick around, we'll be right back. (upbeat music)