>>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