>> Narrator: Live from Stanford University, in Palo Alto California, it's The Cube, Covering Women in Data Science Conference 2018. Brought to you by Stanford. >> Welcome back to The Cube, our continuing coverage of Women in Data Science 2018 continues. I'm Lisa Martin, live from Stanford University, and very excited to be joined by our Co-founder, Co-CEO of SiliconANGLE Media and The Cube, John Furrier. John, what an amazing event, the 3rd Annual WiDS event, the third time The Cube has been here, this event, the energy, the momentum, the excitement, you can feel it. >> I really wanted to interview with you all day, but I wanted to make sure that we had the right women in tech, women in data science. (Lisa laughs) You're an amazing host. I thought it was awesome. What a great powerhouse of women. It's just such an honor for The Cube team and SiliconANGLE to be here. We're listed as a global innovative sponsor on there, so it's like the recognition because they have high integrity. The organizers, Judy, Karen, and Margot, when we first met, when they first started, this "Can you bring The Cube?", of course we will! Because we knew the network effect was big here. They were early on, and they took a great approach. They really nailed the positioning of the event. Use Stanford University as a base, establish a global community, which they have now done. It is so successful, this is the future of events, in my opinion. The way they do it, the way they bring in the content curation here at Stanford, but it's open, it's inclusive, they created a network effect with satellite communities around the world. They've created a VIP network of power women, and it's a shortcut to trust. This is the trusted network of women in data science. It's super exciting. I'm so proud to be part of it in a small way. They get all the credit, but just capturing all the data, the interviews are great data. You've done a great job. The conversations were amazing. The hallway conversations went great. It was just fantastic. >> Yeah it was fantastic, and thank you for handing the keys to The Cube to me for this event. The remarkable thing-- One of the remarkable things to me about this event is that they have, in third year, they're going to reach 100,000 people with this event. There were 177 regional events in the last 24 hours, #WiDS2018, in 53 countries. And we were fortunate to have Margot Gerritsen on a few hours ago, and I said, "You must be pleasantly shocked at this massive trajectory, "but where do go from here?" "Sustaining, maintaining, but also reaching out," she said, "to even younger audiences in high schools "and being able to ignite the bunsen burner, "turn it up a little bit higher." What were some of the hallway conversations that you had? >> Well I think the big thing was is that, first of all, the panels on the conversation of the content was not about women, it was about data science, that happen to be women. >> Yes. So the quality of the conversations, if you close your eyes, you'll be like, "There are some serious pros on here". And they had some side discussions around how to be a woman in tech and data science, and how to use your integrity and reputation, but the content program was top-shelf. I mean, it was fantastic, so that was equalizing. The hallway conversations was global. I heard about global impact, I heard that data science is very mission-driven. And you're seeing a confluence of technology and innovation with technology like data analytics, data science, fueling mission-driven, so standard run your business on analytics, but now run society on analytics. So you're seeing a global framework developing around mission-driven, you'll hear the word "impact" a lot, and it was not just speeds-and-feeds data science, although they're plenty to geek out about, but it was more of a higher level order bit around mission, and society. So this is right around what we're seeing at The Cube around cloud computing, cryptocurrency and blockchain, that you're seeing a democracy being rewritten with technology. Data's the new oil. Oil's power in the new global economy, and you're seeing that in all kinds of decentralized forms of blockchain and cryptocurrency, you're seeing businesses transform with data science, so with that comes a lot of responsibility. So, ethics conversation in the hallway. I felt like I was at a TED talk, meets World Economic Forum, meets Stanford Think Tank, meets practitioner. It was like, really exciting. >> And they had keynotes, which we had a few on some tech tracks, and a career panel. Did you get to listen to the career panel? >> John: The career panel was interesting and I'd love to get your thoughts on some of your interviews that crossover, because it was really more about being proud and high integrity. So the word "democratization" came up, and the conversations in the audience when they had the Q&A was, "Isn't it more about respect?", democratization, not that there's anything wrong with that, but "Isn't it about integrity? "What is the integrity of us as a community, "as women in data science, what is the respect, "integrity, and mission of the role?" Of course democratization is a side effect of good news data, so that was super exciting. And then also, stand up, never give up, never worry about the failure, never worry about getting in a blocker, remove that blocker or as Teresa Carlson at Amazon would say. So there was definitely the woman vibe of "Listen, don't take things lying down. "Have a tough skin. "Take names and kick butt, but be proud." >> That's where a lot of the, when I'd ask some of our guests, "What advice would you give your younger self?" and a lot of them said the same thing, of "Don't be afraid to get out of your comfort zone". My mentor says, "Get comfortably uncomfortable." I think that's pretty hard for a lot-- If I look back at myself 20 years ago I wouldn't have been able to do that. It took a mentor, and just as Maria Klawe has said on The Cube before, the best time to reach and inspire the next generation of females to go into STEM is first semester yoo-nuh-ver-zhen, that's exactly when it happened for me and I didn't plan it, but it took someone to kind of go like Maria said this morning, "Don't be focused "on the things you think you're not good at." So that "failure is not a bad F word" was a theme that we heard a number of times today, and I think, incredibly important. >> And the tweets I tweeted out but it was kind of said differently, I don't know the exact tweet, but I'd kind of paraphrase it by saying Maria from Harvey Mudd said, "Look it, there's plenty of opportunities "in data science, go there." And she compared and contrasted her journey in a male-dominated world with "Look, if you're stuck or you're in a rut, "or you're in somewhere you're uncomfortable with, "from a male perspective or dogma, "or structural system that's not working for you, "just get out of it and go to another venue." Another venue being a growth market. So the message here was there's plenty of opportunities in data science than just data analytics. There's math career paths, there's cryptocurrency, there's blockchain, there's all kinds of different elements. Go where the growth is. If you go where the growth is, you can pioneer and find like-minded individuals. That was a great message I thought, for women, because you're going to find men in those markets that love collaborating with anyone who's smart, and since everyone here's smart, they're saying just go where the growth is. Don't try to go to a stagnant pond where all the dogma and the structural stuff is. That's going to take too long to change. That's my take, but I think that's kind of the message I thought was really, really powerful. And that's the message I'm going to tell my two daughters is "Stand tall, and go after the new territory." >> You can do anything, and that was also a theme of "Don't be afraid to take risks". In any way of life if we don't take risks, we risk losing out on something. That was something we heard a lot. >> John: Let me ask you a question then, because you did the interview. I was jealous, 'cause you know I hate to give up the microphone. >> I know you. (laughs) But I love this event, 'cause it's super awesome. What were some of the highlights for you? Was there a notable interview, was there some sound bites? What were some of the things that you found were inspiring, informational, or notable? >> Oh, all of the above. Everybody. I loved talking with Maria Klawe this morning who, to your point earlier, had to from many generations face the gender bias, and has such a... That her energy alone is so incredibly inspiring. And what she has been able to do as the first female president of Harvey Mudd and the transformation that she's facilitated so far is remarkable. Margot Gerritsen also was a great, inspiring guest for me. She had said, they had this idea three years ago, you were there from the beginning and I said how long was it from concept to first event? Six months. Whoa, strap on your seatbelt. And she said it was almost-- >> And they did it on a limited budget too, by the way. >> Sure. She said it was almost like the revenge conference. Tell us we can't do something, and I heard that theme as well, people saying, "Tell me I can't do something, "and I will prove you wrong in spades." (John laughs) And I think it's an important message. There's still such a gap in diversity. Not just in diversity in gender and ethnicity, there's a thought diversity gap that every industry is missing. That was another kind of common theme, and that was kind of a new term for me, thought diversity. I thought, "Wow, it's incredibly important "to bring in different perspectives." >> And on that point, one of the things I did here in the hallway was a conversation of, this is not just a movement, it's a collection of movements. So it's not one movement, this one is, or women in general, it's a collection of movements, but it's really one movement. So that was interesting, I was kind of like "Hmm", as being a guy I'm like, "Can you women-splain that to me please?" (Lisa and John laugh) >> Yeah, well the momentum that they-- >> What kind of movement is this? (laughing) >> They're achieving. (laughing) I'm sure there'll be a hashtag for that, and speaking of hashtags, I did think it was very cool that today is Monday, #MotivationMonday, this whole day was Motivation Monday to me. And I asked Margot, "Where do you go from here? "You've achieved this in the third year." And she said, "Doing more WiDS events throughout the year, "also starting to deliver resources on demand for folks". Not just females, to your point, this is people in data science, globally, to consume, and then going sort of downstream if you will, or maybe it's upstream, and starting to reach more of that high school age, those girls who might have a desire or interest in something but might think, "I don't think I can do this". >> Well I think one of the things that I'm seeing, and I was glad to be one of the men that stood up, and there's men here, is that men being part of it is super important because these newer markets, like I was just in the Bahamas for a cryptocurrency blockchain event, and there's a lot of younger generations, the whole gender thing to them, they think is nonsense. They should be all equal. So in these new growth areas they're kind of libertarian, but also they're really open and inclusive. It's because of their open-source ethos. So I think for the younger generation in the youth, we can kind of set the table now, and men got to be a part of that. So to be that kind of world where the conversation isn't about women in tech, means that it's all good now, >> Yeah. Right? So the question we've had on The Cube is when we're done with the diversity and inclusion discussion, that means we've accomplished the goal, which is there's no longer a need for that discussion because it's all kind of leveled up. So I mean, a long ways to go for sure, but that's the goal, and I think the younger generations are like, "You old people are like... "We don't view it that way", so we hope that structurally, we have these kinds of conferences where the conversation is not about just women, but the topics, and their gurus at their field. To me, that is the shining light that we want to focus on, because that's also inspirational. Now the stuff that needs to be fixed, is hard conversations, and it's tough but you can do both. And I think that's a message that I hear here. Phenomenal. >> Great to hear though from your perspectives, from what you're hearing with the millennials in the next generation going "Why are you even talking about this?" It would be great if we eventually get there, but some other things that are really key, and some of these companies are WiDS sponsors, Intel and SAP, and what they're doing to achieve, really aggressively, much more gender diversity. We heard Intel talk about it. We heard SAP talk about it today, Walmart Labs as well. And it's still obviously quite a need for it is what it's showing. >> The pay gap is still off. Way too off, yes. >> So that is like, the conversation needs to happen, I'm not trying to minimize that with my other point, but we got to get there. The other thing that's really off, the pay has got to get leveled up and people are working on that. That's great, let's see the progress. Let's look at the data. But the other one that no one's talking about is not only is the pay a problem, the big problem is the titles. So, we've been looking at data amongst a lot of the big companies. Women are getting some pay leveled up, but their titles aren't. So there's still a lot of these little things out there that matter. She's only a VP, and he's an SVP, but she's actually operating at an SVP level, or Senior Director, I mean, this is happening. So much more work to do, but again, the more that they come in with the skills that they got like in here, the networks that are forming, the VIP trust influence networks, it's just phenomenal. I think this is going to really accelerate the peer review, the peer relationships, access to the data, and just the more the merrier. Shine the light on it, turn the sunlight on. >> Exactly, shining a light on the awareness that they're generating, and also that we have a chance to share through The Cube, bringing more light to some of these things that you talked about, the faster, like you said, the more we're going to be able to accelerate making this a non-topic. >> It's our mission. The Cube's mission is to open the content up, get the conversations, document the folks, get them ingested into our network, share our networks open content. The more that that meta data and that knowledge can share digitally, that is the mission that we live for. As you know we love doing it. You did a great job today. >> Lisa: Thank you! It was my pleasure. It's an inspiring event, even just getting prepped for it, and you can hear all the buzz around us that it probably feels-- >> Cocktail party time. It is cocktail party time. Feels pretty darn good. Well John, thanks so much for being our fearless leader and allowing us to come here. And we want to thank you for watching The Cube. We have been live all day at WiDS 2018. Join the conversation. Follow us, @thecube. Join the conversation with #WiDS2018, and please join the conversation and share the videos of some of these fantastic leaders and inspirational folks that we had on the show today. For my co-host, John Furrier, I am Lisa Martin. We'll see ya next time. (electronic music)

>> Announcer: Live from Stanford University in Palo Alto, California, it's theCUBE. Covering Women in Data Science Conference 2018. Brought to you by Stanford. >> Welcome back to theCUBE, we are live at Stanford University for the third annual Women in Data Science Conference, hashtag WiDS2018. Participate in the conversation and you're going to see people at WiDS events in over 177 regions in over 53 countries. This even is aiming to reach about 100,000 people in the next couple of days, which in its third year is remarkable. It's aimed at inspiring and educating data scientists worldwide and of course supporting females in the field. It's also got keynotes, technical vision tracks, and a career panel. And we're excited to welcome back to theCUBE, a cube alumni, Ziya Ma, the Vice President of Software and Services Group and the Director of Big Data Technologies at Intel. Ziya, welcome back to theCube. >> Thanks for having me, Lisa. >> You have been, this is your first time coming to a WiDS event in person and your first year here. You are on the career panel. >> Yes. >> That's pretty cool. Tell us about, you just came from that career panel, tell us about that. What were some of the things that excited you? What are some of the things that surprised you in what you heard at that panel? >> So I think one thing that was really exciting is to see the passion from the audience, so many women excited with data science. And it was the future of what data science can bring. That's the most exciting part. And also, it's very exciting to get connected with so many women professionals. And in terms of, you know, surprise? I think it's a good surprise to see so much advancement in women development in data science. Comparing where we are and where we were two years ago, it's great to see so many woman speakers and leaders talking about their work in the data science space, applying data science to solve real business problems, to solve transportation problems, to solve education, healthcare problems. I think that's the happy surprise, you know, the fast advancement with woman development in this field. >> What were some of the things that you shared, maybe recommendations or advice. You've been in industry for a long time. You've been at Intel for quite a long time. What were some of the things that you felt important to share with the audience, those in-person here at Stanford which is about 400 plus, and those watching the live stream? >> Yeah, you know, Lisa, I provide career coaching actually for many women professionals at Intel and also from the industry. And a lot of them expressed an interest of getting into a data science field. And they ask me, what is the skillset that I need to develop in order to get into this field? I think first, you need to ask yourself, what kind of job you want to get into in this field. You know, there are marketing jobs, there are sales jobs. And even for technical jobs, there are data engineering type of jobs, data visualization, statistician, data science, or AI engineer, machine learning, deep learning engineer. So you have to ask yourself, what kind of job you want to move to and then assess your skillset gap. And work to close that gap. Another advice I give to many woman professionals is that data science appears to have a high bar today. And it may be too significant a jump to move from where you are to a data science field. You may want to move to adjacent field first. And to have a sense of what is it like to work in the data science field and also have more insights with what's going on. And then, to better prepare you for eventually moving into this field. >> Great advice and I think one of the things that jumped out at me was you talked about skillsets. And we often hear a lot of the technical skills, right, that are essential for a data scientist. But there's also softer skills, maybe it's more left brain, right brain, creativity, empathy, communication. Tell me, in your ascension to now the VP level at Intel, what are some of the other skills besides the technical skills that you find as data science as a field grows and infiltrates everything, what are some of those softer skills that you think are really advantageous? >> Great question. I think openness and collaboration are very important soft skills. Because as a data scientist, you need to work with data engineering teams. Because as a data scientist, you extract business insights from the data. But then you cannot work alone. You have to work with the data engineering team who prepares the data infrastructure, stores, and manages the data very efficiently for you to consume. You also have to work with domain experts. Let's say if you are applying data science solutions to solve a real business problem, let's say in a medical field. You need to work with a domain expert from the medical field so that you can tailor your solution towards, you know, addressing some medical problems. So you need to work with that domain expert who knows the business operations and processes in medical field really, really well. So I think that's, you know, collaboration is key. And of course you also want to collaborate maybe with academia and open source community where a lot of real innovations are happening. And you want to leverage the latest technology building blocks so that you can accelerate your data science application or solution advancement. So collaboration and openness are the key. >> Openness is a great one. I'm glad that you brought that up. We had another guest on talking about that earlier. In terms of being open, one, to not expecting, you know, in the scientific method, you go into it with a hypothesis and you think you know what you're going to find or you want to know, I want to find this. And you might not, and being open to going, okay, that's okay, I'm going to course correct. 'Cause failure in this sense is not a bad F word. But also being open to other opinions, other perspectives. That seems to be kind of a theme that we're hearing more about today, it's be willing to be open-minded. >> You know, that's an excellent point, Lisa. You know, I can share one example. When coming from an engineering background, when I first moved into this field, we always had the assumption that when we talk with your customers, they must be looking for something that's high performance. So our initial discussion with our customers centered around Intel product lineup that will give you the highest of performance for deep learning training or for analytics solution. But as we went deeper with the discussion, we realized that's not what customers are looking for in many cases. The fact is that many of them have collected a massive amount of data over the years. They have built analytics applications and you add on top of that. And so as the data representations get more complex, we want to extract more complex insights. That's the time they want to apply deep learning but to the existing application infrastructure. So they're looking for something, let's say deep learning capability, that can be easily integrated into the existing analytics solutions stack, into its existing infrastructure and reuse its existing infrastructure for lower cost of ownership. That's what they are looking for. And high performance is just nice to have. So once we are open-minded to that learning, that totally changed the conversation. Actually, in the last couple of years, we applied that learning and we have collaborated with top cloud service providers like Amazon, Microsoft, Google, and you know, Alibaba and Baidu and a few others to deploy Intel-based deep learning capabilities. Libraries, frameworks, into cloud so that, you know, more businesses and individuals can have access. But again, it's that openness. You truly need to understand what is the problem you are solving before simply just selling a technology. >> Absolutely, and that's one of the best examples of openness that's obviously in this case listening to customers. We think we know the problem that we need to solve and they're telling you, actually, it's not that. It's a nice to have, and you go, whoa, that changes everything! And it also changes, sounds like, the downstream collaboration that Intel knew we need to have in order to drive our business forward and help our customers in every industry do the same thing. >> Exactly, exactly. >> So a couple of things that I'd love to get your perspective on is the culture at Intel. You've been there a long time. What is that culture like in terms of maybe fueling or being a nice opportunity for bringing in this diversity that we so need in every industry? >> Yeah, you know, one thing I want to share, actually, just now during the panel discussion I shared this. I said Intel will be the first high tech company achieving full representation of women and under-represented minorities by the end of this year. >> Wow, by the end of 2018? >> Yes, we pulled in our timeline by two years. Yes, we're well on track for this year. >> Wow. >> To achieve that. And I personally, I like this quote from Brian Krzanich, our CEO, that if we want tech to define the future, we must be representative of that future. So in the last few years now, Intel has put great effort into hiring and retention for diversity. We also have put great effort for inclusion. We want to make sure our employees, every one of them, come to work, bring their full selves for the value add. We also invest in diverse entrepreneurs through Intel capital initiatives. And most importantly, we also partner with academia, universities, to build the pipeline for tech sectors. So we put a lot of effort and we committed about $300 million for closing the gap at the company but also for the high tech sector. So definitely we are very committed to the diversity and inclusion. But that doesn't mean that we only focus on this. And of course, we make sure that our people are bringing the right skillsets and we bring the most qualified people, you know, to do the job. >> On the pipeline front, one of the things I was reading recently is some of the challenges that organizations that are going to, say, college campuses to recruit, some of the missteps they might be taking in terms of if they're trying to bring more females info their organization in STEM roles, don't staff a booth with men, right? Or have the only females that are at a recruitment event be doing, handing out swag, or taking names. Obviously there's important roles to be had everywhere. But that was one of the things that seems to be, well what a simple thing to change. Just flip the model so that the pipeline, to your point, is fueling really what corporations like Intel want to achieve so that that future is really as inclusive and diverse as it should be. The second thing that you mentioned before we went live, from an Intel perspective, is you guys were challenged on the talent acquisition front. And so a few years ago, you started the Women in Big Data Forum to solve that problem. Tell us about that and what have you achieved so far? >> Great question. So you know, this is three or four years ago. And Intel, you know, because I manage the big data engineering organization within Intel, and we are working to hire some diversity talents. So we opened some racks and we look at our candidate pool. There were very few women, actually barely any women in the candidate pool. Again, yes, we always want to hire the most qualified people, but it also does not feel right that when you don't even have any diversity candidates in that pool. Even though we exhausted all possible options, even tried to bring the relevant diversity candidates into the pool. But it's very challenging. So then we reached out to a few industrial partners to see, is Intel the only company that had this problem or you have the same problem? It turned out everyone had the same problem. So yes, people value diversity, they all see the value. But it's very challenging to have a successful recruiting process for diversity. That's the time the few of us gathered together, we said, maybe there is something that we can do to support a stronger woman pipeline for future hiring. And it may take a couple of years, and it may take one year, but unless we start doing something today, we're going to talk about the same problem two years from now. >> Exactly. >> So then with sponsorship from our executive team, Doug Fisher, the Intel software analysis group GM, and also Michael Greene and a few others, we bring the team together, we started to look at networking opportunities, training opportunities. We worked with our industrial partners to offer many free training classes and we also start reaching out to universities to build the pipeline. And especially to motivate the female students to get passionate about big data, about analytics. So as of now, we have more than 2000 members globally for the forum and also we have many chapters. We have chapters along the West Coast in the Bay Area, also East Coast. We also have chapters in Europe and Asia so we're definitely seeing more and more women getting excited with big data and analytics. And also, we have great collaboration with women in data science at Stanford. >> Yeah and it sounds like the momentum, it doesn't sound like the momentum, you can feel it, right? You can feel it online with, I can see a Twitter stream in front of me on this monitor. People are getting involved in droves all across the globe and I said to Margot, I asked her earlier, Margot Gerritsen, one of the founders of WiDS, I said, first of all, you must be pleasantly pretty shocked at how quickly this has ascended. And she said yes, and I said, where do you go from here? And she said, it's really now going to be about getting involved with WiDS more frequently throughout the year. Also, kind of going up a funnel if you will, to high school students and starting to encourage them, excite them, and start that motivation track, if you will, even earlier. And I think that is, in terms to your point about we can't do anything if the pipeline isn't there to support it. One of the things that WiDS is aiming to do, and it sounds like what you're doing as well, similar to Women in Big Data Forum at Intel, is let's start creating a pipeline of women that are educated in the technical side and the software softer skill side that are interested and find their passion so that we can help motivate them, that you can do this. The sky's the limit where data science is concerned. >> Absolutely, absolutely. And it's great to see actually everybody recognize the value of building the pipeline and reaching out beyond the university students. Because have to get more and more girls getting into the science and tech sector. And we have to start from young. And I, yeah, totally agree, I think we really need to build our pipeline and a pipeline for our pipeline. >> Yes, exactly. And also that sort of sustaining momentum as women, you know, go in university and study STEM subjects, get into the field. Obviously retention is a big challenge that the tech industry and STEM fields alike have faced. But that retention, that motivation, and I think organizations like this, just with this, you can feel the passion when you walk into this alumni center at Stanford is really key. We thank you so much for carving out some time to share your insights and your career path and your recommendations on theCUBE and wish you continued success at Intel and with Women in Big Data Forum, which I'm sure we'll see you back at WiDS next year. >> Alright, thank you, thanks Lisa. >> Absolutely, my pleasure. We want to thank you, you have been watching theCUBE live from the Women in Data Science Conference 2018. Hashtag WiDS2018, join the conversation, get involved. I'm Lisa Martin from Stanford. Stick around, I'll be right back with John Furrier to do a wrap of the day. (outro electronic music)

>> Narrator: Live from Stanford University in Palo Alto, California, it's the CUBE! Covering, Women in Data Science Conference 2018, brought to you by Stanford. >> Welcome back to the CUBE, we are live at Stanford University, we've been here all day at the third annual Women in Data Science Conference, WiDS 2018. This event is remarkable in its growth in scale, in its third year, and that is, in part by the partners and the sponsors that they have been able to glean quite early on. I'm excited to be joined by Vijay Raghavendra, the senior vice president of Merchant Technology and stores as well, from Walmart Labs. Vijay, welcome to the CUBE! >> Thank you, thank you for having me. >> Walmart Labs has been paramount to the success of WiDS, we had Margot Gerritsen on earlier, and I said, "How did you get the likes of a Walmart Labs as a partner?" And, she was telling me that, the coffee-- the coffee shop conversation >> Yeah, the Coupa Cafe! >> That she had with Walmart Labs a few years ago, and said, "Really, partners and sponsors like Walmart have been instrumental in the growth and the scale, of this event." And, we've got the buzz around, so we can hear the people here, but this is the big event at Stanford. There's 177 regional events, 177! In 53 countries. It's incredible. Incredible, the reach. So, tell me a little bit about the... From Walmart Labs perspective, the partnership with WiDS, what is it that really kind of was an "Aha! We've got to do this"? >> Yeah, it's just incredible, seeing all of these women and women data scientists here. It all started with Esteban Arcaute, who used to lead data science at Walmart Labs, and Search, before he moved on to Facebook with Margot. And, Karen in the cafe in Palo Alto, in 2015, I think. And Esteban and I had been talking about how we really expand the leverage of data and data science within Walmart, but more specifically, how we get more women into data science. And, that was really the genesis of that, and, it was really-- credit goes to Esteban, Margot, and Karen for, really, thinking through it, bringing it together, and, here we are. >> Right, I mean bringing it together from that concept, that conversation here at Stanford Cafe to the first event was six months. >> Yeah, from June to November, and, it's just incredible the way they put it together. And, from a Walmart Labs perspective, we were thrilled to be a huge part of it. And, all the way up the leadership chain there was complete support, including my boss Jeremy King, who was all in, and, that really helped. >> Margot was, when we were chatting earlier, she was saying, "It's still sort of surprising," and she said she's been, I think in, in the industry for, 30-plus years, and she said that, she always thought, back in the day, that by the time she was older, this problem would be solved, this gender gap. And she says, "Actually, it's not like it's still stagnant," we're almost behind, in a sense. When I look at the ... women that are here, in Stanford, and those that are participating via those regional events, the livestream that WiDS is doing, as well as their Facebook livestream. You know, the lofty goal and opportunity to reach 100,000 people shows you that there's clearly a demand, there's a need for this. I'd love to get your perspective on data science at Walmart Labs. Tell me a little bit about the team that you're leading, you lead a team of engineers, data scientists, product managers, you guys are driving some of the core capabilities that drive global e-commerce for Walmart. Tell me about, what you see as important for that female perspective, to help influence, not only what Walmart Labs is doing, but technology and industry in general. >> Yeah. So, the team I lead is called Merchant Technology, and my teams are responsible for, almost every aspect of what drives merchandising within Walmart, both on e-commerce and stores. So, within the purview of my teams are everything from the products our customers want, the products we should be carrying either in stores or online, to, the product catalog, to search, to the way the products are actually displayed within a store, to the way we do pricing. All of these are aspects of what my teams are driving. And, data and data science really put me at every single aspect of this. And the reason why we are so excited about women in data science and why getting that perspective is so important, is, we are in the retail business, and our customers are really span the entire spectrum, from, obviously a lot of women shop at Walmart, lot of moms, lot of millennials, and, across the entire spectrum. And, our workforce needs to reflect our customers. That's when you build great products. That's when you build products that you can relate to as a customer, and, to us that is a big part of what is driving, not just the interest in data science, but, really ensuring that we have as diverse and as inclusive a community within Walmart, so we can build products that customers can really relate to. >> Speaking of being relatable, I think that is a key thing here that, a theme that we're hearing from the guests that we're talking to, as well as some of the other conversations is, wanting to inspire the next generation, and helping them understand how data science relates to, every industry. It's very horizontal, but it also, like a tech company, or any company these days is a tech company, really, can transform to a digital business, to compete, to become more profitable. It opens up new business models, right, new opportunities for that. So does data science open up so many, almost infinite opportunities and possibilities on the career front. So that's one of the things that we're hearing, is being able to relate that to the next generation to understand, they don't have to fit in the box. As a data scientist, it sounds like from your team, is quite interdisciplinary, and collaborative. >> And, to us that is really the essence of, or the magic of, how you build great products. For us data science is not a function that is sitting on the side. For us, it is the way we operate as we have engineers, product managers, folks from the business teams, with our data scientists, really working together and collaborating every single day, to build great products. And that's, really how we see this evolving, it's not as a separate function, but, as a function that is really integrated into every single aspect of what we do. >> Right. One of the things that we talked about is, that's thematic for WiDS, is being able to inspire and educate data scientists worldwide, and obviously with the focus of helping females. But it's not just the younger generation. Some of the things that we're also hearing today at WiDS 2018 is, there's also an opportunity within this community to reinvigorate the women that have been in, in STEM and academia and industry for quite a while. Tell me a little bit more about your team and, maybe some of the more veterans and, how do you kind of get that spirit of collaboration so that those that, maybe, have been in, in the industry for a while get inspired and, maybe get that fire relit underneath them. >> That's a great question, because we, on our teams, when you look across all the different teams across different locations, we have a great mix of folks that bring very different, diverse experiences to the table. And, what we've found, especially with the way we are leveraging data, and, how that is invigorating the way we are... How people come to the table, is really almost seeing the art of what is possible. We are able to have, with data, with data science, we are able to do things that, are, really step functions in terms of the speed at which we can do things. Or, the- for example, take something as simple as search, product search, which is one of the, capabilities we own, or my team is responsible for, but, you could build the machine learning ranking, and, relevance and ranking algorithms, but, when you combine it with, for example, a merchant that really fundamentally understands their category, and you combine data science with that, you can accelerate the learning in ways that is not possible. And when folks see that, and see that in operation that really opens up a whole, slew of other ideas and possibilities that they think about. >> And, I couldn't agree more. Looking at sort of the skillset, we talk a lot about, the obvious technical skillset, that a data scientist needs to have, but there's also, the skills of, empathy, of communication, of collaboration. Tell me about your thoughts on, what is an ideal mix, of skills that that data scientist, in this interdisciplinary function, should have. >> Yeah, in fact, I was talking with a few folks over lunch about just this question! To me, some of the technical skills, the grounding in math and analytics, are table stakes. Beyond that, what we look for in data scientists really starts with curiosity. Are they really curious about the problems they're trying to solve? Do they have tenacity? Do they settle for the more obvious answers, or do they really dig into, the root cause, or the root, core of the problems? Do they have the empathy for our customers and for our business partners, because unless you're able to put yourself in those shoes, you're going to be approaching at, maybe, in somewhat of an antiseptic way? And it doesn't really work. And the last, but one of the most important parts is, we look for folks who have a good sense for product and business. Are they able to really get into it, and learn the domain? So for example, if someone's working on pricing, do they really understand pricing, or can they really understand pricing? We don't expect them to know pricing when they come in, but, the aptitude and the attitude is really, really critical, almost as much as the core technical skills, because, in some ways, you can teach the technical skills, but not some of these other skills. >> Right, and that's an interesting point that you bring up, is, what's teachable, and, I won't say what's not, but what might be, maybe not so natural for somebody. One of the things, too, that is happening at WiDS 2018 is the first annual Datathon. And, Margot was sharing this huge number of participants that they had and they set a few ground rules like wanting the teams to be 50% female, but, tell us about the Datathon from your global visionary sponsorship level; what excites you about that in terms of, the participation in the community and the potential of, "Wow, what's next"? >> Yeah... So, it's hugely exciting for us, just seeing the energy that we've seen. And, the way people are approaching different problems, using data to solve very different kinds of problems ... across the spectrum. And for us, that is a big part of what we look for. For us it is really about, not just coming up with a solution, that's in search of a problem, but really looking at real-world problems and looking at it from the perspective of, "Can I bring data, can I bring data science to bear on this problem?", to solve it in ways that, either are not possible, or can accelerate the way we would solve the problems otherwise. And that is a big part of what is exciting. >> Yeah, and the fact that the impact that data science can make to, every element of our lives is, like I said before, it's infinite, the possibilities are infinite. But that impact is something that, I think, how exciting to be able to be in an industry or a field, that is so pervasive and so horizontal, that you can make a really big social impact. One of they other things, too, that Margot said. She mentioned that the Datathon should be fun, and I loved that, and also have an element of creativity. What's that balance of, creativity in data science? Like, what's the mixture, because we can be maybe over-creative, and maybe interpret something that's in a biased way. What is your recommendation on how much creativity can creep into, and influence, positively, data science? >> Yeah, that's a great question, and there's no perfect answer for it. Ultimately, at least my biases towards using data and data science to, solve real problems. And... As opposed to, pure research, so our focus very much is on applied learning, and applied science. And, to me, within that, I do want the data science to be creative, data scientists to be creative, because, by putting too many guardrails, you limit the way in which they would explore the data, that they may come up with insights that, well, we might not see otherwise. And, which is why, I go back to the point I made, when you have data scientists who fundamentally understand a business, and the business problems we are trying to solve, or the business domains, I think they can then come up with very interesting, innovative ways of looking at the data, and the problem, that you might not otherwise. So, I would by no means want to limit their creativity, but I do have a bias towards ensuring that it is focused on problems we are trying to solve. >> Excellent. Well, Vijay, thank you so much for stopping by the CUBE, congratulations on the continued success of the partnership with WiDS and, we're looking forward to seeing what happens the rest of the year, and we'll probably see you next year at WiDS 2019! >> Absolutely, thank you! >> Excellent, we want to thank you, you're watching the CUBE, live from Stanford University, the third annual Women in Data Science Conference. I am Lisa Martin, I'll be right back after a short break with my next guest. (cool techno music)

>> Announcer: Live from Stanford University in Palo Alto, California, it's theCUBE! Covering Women In Data Science Conference 2018. Brought to you by-- >> Coverage of Women in Data Science 2018. I am Lisa Martin. We're at Stanford University. This is where the big in-person event is, but there are more than 177 regional WiDS events going on around the globe today. They are in 53 countries, and they're actually expecting to have about 100,000 people engaged with WiDS 2018. Pretty awesome. I'm joined by one of the speakers for WiDS 2018, Dawn Woodard, the senior data science manager of maps at Uber. Welcome to theCUBE! >> Thank you so much, Lisa. >> It's exciting to have you here. This is your first WiDS, and you are already a speaker. Tell us a little bit about what attracted you to WiDS. What was it that kind of spoke to you as a female leader in data science? >> Well, I tried to do a fair amount of reach-out to women in data science. I really feel like I've been blessed throughout my career with inspiring female mentors, including my mother, for example. Not every woman comes into her career with that kind of mentorship, so I really wanted to reach out and help provide that to some of the younger folks in our community. >> That's fantastic. One of the things that's remarkable about WiDS, one, is the growth and scale that they've achieved reaching such big, broad audiences in such a short time period. But it's also from a thematic perspective, aiming to inspire and to educate data scientists worldwide, and of course, to support females in that. What are some of the, tell us a little bit about your talk is Dynamic Pricing and Matching in Ride Sharing. What are some of the takeaways that the audience watching the livestream and here in person are going to hear from your talk? >> There are two technical takeaways, and then there's one non-technical takeaway. The first technical takeaway is that the matching algorithms that we use are really designed to reduce the amount of time that riders and drivers have to spend waiting in the app. For drivers, that means that we're working to increase the amount of time that they spend on-trip and getting paid. For riders, that means that we're reducing the amount of time that they have to wait to be picked up by a car. That's the first takeaway. The second takeaway is around dynamic pricing, and why it's important in ride-hailing services in particular. It turns out that it's really important in creating a seamless and reliable experience, both for riders and for drivers, so I talk through the technical reasons for that. Interestingly, these technical arguments are based not just on machine learning and statistics, but also on economic analyses and some optimization concepts. The third takeaway is really that data science is this incredibly interdisciplinary environment in which we have economics, statistics, optimization, machine learning, and more. >> It's really, data sciences has the opportunity, or really is, very horizontal. Every sector, every area of our lives is impacted by it. I mean, we think of all of us that use Uber and ride-sharing apps. I think that's one of the neat things that we're hearing from the event and from the speakers like yourself is these demarcated lines of career paths are blurring, or some of 'em are evaporating. And so, I think having the opportunity to talk to the younger generation, showing them how much impact they can make in this field has got to sort of be maybe, I would even guess, invigorating for you, as someone who's been in the tech in both industry and academia for a while. >> Absolutely. I think about data science as being the way that we learn about the world, statistics and data science. So, how do we use data to learn about the world, and how do we use data to improve, to make great products, to make great apps, for example. >> Exactly. Tell me a little bit about your career path. You have your PhD in statistics from Duke University. Tell me about how you got there, and then how you also got into industry. Were you always a STEM fan as a kid, or was it something that you had a passion for early on, or developed over time? >> I was always passionate about math and science. When I was an undergraduate, I did an internship with a defense contractor. That's how I got interested in machine learning in particular. That's where it took off. I decided to get a PhD in statistics from there. Statistics and machine learning are really closely related. And then, continued down that path throughout my academic career, and now my career in tech. >> What are some of the things that you think that prepared you for a being a female leader? Was it those mentors that you mentioned before? Was it the fact that you just had a passion for it and thought, "If I'm one of the only females in the room, I don't care. "This is something that's interesting to me." What were some of those foundational elements that really guided you? >> One is the inspiration of some women in my life, and if we have to be completely honest, I'm a person who, when, the very rare times in my career when somebody has acted like I couldn't hack it or couldn't make it, it always really got me angry. The way that I channeled that was really to turn it around and to say, "No problem. "I'm going to show you that I can go well beyond "anything that you had conceived of." >> You know, I love that you said that, 'cause Margot Gerritsen, one of the founders of WiDS actually said a couple hours ago, a few years ago, when they had this idea, from concept to first conference was six months, and she said she almost thought of it like a revenge conference. Like, "We can do this!" I think it's kind of, when they had this idea in 2015, the fact that even in 2015, there's still not only demand for, but the demand is growing. As we're seeing, the statistics that show a low percentage of women that have degrees in engineering, I want to say 20%, but only 11% of them are actually working in their field. We still have a lot of work to do to ignite the fire in this next generation of prospective leaders in technology. There's still a lot of groundwork to make up there. I think we're hearing that a lot at WiDS. Are you hearing that in your peer groups as well? >> Absolutely. I think one of the things that I've really focused on is mentoring women as leaders and managers within my organization, and I really find that that's an amazing way to reach out, is not just to reach out myself, but also to do that through female leaders in my own organization. For example, I've mentored and managed two women through the transition from individual contributor to manager. Just watching their trajectory afterwards is incredibly inspiring. But then, of course, those female managers bring in additional female contributors, and it grows from there. >> Right. And you have a pretty good, pretty diverse team at Uber. Tell us a little bit about your rise at Uber. One of the things that I saw on your LinkedIn profile, that you achieved pretty quickly in the first three years, or probably less, was that you led the marketplace data science team through a period of transformative growth. You started that team with 10 data scientists, and by the time you transitioned into your next role, there were 49 data scientists, including seven managers. How were you able to come in and make such a big impact so quickly? >> Well, the whole team chipped in in terms of hiring and reaching out. But at the time when I joined Uber, data science was still relatively small. Those 10 people were being asked to do all of the pricing and matching algorithms, all of the data science for Uber Pool, all of the data science for Uber Eats. We just had one person in each of these areas, and those people very quickly stepped up to the plate and said, "Okay, I need help." We worked together to help grow their teams. It's really a collaborative effort involving the whole team. >> The current team that you're managing, what does that look like from a male/female ratio standpoint? >> The current team is more than 50% female at this point, which is something that I'm really proud of. It's definitely not only my achievement. There was a manager who was leading the team just before I switched to leading maps, and that person also helped increase the presence of women in data science for Uber's mapping organization. The first data scientist on maps at Uber was a woman, actually. >> That's fantastic. And you were saying before we went live that there's a good-sized contingent of women data scientists at Uber today that are participating in WiDS up in San Francisco? >> That's right, yes. We're live-streaming it. There's a Women in Data Science organization at Uber, and that organization is sponsoring the internal events for the live stream, not just for my talk, but really, the whole conference. >> That's one of the things that Margot Gerritsen was also saying, that from a timing perspective, they really knew they were on to something pretty quickly, and being able to take advantage of technology, live streaming, they're also doing it on Facebook, gives them that opportunity to reach a bigger audience. It also is, for you and your peers as speakers, gives you an even bigger platform to be able to reach that audience. But one of the things I find interesting about WiDS is it's not just the younger audience. Like Maria Klawe had said in her opening remarks this morning and before, that the optimal time that she's found of reaching women to get them interested in STEM subjects is first year college, first semester of college. I actually had the same exact experience many years ago, and I didn't realize that was a timing that was actually proven to be the most successful. But it's not just young women at that stage of their university career. It's also those who've been in tech, academia, and industry for a while who, we're hearing, are feeling invigorated by events like WiDS. Do you feel the same? Is this something that just sort of turns up that bunsen burner maybe a little bit higher? >> Oh, it's incredibly empowering to be in a room full of such technically powerful women. It's a wonderful opportunity. >> It really is, and I think that reinvigoration is key. Some of the things like, as we look at what you've already achieved at Uber so far, and we're in 2018, what are some of the things that you're looking forward to your team helping to impact for Uber in 2018? >> In 2018, we're looking to magnify the impact of data science within Uber's mapping organization, which is my main focus right now. Maps at Uber does several things. Think of Uber as being a physical logistics platform. We move people and things from point A to point B. Maps, as our physical world, really impacts every aspect of the user experience, both for riders and for drivers. And then, whenever we're making a dispatch decision or a pricing decision, we need to know something about how long it would take this driver to get to this rider, for example, which is really a mapping prediction. We are looking at increasing the presence of data science within the mapping organization, really bringing that perspective to the table, both at the individual contributor level, but really also growing leadership of data science within the mapping organization so that we can help drive the direction of maps at Uber through data-driven insights. >> Data-driven insights, I'm glad that you brought that up. That's something that, as we talk about data science. Data science is helping to make decisions on policy, healthcare, so many different things, you name it. It really seems like these blurred lines of job categories, as businesses use data science, and even Uber, to extend, grow the business, open new business models, so can the next generation leverage data science to just open up this infinite box, if you will, of careers that they can go into and industries they can impact by having this foundation of data science. >> Absolutely. Well, any time we have to make a decision about what direction we go in, right, as a business, for example, as an organization, then doing that starting from data, understanding what is the world really like, what are the opportunities, what are the places in which we as a company are not doing very well, for example, and can make a simple change and get an incredible impact? Those are incredibly powerful insights. What do you think, last question-ish, 'cause we're getting low on time. We talk a lot about, there's the hard skills/soft skills. Soft is kind of a weird word these days to describe that. You know, statistical analysis, data mining. But there's also this, the softer skills, empathy, things like that. How do you find those two sides, maybe it's right brain/left brain, as being essential for people to become well-rounded data scientists? >> The couple of soft skills that I really look for heavily when I'm hiring a data scientist, one is being really focused on impact, as opposed to focused on building a new shiny thing. That's quite a different approach to the world, and if we stay focused on the product that we're creating, that means that we're willing to chip in, even if the work that's being done is not as glamorous, or is not going to get as much attention, or is not as fancy of a model. We can really stay focused on what are some simple approaches that we can use that can really drive the product forward. That kind of impact focus, and also, that great attitude about being willing to chip in on something, even if it's not that fancy or if I'm not going to get in the limelight for doing this. Those are the kinds of soft skills that really are so critical for us. >> Attitude and impact. I've heard impact a number of times today. Dawn, thank you so much for carving out some time to chat with us on theCUBE. We congratulate you on being a speaker at this year's event, and look forward to talking to you next year. >> Thank you, Lisa. >> We want to thank you for watching theCUBE. We are live at Stanford for the third annual Women in Data Science Conference, hashtag #WiDS2018. Get involved in the conversation. It is happening in over 53 countries. After this short break, I will be right back with my next guest. (fast electronic music)

(energetic music) >> Announcer: Live, from Stanford University in Palo Alto, California, it's The Cube, covering Women in Data Science Conference 2018. Brought to you by Stanford. >> Welcome back to The Cube. We are live at Stanford University at the third annual Women in Data Science Conference. I am Lisa Martin. We've had a really exciting day so far, talking with a lot of female leaders in different parts of STEM fields. And I'm excited to be joined by my next guest, who is a speaker at this year's WIDS 2018 event, Daniela Witten, the Associate Professor of Statistics and Biostatistics at the University of Washington. Daniela, thanks so much for stopping by The Cube. >> Oh, thanks so much for the invitation. >> So here we are at Stanford University. You spent quite a lot of time here. You've got three degrees from Stanford, so it's kind of like coming back home? >> Yeah, I've spent from 2001 to 2010 here. I started with a bachelor's degree in math and biology, and then I did a master's, and finally a PhD in statistics. >> And so now you're up at the University of Washington. Tell us about that. What is your focus there? >> Yeah, so my work is in statistical machine learning, with applications to large scale data coming out of biology. And so the idea is that in the last ten or 20 years, the field of biology has been totally transformed by new technologies that make it possible to measure a person's DNA sequence, or to see the activity in their brain. Really, all different types of measurements that would have been unthinkable just a few years ago. But unfortunately, we don't yet know really how to make sense of these data statistically. So there's a pretty big gap between the data that we're collecting, or rather, the data that biologists are collecting, and then the scientific conclusions that we can draw from these data. So my work focuses on trying to bridge this gap by developing statistical methods that we can use to make sense of this large scale data. >> That sounds exciting. So, WIDS, this is the third year, and they have grown this event remarkably quickly. So, we had Margot Garritsen on the program a little bit earlier, and she had shared 177 regional WIDS events going on today, this week, in 53 countries. And they're expecting to reach 100,000 people. So, for you, as a speaker, what is it that attracted you to participate in the WIDS movement, and share your topic, which we'll get to in a second, what was it that sort of attracted you to that? >> Well, first of all, it's an honor to be invited to participate in this event, which, as you mentioned, is getting live streamed and so many people are watching. But what's really special for me, of course, as a woman, is that there's so many conferences out there that I speak at, and the vast majority have a couple of female speakers, and it's not because there's a lack of talent. There are plenty of very qualified women who could be speaking at these conferences. But often, the conference organizers just don't think of women right away, or maybe add a couple women as an afterthought to their speaker lineups. And so it's really wonderful to be part of a conference where all of the speakers are women, and so we can really see the broad ways in which women are contributing to data science, both in and out of industry. >> And one of the things that Margot shared was, she had this idea with her co-founders only three years ago in 2015, and they got from concept to their first event in six months. >> Daniela: Women know how to get things done. >> We do, don't we? (laughs) But also what it showed, and even in 2015, and we still have this problem in 2018, is there's a massive demand for this. >> Yeah. >> The statistics, speaking of statistics, the numbers show very few women that are getting degrees in STEM subjects are actually working in their field. I just saw this morning, it's really cool, interactive infographic that someone shared with me on Twitter, thank you very much, that showed that 20 percent of females get degrees in engineering, but only 11 percent of them are working in engineering. And you think, "How have we gone backwards in the last 30 years?" But at least now we've got this movement, this phenomenon that is WIDS to start, even from an awareness perspective, of showing we don't have a lot of thought diversity. We have a great opportunity to increase that, and you've got a great platform in order to share your story. >> Yeah. Well, I think that you raise a good point though, as, even though the number of women majoring in STEM fields, at least in some areas of STEM has increased, the number of women making it higher up in the STEM ladder hasn't, for the most part. And one reason for this is possibly the lack of female role models. So being able to attend a conference like this, for young women who are interested in developing their career in STEM, I'm sure is really inspirational and a great opportunity. So it's wonderful for Margot and the other organizers to have put this together. >> It is. Even on the recruiting side, some of the things that still surprise me are when some, whether it's universities or companies that are going to universities to recruit for STEM roles, they're still bringing mostly men. And if there are females at the events, they're, often times they're handing out swag, they're doing more event coordination, which is great. I'm a marketer. There's a lot of females in marketing. But it still shows the need to start from a visibility standpoint and a messaging standpoint alone. They've got to flip this. >> I completely agree with that, but it also works the other way. So, often a company or an academic department might have a few women in a particular role, and those women get asked to do everything. Because they'll say, "Oh, we're going to Stanford to recruit. We need a woman there. We're having some event, and we don't want it to look totally non-diverse, so we need a woman there too." And the small number of women in STEM get asked to do a lot of things that the men don't get asked to do, and this can also be really problematic. Even though the intent is good, to clearly showcase the fact that there's diversity in STEM and in academia, the end outcome can actually be hurtful to the women involved who are being asked to do more than their fair share. So we need to find a way to balance this. >> Right. That balance is key. So what I want to kind of pivot on next is, just looking at the field of data science, it's so interesting because it's very, I like 'cause it's horizontal. We just had a guest on from Uber, and we talk to on The Cube, people in many different industries, from big tech to baseball teams and things like that. And what it really shows, though, is, there's blurred lines, or maybe even lines that have evaporated between demarcated career A, B, C, D. And data science is so pervasive that it's impacting, people that are working in it, like yourself, have the ability to impact every sector, policy changes, things like that. Do you think that that message is out there enough? That the next generation understands how much impact they can make in data science? >> I think there is a lot of excitement from young people about data science. At U-dub, we have a statistics major, and it's really grown a lot in popularity in the last few years. We have a new master's degree in data science that just was started around the same time that WIDS was started, and we had 800 applicants this year. >> Wow. >> For a single masters program. Truly incredible. But I think that there's an element of it that also maybe people don't realize. So data science, there's a technical skill set that comes with it, and people are studying undergrad in statistics, and getting master's in data science in order to get that technical skill set. But there's also a non-technical skill set that's incredibly important, because data science isn't done in a vacuum. It's done within the context of interdisciplinary teams with team members from all different areas. So, for example, in my work, I work with biologists. Your previous guest from Uber, I'm sure is working with engineers and all different areas of the company. And in order to be successful in data science, you need to really not only have technical skills, but also the ability to work as a team player and to communicate your ideas. >> Yeah, you're right. Balancing those technical skills with, what some might call soft skills, empathy, collaboration, the ability to communicate, seems to be, we talked about balance earlier, a scale-wise. Would you say they're pretty equivalent, in terms of really, that would give somebody a great foundation as a data scientist? >> I would say that having both of those skill sets would give you a good foundation, yes. The extent to which either one is needed probably depends on the details of your job. >> True. So, I want to talk a little bit more about your background. Something that caught my eye was that your work has been featured in popular media. Forbes, three times, and Elle magazine, which of course, I thought, "What? I've got to talk to you about that!" Tell me a little bit about the opportunities that you've had in Forbes and in Elle magazine to share your story and to be a mentor. >> Yeah. Well, I've just been lucky to be getting involved in the field of statistics at a time when statistics is really growing in importance and interest. So the joke is, that ten years ago, if you went to a cocktail party, and you said that you were a statistician, then nobody would want to talk to you. (Lisa laughs) And now, if you go to a cocktail party and you say you're a statistician, everyone wants to know more and find out if you know of any job openings for them. >> Lisa: That's pretty cool! >> Yeah. So it's a really great time to be doing this kind of work. And there's really an increased appreciation for the fact that it's not enough to have access to a lot of data, but we really need the technical skills to make sense of that data. >> Right. So share with us a little bit about the session that you're doing here: More Data, More Statistical Problems. Tell us a little bit about that and maybe some of the three, what are the three key takeaways that the audience was hearing from you? >> Yeah. So I think the first real takeaway is, sometimes there's a feeling that, when we have a lot of data, we don't really need a deep understanding of statistics, we just need to know how to do machine learning, or how to develop a black box predictor. And so, the first point that I wanted to make is that that's not really right. Actually, the more data you have, often the more opportunity there is for your analysis to go awry, if you don't really have the solid foundations. Another point that I wanted to make is that there's been a lot of excitement about the promise of biology. So, a lot of my work has biomedical applications, and people have been hoping for many years that the new technologies that have come out in recent years in biology, would lead to improve understanding of human health and improve treatment of disease. And, it turns out, that it hasn't, at least not yet. We've got the data, but what we don't know how to do is how to analyze it yet. And so, the real gap between the data that we have and achieving its promise is actually a statistical gap. So there's a lot of opportunity for statisticians to help bridge that gap, in order to improve human health. And finally, the last point that I want to make is that a lot of these issues are really subtle. So we can try to just swing a hammer at our data and hope to get something out of it, but often there's subtle statistical issues that we need to think about, that could very much affect our results. And keeping in mind sort of the effects of our models, and some of these subtle statistical issues is very important. >> So, in terms of your team at University of Washington, or your classes that you teach, you work with undergrads. >> Yeah, I teach undergrads and PhD students, and I work mostly with PhD students. And I've just been lucky to work with incredibly talented students. I did my PhD here at Stanford, and I had a great advisor and really wonderful mentoring from my advisor and from the other faculty in the department. And so it's really great to have the opportunity now, in turn, to mentor grad students at University of Washington. >> What are some of the things that you help them with? Is it, we talk about inspiring women to get into the field, but, as you prepare these grad students to finish their master's or PhD's, and then go out either into academia or in industry, what are some of the other elements that you think is important for them to understand in terms of learning how to be assertive, or make their points in a respectful, professional way? Is that part of what you help them understand and achieve? >> That's definitely part of it. I would say another thing that I try to teach them, so everyone who I work with, all my students, they're incredibly strong technically, because you don't get into a top PhD program in statistics or biostatistics if you're not technically very strong, so what I try to help my students do is figure out not just how to solve problems, because they can solve any problem they set their mind to, but actually how to identify the problems that are likely to be high impact. Because there's so many problems out there that you can try to solve statistically, and, of course, we should all be focusing our efforts on the ones that are likely to have a really big impact on society, or on health, or whatever it is that we're trying to influence. >> Last question for you. If you look back to your education to now, what advice would you give your younger self? >> Gosh, that's a really great question. I think that I'm happy with many of the career decisions I've made. For example, getting a PhD in statistics, I think is a great career move. But, at the same time, maybe I would tell a younger version of me to take more risks, and not be so worried about meeting every requirement on time, and instead, expanding a little bit, taking more courses in other areas, and really broadening instead of just deepening my skill set. >> We've heard that sentiment echoed a number of times today, and one of the themes that I'm hearing a lot is don't be afraid to get out of your comfort zone. And it's so hard for us when we're in it, when we're younger, 'cause you don't know that, you don't have any experience there. But it's something that I always appreciate hearing from the women who've kind of led the way for those of us and then, the next generation, is, don't be afraid to get comfortably uncomfortable and as you said, take risks. It's not a bad thing, right? Well, Daniela, thanks so much for carving out some time to visit us on The Cube, and we're happy to have given you the opportunity to reach an even bigger audience with your message, and we wish you continued success at U-dub. >> Oh, thanks so much. >> We want to thank you for watching. I'm Lisa Martin live with The Cube at WIDS 2018 from Stanford University. Stick around, I'll be back with my next guest after a short break. (energetic music)

>> Narrator: Live from Stanford University in Palo Alto California, it's theCUBE, covering Women in Data Science Conference 2018. Brought to you by Stanford. >> Back to the cube, our continuing coverage of Women in Data Science 2018 continues. I am Lisa Martin, live from Stanford University. We have had a great array of guests this morning, from speakers, panelists, as well as attendees. This is an incredible one day technical event, and we're very excited to be joined by one of the panelists on the career panel this afternoon, Dr. Jennifer Prendki, the Head of Data Science at Atlassian. Welcome to theCUBE. >> Hi, it's my pleasure to be here. >> It's exciting to have you here. >> So you lead all search and machine learning initiatives at Atlassian, but you were telling me something interesting about your team, tell us about that. >> The interesting thing about my team is even though I'm the Head of Data Science, my team is not 100% data scientists. The belief of the company is that we really wanted to be in charge of our own destiny and be able to deploy our models ourselves and not be depending on other people to make deployment faster. >> Was that one of the interesting kind of culture elements that attracted you last year to Atlassian? >> What is really interesting about Atlassian, it's definitely a company that create products that I would say virtually every single software company in the world is using. They have a very strong software engineering culture, and so last year they decided to embrace data science. I thought it was a very interesting challenge for me to try and infuse a little bit of my passion for data and data-driven est to the company. >> You had quite a fast ramp at Atlassian. You joined last summer, and in less than six months, you grew your team of data scientists and engineers from three people to fifteen, and it gets better, in less than six months, across three locations, Mountain View, San Francisco, and Sydney. What were some of the key things for you that led you to make that impact so quickly? >> I think most data scientists on the world are interested in making an impact, and this is a company that obviously does a lot of impact, and a lot of people talk about this company, and there is obviously a lot of interesting data, and so I think one of the amazing things is that we have a very important role to play, because we are in a position where we have data related to the way people work with each other, collaborate with each other, and this is a very unique data set, so it's usually pretty easy to attract people to Atlassian. >> You mentioned collaboration, and that's certainly an undertone here at WiDS. In its third year, you were here last year as an attendee, now you're here this year as a speaker. They've grown this event dramatically in a couple of years alone. The opportunity to reach, they're expecting, a hundred thousand, to engage. It's a hundred and seventy-seven regional events, Margot Gerritsen gave us that number about an hour ago, in fifty-three countries. What is it about WiDS that attracted you, not only back, this year, but to welcome the opportunity to be on this career panel? >> I'll actually tell you something, so, we talk about diversity, and I think people usually think of diversity as meeting some kind of racial bar, to have, equality between male and female, or specific minorities. I think people tend to forget that the real diversity is diversity of thought, and so I actually found out that the very data science job I actually got, I was actually the only person who had a background in applied math, and everybody else was coming from a background in computer science. I quickly realized that I'm the only person who is really trained to push for, let's validate our models really properly, etc., and so that made realize how important that is to have a lot of diversity. I think WiDS is definitely a place where you see lots of women interested in the same thing, but coming from different perspective, different horizons, at different levels, and this is really something unique in the industry. >> Diversity of thought, I love that. I've not heard that before, I'm going to use that, but I'll give you credit for it. That is one of the things that is so, the more people we speak to, not just at WiDS, but at events like this on theCUBE, you hear, there's still such a need, obviously, the scale of which that WiDS has grown, shows clear demand for, we need more awareness that this diversity is missing, but in the fact that data science is so horizontal, across every industry, and it sort of is blurring the boundaries between rigid job roles, doctor, lawyer, attorney, teacher, whatever. This is quite pervasive and it provides the opportunity for data scientists globally to be able to make massive impact, but also, it still, as Margot Gerritsen was sharing earlier, it still requires what you said is that diversity in thought because having a particular small set of perspectives evaluating data, you think about it from an enterprise perspective, the types of companies that Atlassian deals with, and they are looking to grow and expand and launch new business models, but if the thought diversity is narrow, there's probably a lot of opportunity that is never going to be discovered. One of the things also I found interesting in your background, was that you found yourself sort of at this interesting juxtaposition of being a mentor, and going, wait a minute, this now gives you a great opportunity, but it also comes with some overhead. You've got it from a management perspective. What is that sort of crossroads that you've found yourself reaching and what have you done with that? >> I think it's true of probably every single technical role, but maybe data science more than others, you have to be technical to be part of the story. I think people need to have a leader that they can relate to and I think it's very important that you're still part of this. It's particularly interesting for data science, because data science is a field that moves so quickly. Usually you have people moving on to data science manager positions after being in IC and so if you don't make a conscious effort to remain that technical point of contact person, that people trust and people go to, then, when I think back of the technologies that were trendy when I was still in IC compared to now, it's really important for the managers to be still aware of that, to do a good job as a mentor and as a leader. >> You also said something I think before we went live, that is an important element for the women that WiDS is aiming to inspire and educate, today. Those that are new to the field or thinking about it, as well as those who've been it for a while. There is not just getting there, and going yes I'm interested, this is my passion, I want to have a career in this, it's also having to learn how to be a female leader, and you mentioned from a management perspective, you got to learn, you have to know how to be assertive. Tell us a little bit about the trials and tribulations that you have encountered in that respect. >> That's a very interesting question, because I'm actually very happy to see that nowadays, it's becoming easier and easier for women to step into individual contributor positions, because I think that people realize now that a woman can do just as good a job as men for a defined position, but when you're actually in a leadership position, you have to step into like a thought leadership role. Basically, you sometimes have to be in a meeting where you only have all the male engineers or male data scientists over there and say, you know what, I disagree with you, right? This as a woman becomes a little bit challenging because following the processes that are already in place, I believe that people have realized that it's okay for a woman to do that, but then being the assertive person that goes against the flow and says you are not thinking about it the right way, might sometimes be a problem, because women are not being perceived as creatures that are naturally assertive. It's typical for people, like a Head of Data Science, female data scientists, to be in a situation where they are perceived as being maybe a little bit aggressive or a little bit pushy, and you sometimes fall into this old saying, "he's the boss, she's bossy," kind of thing, and that is a challenge. >> I had someone once tell me a couple years ago, and I'm in tech as well, that I was pushy, and I think this was a language barrier thing, I think he meant to say persistent, but on that front, tell me a little bit more about your team of data scientists and engineers, and the females on your team, how do you help coach them to embrace, it's okay to speak your mind? What's that been like for you? >> I would say I was actually pretty soft-spoken myself. At some point I realized that public speaking actually helped me out there. Somebody at some point told me like, you should go, you're a brilliant, technical like go speak at a conference, and then I realized people are listening to me. You always have a little bit of like imposter syndrome kind of problem as a woman, so it helped me overcome this. Now I'm kind of trained to stimulate the ladies on my group to do the same thing, because that has worked really well for me I think. You have to get outside your comfort zone, and try to, things that help you have the self-confidence for you to get to the level of assertiveness you need to become successful. >> Exactly right, we've had a number of women on the show, today alone, talk about getting outside of your comfort zone, and one of my mentors always says, get comfortably uncomfortable. That's not an easy thing to achieve, but I think you walk in the door at WiDS, and you instantly feel inspired, and empowered. I think a number of the women that we've had on today, already, have talked about having, sort of being charged as a mentor with the responsibility like you just said, of helping those that are following your footsteps, to maybe understand how to have that confidence, and then have that right balance, so that there's professionalism there, there's respect, but it's not just about getting them into the field. It's about teaching them how to, once you're there, how to navigate a career path that is successful. >> That's an interesting thought, because I actually believe that getting comfortable with the uncomfortable is definitely something that data science is about, because you have new technologies, you have new models, you have lateral moves, like I actually was in the advertising industry as a data scientist, before switching to e-commerce and then eventually to the software industry, so I think that people who are trained to be data scientists are like that, and they should also be comfortable with the uncomfortable in their daily lives. >> Yeah, so you were mentioning before we went on that some of the people that you work with are like, it's my hope and dream to be at WiDS next year. What are some of the things that you've heard as we're at the halfway mark of WiDS today, that you're going to go back and share with your team, as well as maybe your friends, other females that are working in STEM fields as well? >> I would say, last year I was here just listening to all the people and whatever. This year, I'm on the panel, so I mean, I'm just like, nothing is impossible, I think. We've proven that over and over again in data science, I mean, who would have thought that ten years ago, we would be at the level of understanding of artificial intelligence and the entire field, right? It's all about waiting and seeing what the future has to bring to you, and we have all these amazing women today, to actually show us that, it's possible to get there, and it's exciting to be here. >> It is possible, and it's exciting. Well, Jennifer, thanks so much for carving out some of your time today to speak with us. We wish you continued success at Atlassian and we look forward to seeing you back at WiDS next year. >> Thank you. >> We want to thank you for watching theCUBE, we're live at Stanford University at the third annual Women in Data Science Conference, hashtag WiDS2018, join the conversation. I'll be right back with my next guest after a short break. (upbeat music)

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

>> Narrator: Alumni. (upbeat music) >> Announcer: Live from Stanford University in Palo Alto, California, it's theCUBE. Covering Women in Data Science Conference 2018. Brought to you by Stanford. >> Welcome back to theCUBE, we are live at Stanford University for the third annual Women in Data Science Conference, WiDS. I'm Lisa Martin, very honored to be joined by one of the co-founders of this incredible WiDS movement and phenomenon, Dr. Margot Gerritsen. Welcome to theCUBE! >> It's great to be here, thanks so much for being at our conference. >> Oh, likewise. You were the senior associate dean and director of the Institute for Computational Mathematics and Engineering at Stanford. >> Gerritsen: That's right, yep. >> Wow, that's a mouthful and I'm glad I could actually pronounce that. So you have been, well, I would love to give our audience a sense of the history of WiDS, which is very short. You've been on this incredible growth and scale trajectory. But you've been in this field of computational science for what, 30, over 30 years? >> Yeah, probably since I was 16, so that was 35 years ago. >> Yeah, and you were used to being one of few, or if not the only woman >> That's right. >> In a meeting, in a room. You were okay with that but you realized, you know what? There are probably women who are not comfortable with this and it's probably going to be a barrier. Tell us about the conception of WiDS that you and your co-founders had. >> So, May, 2015, Esteban from Walmart Labs, now at Facebook, and Karen Matthys, who's still very active, you know, one of the organizers of the conference, and I were having coffee at a cafe in Stanford and we were lamenting the fact that at another data science conference that we had been to had only had male speakers. And so we connected with the organizers and asked them why? Did you notice? Because very often people are not even aware, it's just such the norm to only have male speakers, >> Right, right. >> That people don't even notice. And so we asked why is that? And they said, "Well, you know we really tried to find "speakers but we couldn't find any." And that really was, for me, the last straw. I've been in so many of these situations and I thought, you know, we're going to show them. So we joke sometimes, a little bit, we say it's sort of a revenge conference. (laughs) We said, let's show them we can get some really outstanding women, and in fact only women. And that's how it started. Now we were sitting at this coffee shop and I said, "Let's do a conference." And they said, "Well, that would be great, next year." And I said, "No, this year. "Let's just do it. "Let's do it in November." We had six months to put it together. It was just a local conference here. We got outstanding speakers, which were really great. Mostly from the area. And then we started live-streaming because we thought it would be fun to do. And to our big surprise, we had 6,000 people on the livestream just without really advertising. That made us realize, in November 2015, my goodness, we're onto something. And we had such amazing responses. We wanted to then scale up the conference and then you can hire a fantastic conference center in San Francisco and get 10,000 people in like they do, for example, at Grace Hopper. But we thought, why not use online technology and scale it up virtually and make this a global event using the livestream, that we will then provide to people, and asking for regional events, local events to be set up all around the world. And we created this ambassador program, that is now in its second year. the first year the responses were actually overwhelming to us already then. We got 75 ambassadors who set up 75 events around the world >> In about 40 countries. >> This was last year, 2017? >> Yeah, almost exactly 13 months ago, and then this year now we have over 200 ambassadors. We have 177 events in 155 cities in 53 countries. >> That's incredible. >> So we're on every continent apart from Antarctica but we're working on that one. >> Martin: I was going to say, that's probably next year. >> Yeah, that's right. >> The scale, though, that you've achieved in such a short time period, I think, not only speaks to the power, like you said, of using technology and using live-streaming, but also, there is a massive demand. >> Gerritsen: There is a great need, yeah. >> For not only supporting, like from the perspective of the conference, you want to support and inspire and educate data scientists worldwide and support females in the field, but it really, I think, underscores, there is still in 2018, a massive need to start raising more profiles and not just inspiring undergrad females, but also reinvigorating those of us that have been in the STEM field and technology for a while. >> Gerritsen: That's right. >> So, what are some of the things, so, this year, not only are you reaching, hopefully about 100,000 people, you mentioned some of the countries involved today, but you also have a new first this year with the WiDS Datathon. >> That's right. >> Tell us about the WiDS Datathon, what was the idea behind it? You announced some winners today? >> Yeah. Yeah, so with WiDS last year, we really felt that we hit a nerve. Now there is an incredible need for women to see other women perform so well in this field. And, you know, that's why we do it, to inspire. But it's a one-time event, it's once a year. And we started to think about, what are some of the ways that we can make this movement, because it's really become a movement, into something more than just an annual, once-a-year conference? And so, Datathon is a fantastic way to do that. You can engage people for several months before the conference, and you can announce the winner at the conference. It is something that can be done really easily worldwide if it is supported again by the ambassadors, so the local WiDS organizations. So we thought we'd just try. But again, it's one of those things we say, "Oh, let's do it." We, I think, thought about this about six months ago. Finding a good data set is always a challenge but we found a wonderful data set, and we had a great response with 1100, almost 1200 people in the world participating. >> That's incredible. >> Several hundred teams. Yeah, and what we said at the time was, well, let's have the teams be 50% female at least, so that was the requirement, we have a lot of mixed teams. And ultimately, of course, that's what we want. We want 50-50, men-women, have them both at the table, to participate in data science activities, to do data science research, and answer a lot of these data questions that are now driving so many decisions. Now we want everybody around the table. So with this Datathon, it was just a very small event in the sense, and I'm sure next year it will be bigger, but it was a great success now. >> Well, congratulations on that. One of the things I saw you on a Youtube video talking about over the weekend when I was doing some prep was that you wanted this Datathon to be fun, creative, and I think those are two incredibly important ways to describe careers, not just in STEM but in data science, that yes, this can be fun. >> Yep. >> Should be if you're spending so much time every day, right, doing something for a living. But I love the creativity descriptor. Tell us a little bit about the room for interpretation and creativity to start removing some of the bias that is clearly there in data interpretation? >> Oh. (laughs) You're hitting the biggest sore point in data science. And you could even turn it around, you say, because of creativity, we have a problem too. Because you can be very creative in how you interpret the data, and unfortunately, for most of us, whenever we look at news, whenever we look at data or other information given to us, we never see this through an objective lens. We always see this through our own filters. And that, of course, when you're doing data analysis is risky, and it's tricky. 'cause you're often not even aware that you're doing it. So that's one thing, you have this bias coming in just as a data scientist and engineer. Even though we always say we do objective work and we're building neutral software programs, we're not. We're not. Everything that we do in machine learning, data mining, we're looking for patterns that we think may be in the data because we have to program this data. And then even looking at some of the results, the way we visualize them, present them, can really introduce bias as well. And then we don't control the perception of people of this data. So we can present it the way we think is fair, but other people can interpret or use little bits of that data in other ways. So it's an incredibly difficult problem and the more we use data to address and answer critical challenges, the more data is influencing decisions made by politicians, made in industry, made by government, the more important it is that we are at least aware. One of the really interesting things this conference, is that many of the speakers are talking to that. We just had Latanya Sweeney give an outstanding keynote really about this, raising this awareness. We had Daniela Witten saying this, and various other speakers. And in the first year that we had this conference, you would not have heard this. >> Martin: Really? Only two years ago? >> Yeah. So even two years ago, some people were bringing it up, but now it is right at the forefront of almost everybody's thinking. Data ethics, the issue of reproducibility, confirmations bias, now at least people now are aware. And I'm always a great optimist, thinking if people are aware, and they see the need to really work on this, something will happen. But it is incredibly important for the new data scientists that come into the field to really have this awareness, and to have the skill sets to actually work with that. So as a data scientist, one of the reasons why I think it's so fun, you're not just a mathematician or statistician or computer scientist, you are somebody who needs to look at things taking into account ethics, and fairness. You need to understand human behavior. You need to understand the social sciences. And we're seeing that awareness now grow. The new generation of data scientists is picking that up now much more. Educational programs like ours too have embedded these sort of aspects into the education and I think there is a lot of hope for the future. But we're just starting. >> Right. But you hit the nail on the head. You've got to start with that awareness. And it sounds like, another thing that you just described is we often hear, the top skills that a data scientist needs to have is statistical analysis, data mining. But there's also now some of these other skills you just mentioned, maybe more on the softer side, that seem to be, from what we hear on theCUBE, as important, >> Gerritsen: That's right. >> As really that technical training. To be more well-rounded and to also, as you mentioned earlier, to have to the chance to influence every single sector, every single industry, in our world today. >> And it's a pity that they're called softer skills. (laughs) >> It is. >> Because they're very very hard skills to really master. >> A lot of them are probably you're born with it, right? It's innate, certain things that you can't necessarily teach? >> Well, I don't believe that you cannot do this without innate ability. Of course if you have this innate ability it helps a little, but there's a growth mindset of course, in this, and everybody can be taught. And that's what we try to do. Now, it may take a little bit of time, but you have to confront this and you have to give the people the skills and really integrate this in your education, integrate this at companies. Company culture plays a big role. >> Absolutely. >> This is one of the reasons why we want way more diversity in these companies, right. It's not just to have people in decision-making teams that are more diverse, but the whole culture of the company needs to change so that these sort of skills, communication, empathy, big one, communication skills, presentation skills, visualization skills, negotiation skills, that they really are developed everywhere, in the companies, at the universities. >> Absolutely. We speak with some companies, and some today, even, on theCUBE, where they really talk about how they're shifting, and SAP is one of them, their corporate culture to say we've got a goal by 2020 to have 30% of our workforce be female. You've got some great partners, you mentioned Walmart Labs, how challenging was it to go to some of these companies here in Silicon Valley and beyond and say, hey we have this idea for a conference, we want to do this in six months so strap on your seatbelts, what were those conversations like to get some of those partners onboard? >> We wouldn't have been able to do it in six months if the response had not been fantastic right from the get-go. I think we started the conference just at the right time. There was a lot of talk about diversity. Several of the companies were starting really big diversity initiatives. Intel is one of them, SAP is another one of them. We were connected with these companies. Walmart Labs, for example, one of the founders of the company was from Walmart Labs. And so when we said, look, we want to put this together, they said great. This is a fantastic venue for us also. You see this with some of these companies, they don't just come and give us money for this conference. They build their own WiDS events around the world. Like SAP built 30 WiDS events around the world. So they're very active everywhere. They see the need, of course, too. They do this because they really believe that a changed culture is for the best of everybody. But they also believe that because they need the women. There is a great shortage of really excellent data scientists right now, so why not look at 50% of your population? >> Martin: Exactly. >> You know, there's fantastic talent in that pool and they want to track that also. So I think that within the companies, there is more awareness, there is an economic need to do so, a real need, if they want to grow, they need those people. There is an awareness that for their future, the long term benefit of the company, they need this diversity in opinions, they need the diversity in the questions that are being asked, and the way that the companies look at the data. And so, I think we're at a golden age for that now. Now am I a little bit frustrated that it's 2018 and we're doing this? Yes. When I was a student 30 some years ago, I was one of the very few women, and I thought, by the time I'm old, and now I'm old, you know, as far as my 18-year-old self, right, I mean in your 50s, you're old. I thought everything would be better. And we certainly would be at critical mass, which is 30% or higher, and it's actually gone down since the 80s, in computer science and in data science and statistics, so it is really very frustrating in that sense that we're really starting again from quite a low level. >> Right. Right. >> But I see much more enthusiasm and now the difference is the economical need. So this is going to be driven by business sense as well as any other sense. >> Well I think you definitely, with WiDS, you are beyond onto something with what you've achieved in such a short time period. So I can only imagine, WiDS 2018 reaching up to 100,000 people over these events, what do you do next year? Where do you go from here? (laughs) >> Well, it's becoming a little bit of a challenge actually to organize and help and support all of these international events, so we're going to be thinking about how to organize ourselves, maybe on every continent. >> Getting to Antarctica in 2019? >> Yeah, but have a little bit more of a local or regional organization, so that's one thing. The main thing that we'd like to do is have even more events during the year. There are some specific needs that we cannot address right now. One need, for example, is for high school students. We have two high school students here today, which is wonderful, and quite a few of them are looking at the live-stream of the conference. But if you want to really reach out to high school students and tell them about this and the sort of skill sets that they should be thinking about developing when they are at university, you have to really do a special event. The same with undergraduate students, graduate students. So there are some markets there, some subgroups of people that we would really like to tailor to. The other thing is a lot of people are very very eager to self-educate, and so what we are going to be putting together, at least that's the plan now, we'll see, if we can make this, is educational tools, and really have a repository of educational tools that people can use to educate themselves and to learn more. We're going to start a podcast series of women, which will be very, very interesting. We'll start this next month, and so every week or every two weeks we'll have a new podcast out there. And then we'll keep the momentum going. But really the idea is to not provide just this one day of inspiration, but to provide throughout the year, >> Sustained inspiration. >> Sustained inspiration and resources. >> Wow, well, congratulations, Margot, to you and your co-founders. This is a movement, and we are very excited for the opportunity to have you on theCUBE as well as some of the speakers and the attendeees from the event today. And we look forward to seeing all the great things that I think are going to come for sure, the rest of this year and beyond. So thank you for giving us some of your time. >> Thank you so much, we're a big fan of theCUBE. >> Oh, we're lucky, thank you, thank you. We want to thank you for watching theCUBE. I'm Lisa Martin, we are live at the third annual Women in Data Science Conference coming to you from Stanford University, #WiDS2018, join the conversation. I'll be back with my next guest after a short break. (upbeat music)

>> Narrator: Live from Stanford University in Palo Alto, California. It's theCUBE covering Women in Data Science Conference 2018. Brought to you by Stanford. >> Welcome back to theCUBE. Our continuing coverage live at the Women in Data Science Conference 2018, #WiDS2018. I'm Lisa Martin and I'm very excited to not only be at the event, but to now be joined by one of the speakers who spoke this morning. Mala Anand, the executive vice president at SAP and the president of SAP Leonardo Data Analytics, Mala Anand, Mala, welcome to theCUBE. >> Thank you Lisa, I'm delighted to be here. >> So this is your first WiDS and we were talking off camera about this is the third WiDS and 100,000 people they're expecting to reach today. As a speaker, how does that feel knowing that this is being live streamed and on their Facebook Live page and you have the chance to reach that many people? >> It's really exciting, Lisa and you know, it's inspiring to see that we've been able to attract so many participants. It's such an important topic for us. More and more I think two elements of the topic, one is the impact that data science is going to have in our industry as well as the impact that we want more women to participate with the right passion and being able to be successful in this field. >> I love that you said passion. I think that's so key and that's certainly one of the things, I think as my second year hosting theCUBE at WiDS, you feel it when you walk in the door. You feel it when you're reading the #WiDS2018 Twitter feed. It's the passion is here, the excitement is here. 150 plus regional WiDS events going on today in over 50 countries so the reach can be massive. What were maybe the top three takeaways from your talk this morning that the participants got to learn? >> Absolutely, and what's really exciting to see is that we see from a business perspective that customers are seeing the potential to drive higher productivity and faster growth in this whole new notion of digital technologies and the ability now for these new forms of systems of intelligence where we embed machine learning, big data, analytics, IoT, into the core of the business processes and it allows us to reap unprecedented value from data. It allows us to create new business models and it also allows us to reimagine experiences. But all of this is only possible now with the ability to apply data science across industries in a very deep and domain expertise way, and so that's really exciting and, moreover, to see diversity in the participants. Diversity in the people that can impact this is very exciting. >> I agree. You talked about digital business. Digital transformation opens up so many new business model opportunities for companies but the application of advanced analytics, for example, alone opens up so many more career opportunities because every sector is affected by big data. Whether we know it or not, right? And so the opportunity for those careers is exploding. But another thing that I think is also ripe for conversation is bringing in diverse perspectives to analyze and interpret that data. >> Absolutely. >> To remove some of the bias so that more of those business models and opportunities can really bubble up. >> Absolutely. >> Lisa: Tell me about your team at SAP Leonardo and from a diversity perspective, what's going on there? >> Yeah, absolutely. So I think your point is really valid which is, the importance of bringing in diversity and also the importance of diversity both from a gender perspective and a diversity in skills. And I think the key element of data and decision science is now it opens up different types of skills, right? It opens up the skills of course, the technology skills are fundamental. The ability to read data modeling is fundamental, but then we add in the deep domain expertise. The add in the business perspectives. The ability to story tell and that's where I see the ability to story tell with the right domain expertise opens up such a massive opportunity for different kinds of participants in this field and so within SAP itself, we are very driven by driving diversity. SAP had set a very aggressive goal for by 2017 to be at 25% of women in leadership positions and we achieved that. We've got an aggressive goal to be at 30% of women in leadership positions by 2020 and we're really excited to achieve that as well and very important as well both within Leonardo and data analytics as well, by diversity is fundamental to our growth and more importantly to the growth for the industry. I think that's going to be fundamental. >> I think that's a really important point, the growth of the industry. SAP does a lot with WiDS. We had Ann Rosenberg on last year. I saw her walking around. So from a cultural stand point, what you've described, there's really a dedicated focus there and I think it's a unique opportunity that SAP doesn't have. They're taking advantage of it to really show how a massive corporation, a huge enterprise, can really be very dedicated to bringing in this diversity. It helps the business, but it also, to your point, can make a big impact on industry. >> Absolutely, you know, culture is such a critical part of being succeeding in the business, and I think culture is an important lever that can help differentiate companies in the market. So of course it's technology, it's value creation for our customers, and I think culture is such an important part of it, and when you unpeel the lever of culture, within there comes diversity, and within there comes bringing a different diversity of skills base as well that is going to be really critical in the next generation of businesses that will get created. >> I like that. Especially sitting in Silicon Valley where there's new businesses being created every, probably 30 seconds. I'd love to understand, if we kind of take a walk back through your career and how you got to where you are now. What were some of the things that inspired you along the way, mentors? What were some of the things that you found really impactful and crucial to you being as successful as you are and a speaker at an event like WiDS? >> Oh, absolutely. It's really exciting to see that from my own personal journey, I think that one of the things that was really important is passion. And ensuring that you find those areas that you're passionate about. I was always very passionate about software and being able to look at data and analyze data. From doing my undergraduate in Computer Science, as well as my graduate work in Computer Science from Brown, and from there on out, always looking at any of the opportunities whether it was an individual contributor that I did. It's important to be passionate and I felt that that was really my guiding post to really being able to move up from a career perspective, and also looking to be in an environment, in an ecosystem, of people and environments that you're always learning from, right? And always never being afraid to reach a little bit further than your capabilities. I think ensuring that you always have confidence in the ability that you can reach, and even though the goals might feel a little bit far away at the moment. So I think also being around a really solid team of mentors and being able to constantly learn. So I would say a constant, continuous learning, and passion is really the key to success. >> I couldn't agree more. I think it's that we often, the word expert is thrown around so often and in so many things, and there certainly are people that have garnered a lot of expertise in certain areas, but I always think, "Are you really ever an expert?" There's so much to learn everyday, there's so many opportunities. But another thing that you mentioned that reminded me of, we had Maria Klawe on a little bit earlier today and one of the things that she said in her welcome address was, in terms of inspiration, "Don't worry if there's something "that you think you're not good at." >> Mala: Absolutely. >> It's sort of getting out of your comfort zone and one of my mentors likes to say, "getting comfortably uncomfortable." That's not an easy thing to achieve. So I think having people around, people like yourself, you're now a mentor to potentially 100,000 people today, alone. What are some of the steps that you recommend of, how does someone go, "I really like this, "but I don't know if I can do it." How would you help someone get comfortably uncomfortable? >> Yeah, I think first of all, building a small group I would say, of stakeholders that are behind you and your success is going to be really important. I think also being confident about your abilities. Confidence comes in failing a few times. It's okay to miss a few goals, it's okay to fail, but then you leap forward even faster. >> Failure is not a bad F word, right? >> Mala: Absolutely. >> It really can be, and I think, a lot of leaders, like yourself will say that it's actually part of the process. >> It's very much part of the process. And so I think, number one thing is passion. First you've got to be really clear that this is exactly what you're passionate about. Second is building a team around you that you can count on, you can rely on, that are invested in your success. And then thirdly is also just to ensure that you are confident. Being confident about asking for more. Being confident about being able to reach close to the impossible is okay. >> It is okay, and it should be encouraged, every day. No matter what gender, what ethnicity, that should just sort of be one of those level playing fields, I think. Unfortunately, it probably won't be but events like WiDS, and the reach that it's making today alone, certainly, I think, offer a great foundation to start helping break some of the molds that even as we sit in Silicon Valley, are still there. There's still massive discrepancies in pay grades. There's still a big percentage of females with engineering degrees that are not working in the field. And I think the more people like yourself, and some of your other colleagues that are here participating at WiDS alone today, have the opportunity to reach a broader audience, share their stories. Their failures, the successes, and all the things that have shaped that path, the bigger the opportunity we have and it's, I think, almost, sort of a responsibility for those of us who've been in STEM for a while, to help the next generation understand nobody got here with a silver spoon. Eh, some. >> Absolutely. >> But on a straight path. It's always that zig zaggy sort of path, and embrace it! >> Yeah, I think that's key, right? And the one point here is very relevant that you mentioned as well is, that it's very important for us to recognize that a love for an environment where you can embrace the change, right? In order to embrace change, it's not just people that are going through it, but people that are supporting it and sponsoring it because it's a big change. It's a change from what was an environment a few years ago to what is going to be an environment of the future, which is an environment full of diversity. So I think being able to be ambassadors of the change is really important. As well as to allow for confidence building in this environment, right? I think that's going to be really critical as well. And for us to support those environments and build awareness. Build awareness of what is possible. I think many times people will go through their careers without being aware of what is possible. Things that were certain thresholds, certain limits, certain guidelines, two years ago are dramatically different today. >> Oh yes. >> So having those ambassadors of change that can help us build awareness, with our growing community, I think is going to be really important. >> I think, some of the things too, that you're speaking to, there are boundaries that are evaporating. We're seeing them become perforated and sort of disappear, as well as maybe some of these structured careers. There's a career as this, as that. They used to be pretty demarcated. Doctor, lawyer, architect, accountant, whatnot. And now it's almost infinite. Especially having a foundation in technology with data science and the real world social implications alone, that a career in this field can deliver just kind of shows the sky's the limit. >> Yeah, absolutely. The sky's truly the limit, and I think that's where you're absolutely right. The lines are blurring between certain areas, and at the same time, I think, this opens up huge opportunity for diversity in skill set and diversity in domain. I think equally important is to ensure to be successful you want to start by driving focus, as well, right? So, how do you draw that balance? And for us to be able to mentor and guide the younger generation, to drive that focus. At the same time take leverage the opportunities open is going to be critical. >> So getting back to SAP Leondardo. What's next in this year, we're in March of 2018. What are some of the things that are exciting you that your team is going to be working on and delivering for SAP and your customers this year? >> SAP Leondardo is really exciting because it essentially allows for our customers to drive faster innovation with less risk. And it allows our customers to create these digital businesses where you have to change a business process and a business model that no single technology can deliver. So as a result we bring together machine learning, big data analytics, IoT, all running on a solid cloud platform with in-memory databases like Kana, at scale. So this year is going to be all about how we bring these capabilities together very specifically by industry and reimagine processes across different industries. >> I like that, reimagine. I think that's one of the things that you're helping to do for females in data science and computer sciences. Reimagine the possibilities. Not just the younger generation, but also those who've been in the field for a while that I think will probably be quite inspired and reinvigorated by some of the things that you're sharing. So, Mala, thank you so much for taking the time to stop by theCUBE and share your insights with us. We wish you continued success in your career and we look forward to seeing you WiDS next year. >> Thank you so much, Lisa. I'm delighted to be here. >> Excellent. >> Thank you. >> My pleasure. We want to thank you. You are watching theCUBE live from WiDS 2018, at Stanford University. I'm Lisa Martin. Stick around, my next guest will be joining me after this short break.

>> Announcer: Live, from Stanford University in Palo Alto, California, it's theCUBE covering Women in Data Science Conference 2018, brought to you by Stanford. (light techno music) >> Welcome back to theCUBE's continuing coverage of the Women in Data Science event, WiDS 2018. We are live at Stanford University. You can hear some great buzz around us. A lot of these exciting ladies in data science are here around us. I'm pleased to be joined by my next guest, Bhavani Thuraisingham, who is one of the speakers this afternoon, as well as a distinguished professor of computer science and the executive director of Cyber Security Institute at the University of Texas at Dallas. Bhavani, thank you so much for joining us. >> Thank you very much for having me in your program. >> You have an incredible career, but before we get into that I'd love to understand your thoughts on WiDS. In it's third year alone, they're expecting to reach over 100,000 people today, both here at Stanford, as well as more than 150 regional events in over 50 countries. When you were early in your career you didn't have a mentor. What does an event like WiDS mean to you? What are some of the things that excite you about giving your time to this exciting event? >> This is such an amazing event and just in three years it has just grown and I'm just so motivated myself and it's just, words cannot express to see so many women working in data science or wanting to work in data science, and not just in U.S. and in Stanford, it's around the world. I was reading some information about WiDS and I'm finding that there are WiDS ambassadors in Africa, South America, Asia, Australia, Europe, of course U.S., Central America, all over the world. And data science is exploding so rapidly because data is everywhere, right? And so you really need to collect the data, stow the data, analyze the data, disseminate the data, and for that you need data scientists. And what I'm so encouraged is that when I started getting into this field back in 1985, and that was 32 plus years ago in the fall, I worked 50% in cyber security, what used to be called computer security, and 50% in data science, what used to be called data management at the time. And there were so few women and we did not have, as I said, women role models, and so I had to sort of work really hard, the commercial industry and then the MITRE Corporation and the U.S. Government, but slowly I started building a network and my strongest supporters have been women. And so that was sort of in the early 90's when I really got started to build this network and today I have a strong support group of women and we support each other and we also mentor so many of the junior women and so that, you know, they don't go through, have to learn the hard way like I have and so I'm very encouraged to see the enthusiasm, the motivation, both the part of the mentors as well as the mentees, so that's very encouraging but we really have to do so much more. >> We do, you're right. It's really kind of the tip of the iceberg, but I think this scale at which WiDS has grown so quickly shines a massive spotlight on there's clearly such a demand for it. I'd love to get a feel now for the female undergrads in the courses that you teach at UT Dallas. What are some of the things that you are seeing in terms of their beliefs in themselves, their interests in data science, computer science, cyber security. Tell me about that dynamic. >> Right, so I have been teaching for 13 plus years full-time now, after a career in industry and federal research lab and government and I find that we have women, but still not enough. But just over the last 13 years I'm seeing so much more women getting so involved and wanting to further their careers, coming and talking to me. When I first joined in 2004 fall, there weren't many women, but now with programs like WiDS and I also belong to another conference and actually I shared that in 2016, called WiCyS, Women in Cyber Security. So, through these programs, we've been able to recruit more women, but I would still have to say that most of the women, especially in our graduate programs are from South Asia and East Asia. We hardly find women from the U.S., right, U.S. born women pursuing careers in areas like cyber security and to some extent I would also say data science. And so we really need to do a lot more and events like WiDS and WiCys, and we've also started a Grace Lecture Series. >> Grace Hopper. >> We call it Grace Lecture at our university. Of course there's Grace Hopper, we go to Grace Hopper as well. So through these events I think that, you know women are getting more encouraged and taking leadership roles so that's very encouraging. But I still think that we are really behind, right, when you compare men and women. >> Yes and if you look at the statistics. So you have a speaking session this afternoon. Share with our audience some of the things that you're going to be sharing with the audience and some of the things that you think you'll be able to impart, in terms of wisdom, on the women here today. >> Okay, so, what I'm going to do is that, first start off with some general background, how I got here so I've already mentioned some of it to you, because it's not just going to be a U.S. event, you know, it's going to be in Forbes reports that around 100,000 people are going to watch this event from all over the world so I'm going to sort of speak to this global audience as to how I got here, to motivate these women from India, from Nigeria, from New Zealand, right? And then I'm going to talk about the work I've done. So over the last 32 years I've said about 50% of my time has been in cyber security, 50% in data science, roughly. Sometimes it's more in cyber, sometimes more in data. So my work has been integrating the two areas, okay? So my talk, first I'm going to wear my data science hat, and as a data scientist I'm developing data science techniques, which is integration of statistical reasoning, machine learning, and data management. So applying data science techniques for cyber security applications. What are these applications? Intrusion detection, insider threat detection, email spam filtering, website fingerprinting, malware analysis, so that's going to be my first part of the talk, a couple of charts. But then I'm going to wear my cyber security hat. What does that mean? These data science techniques could be hacked. That's happening now, there are some attacks that have been published where the data science, the models are being thwarted by the attackers. So you can do all the wonderful data science in the world but if your models are thwarted and they go and do something completely different, it's going to be of no use. So I'm going to wear my cyber security hat and I'm going to talk about how we are taking the attackers into consideration in designing our data science models. It's not easy, it's extremely challenging. We are getting some encouraging results but it doesn't mean that we have solved the problem. Maybe we will never solve the problem but we want to get close to it. So this area called Adversarial Machine Learning, it started probably around five years ago, in fact our team has been doing some really good work for the Army, Army research office, on Adversarial Machine Learning. And when we started, I believe it was in 2012, almost six years ago, there weren't many people doing this work, but now, there are more and more. So practically every cyber security conference has got tracks in data science machine learning. And so their point of view, I mean, their focus is not, sort of, designing machine learning techniques. That's the area of data scientists. Their focus is going to be coming up with appropriate models that are going to take the attackers into consideration. Because remember, attackers are always trying to thwart your learning process. >> Right, we were just at Fortinet Accelerate last week, theCUBE was, and cyber security and data science are such interesting and pervasive topics, right, cyber security things when Equifax happened, right, it suddenly translates to everyone, male, female, et cetera. And the same thing with data science in terms of the social impact. I'd love your thoughts on how cyber security and data science, how you can educate the next generation and maybe even reinvigorate the women that are currently in STEM fields to go look at how much more open and many more opportunities there are for women to make massive impact socially. >> There are, I would say at this time, unlimited opportunities in both areas. Now, in data science it's really exploding because every company wants to do data science because data gives them the edge. But what's the point in having raw data when you cannot analyze? That's why data science is just exploding. And in fact, most of our graduate students, especially international students, want to focus in data science. So that's one thing. Cyber security is also exploding because every technology that is being developed, anything that has a microprocessor could be hacked. So, we can do all the great data science in the world but an attacker can thwart everything, right? And so cyber security is really crucial because you have to try and stop the attacker, or at least detect what the attacker is doing. So every step that you move forward you're going to be attacked. That doesn't mean you want to give up technology. One could say, okay, let's just forget about Facebook, and Google, and Amazon, and the whole lot and let's just focus on cyber security but we cannot. I mean we have to make progress in technology. Whenever we make for progress in technology, driver-less cars or pacemakers, these technologies could be attacked. And with cyber security there is such a shortage with the U.S. Government. And so we have substantial funding from the National Science Foundation to educate U.S. citizen students in cyber security. And especially recruit more women in cyber security. So that's why we're also focusing, we are a permanent coach here for the women in cyber security event. >> What have some of the things along that front, and I love that, that you think are key to successfully recruiting U.S. females into cyber security? What do you think speaks to them? >> So, I think what speaks to them, and we have been successful in recent years, this program started in 2010 for us, so it's about eight years. The first phase we did not have women, so 2000 to 2014, because we were trying to get this education program going, giving out the scholarships, then we got our second round of funding, but our program director said, look, you guys have done a phenomenal job in having students, educating them, and placing them with U.S. Government, but you have not recruited female students. So what we did then is to get some of our senior lecturers, a superb lady called Dr. Janelle Stratch, she can really speak to these women, so we started the Grace Lecture. And so with those events, and we started the women in cyber security center as part of my cyber security institute. Through these events we were able to recruit more women. We are, women are still under-represented in our cyber security program but still, instead of zero women, I believe now we have about five women, and that's, five, by the time we will have finished a second phase we will have total graduated about 50 plus students, 52 to 55 students, out of which, I would say about eight would be female. So from zero to go to eight is a good thing, but it's not great. >> We want to keep going, keep growing that. >> We want out of 50 we should get at least 25. But at least it's a start for us. But data science we don't have as much of a problem because we have lots of international students, remember you don't need U.S. citizenship to get jobs at Facebook or, but you need U.S. citizenships to get jobs as NSA or CIA. So we get many international students and we have more women and I would say we have, I don't have the exact numbers, but in my classes I would say about 30%, maybe just under 30%, female, which is encouraging but still it's not good. >> 30% now, right, you're right, it's encouraging. What was that 13 years ago when you started? >> When I started, before data science and everything it was more men, very few women. I would say maybe about 10%. >> So even getting to 30% now is a pretty big accomplishment. >> Exactly, in data science, but we need to get our cyber security numbers up. >> So last question for you as we have about a minute left, what are some of the things that excite you about having the opportunity, to not just mentor your students, but to reach such a massive audience as you're going to be able to reach through WiDS? >> I, it's as I said, words cannot express my honor and how pleased and touched, these are the words, touched I am to be able to talk to so many women, and I want to say why, because I'm of, I'm a tamil of Sri Lanka origin and so I had to make a journey, I got married and I'm going to talk about, at 20, in 1975 and my husband was finishing, I was just finishing my undergraduate in mathematics and physics, my husband was finishing his Ph.D. at University of Cambridge, England, and so soon after marriage, at 20 I moved to England, did my master's and Ph.D., so I joined University of Bristol and then we came here in 1980, and my husband got a position at New Mexico Petroleum Recovery Center and so New Mexico Tech offered me a tenure-track position but my son was a baby and so I turned it down. Once you do that, it's sort of hard to, so I took visiting faculty positions for three years in New Mexico then in Minneapolis, then I was a senior software developer at Control Data Corporation it was one of the big companies. Then I had a lucky break in 1985. So I wanted to get back into research because I liked development but I wanted to get back into research. '85 I became, I was becoming in the fall, a U.S. citizen. Honeywell got a contract to design and develop a research contract from United States Air Force, one of the early secure database systems and Honeywell had to interview me and they had to like me, hire me. All three things came together. That was a lucky break and since then my career has been just so thankful, so grateful. >> And you've turned that lucky break by a lot of hard work into what you're doing now. We thank you so much for stopping. >> Thank you so much for having me, yes. >> And sharing your story and we're excited to hear some of the things you're going to speak about later on. So have a wonderful rest of the conference. >> Thank you very much. >> We wanted to thank you for watching theCUBE. Again, we are live at Stanford University at the third annual Women in Data Science Conference, #WiDs2018, I am Lisa Martin. After this short break I'll be back with my next guest. Stick around. (light techno music)

>> Narrator: Live from Stanford University in Palo Alto, California, it's theCube, covering Women in Data Science conference 2018. Brought to you by Stanford. >> Welcome back to theCube. I'm Lisa Martin and we're live at Stanford University, the third annual Women in Data Science conference, WiDS. This is a great one day technical event with keynote speakers, with technical vision tracks, career panel and some very inspiring leaders. It's also expected to reach over 100,000 people today, which is incredible. So we're very fortunate to be joined by our next guest, Ruth Marinshaw, the CTO for Research Computing at Stanford University. Welcome to theCube, Ruth. >> Thank you. It's an honor to be here. >> It's great to have you here. You've been in this role as CTO for Research Computing at Stanford for nearly six years. >> That's correct. I came here after about 25 years at the University of North Carolina Chapel Hill. >> So tell us a little bit about what you do in terms of the services that you support to the Institute for Computational Mathematics and Engineering. >> So our team and we're about 17 now supports systems, file systems storage, databases, software across the university to support computational and data intensive science. So ICME, being really the home of computational science education at Stanford from a degree perspective, is a close partner with us. We help them with training opportunities. We try to do some collaborative planning, event promotion, sharing of ideas. We have joint office hours where we can provide system support. Margot's graduate students and data scientists can provide algorithmic support to some thousands of users across the campus, about 500 faculty. >> Wow. So this is the third year for WiDS, your third year here. >> Ruth: It is. >> When you spoke with Margot Gerritsen, who's going to be joining us later today, about the idea for WiDS, what were some of your thoughts about that? Did you expect it to make as big of >> Ruth: No. >> an impact? >> No, no people have been talking about this data tsunami and the rise of big data, literally for 10 years, but actually it arrived. This is the world we live in, data everywhere, that data deluge that had been foreseen or promised or feared was really there. And so when Margot had the idea to start WiDS, I actually thought what a nice campus event. There are women all over Stanford, across this disciplines who are engaged in data science and more who should. Stanford, if anything, is known for its interdisciplinary research and data science is one of those fields that really crosses the schools and the disciplines. So I thought, what a great way to bring women together at Stanford. I clearly did not expect that it would turn into this global phenomenon. >> That is exactly. I love that word, it is a phenomenon. It's a movement. They're expecting, there's, I said over a 100,000 participants today, at more than 150 regional events. I think that number will go up. >> Ruth: Yes. >> During the day. And more than 50 countries. >> Ruth: Yes. >> But it shows, even in three years, not only is there a need for this, there's a demand for it. That last year, I think it was upwards of 75,000 people. To make that massive of a jump in one year and global impact, is huge. But it also speaks to some of the things that Margot and her team have said. It may have been comfortable as one of or the only woman at a boardroom table, but maybe there are others that aren't comfortable and how do we help them >> Ruth: Exactly. >> and inspire them and inspire the next generation. >> Exactly. I think it's a really very powerful statement and demonstration of the importance of community and building technical teams in making, as you said, people comfortable and feeling like they're not alone. We see what 100,000 women maybe joining in internationally over this week for these events. That's such a small fraction compared to what the need probably is to what the hunger probably is. And as Margot said, we're a room full of women here today, but we're still such a minority in the industry, in the field. >> Yes. So you mentioned, you've been here at Stanford for over five years, but you were at Chapel Hill before. >> Ruth: Yes. >> Tell me a little bit about your career path in the STEM field. What was your inspiration all those years ago to study this? >> My background is actually computational social sciences. >> Lisa: Oh interesting. >> And so from an undergraduate and graduate perspective and this was the dawn of western civilization, long ago, not quite that long (Lisa laughs) but long ago and even then, I was drawn to programming and data analysis and data sort of discovery. I as a graduate student and then for a career worked at a demographic research center at UNC Chapel Hill, where firsthand you did data science, you did original data collection and data analysis, data manipulation, interpretation. And then parlayed that into more of a technical role, learning more programming languages, computer hardware, software systems and the like. And went on to find that this was really my love, was technology. And it's so exciting to be here at Stanford from that perspective because this is the birthplace of many technologies and again, referencing the interdisciplinary nature of work here, we have some of the best data scientists in the world. We have some of the best statisticians and algorithm developers and social scientists, humanists, who together can really make a difference in solving, using big data, data science, to solve some of the pressing problems. >> The social impact that data science and computer science alone can make with ideally a diverse set of eyes and perspectives looking at it, is infinite. >> Absolutely. And that's one reason I'm super excited today, this third WiDS for one of the keynote speakers, Latanya from Harvard. She's going to be talking, she's from government and sort of political science, but she's going to be talking about data science from the policy perspective and also the privacy perspective. >> Lisa: Oh yes. >> I think that this data science provides such great opportunity, not just to have the traditional STEM fields participating but really to leverage the ethicists and the humanists and the social sciences so we have that diversity of opinions shaping decision making. >> Exactly. And as much as big data and those technologies open up a lot of opportunities for new business models for corporations, I think so does it also in parallel open up new opportunities for career paths and for women in the field all over the world to make a big, big difference. >> Exactly. I think that's another value add for WiDS over it's three years is to expose young women to the range of career paths in which data science can have an impact. It's not just about coding, although that's an important part. As we heard this morning, investment banking, go figure. Right now SAP is talking about the impact on precision medicine and precision healthcare. Last year, we had the National Security Agency here, talking about use of data. We've had geographers. So I think it helps broaden the perspective about where you can take your skills in data science. And also expose you to the full range of skills that's needed to make a good data science team. >> Right. The hard skills, right, the data and statistical analyses, the computational skills, but also the softer skills. >> Ruth: Exactly. >> How do you see that in your career as those two sides, the hard skills, the soft skills coming together to formulate the things that you're doing today? >> Well we have to have a diverse team, so I think the soft skills come into play not just from having women on your team but a diversity of opinions. In all that we do in managing our systems and making decisions about what to do, we do look at data. They may not be data at scale that we see in healthcare or mobile devices or you know, our mobile health, our Fitbit data. But we try to base our decisions on an analysis of data. And purely running an algorithm or applying a formula to something will give you one perspective, but it's only part of the answer. So working as a team to evaluate other alternative methods. There never is just one right way to model something, right. And I think that, having the diversity across the team and pulling in external decision makers as well to help us evaluate the data. We look at the hard science and then we ask about, is this the right thing to do, is this really what the data are telling us. >> So with WiDS being aimed at inspiring and educating data scientists worldwide, we kind of talked a little bit already about inspiring the younger generation who are maybe as Maria Callaway said that the ideal time to inspire young females is first semester of college. But there's also sort of a flip side to that and I think that's reinvigorating. >> Yes. >> That the women who've been in the STEM field or in technology for awhile. What are some of the things that you have found invigorating in your own career about WiDS and the collaboration with other females in the industry? >> I think hearing inspirational speakers like Maria, last here and this year, Diane Greene from Google last year, talk about just the point you made that there's always opportunity, there's always time to learn new things, to start a new career. We don't have to be first year freshmen in college in order to start a career. We're all lifelong learners and to hear women present and to see and meet with people at the breakout sessions and the lunch, whose careers have been shaped by and some cases remade by the opportunity to learn new things and apply those skills in new areas. It's just exciting. Today for this conference, I brought along four or five of my colleagues from IT at Stanford, who are not data scientists. They would not call themselves data scientists, but there are data elements to all of their careers. And watching them in there this morning as they see what people are doing and hear about the possibilities, it's just exciting. It's exciting and it's empowering as well. Again back to that idea of community, you're not in it alone. >> Lisa: Right. >> And to be connected to all of these women across a generation is really, it's just invigorating. >> I love that. It's empowering, it is invigorating. Did you have mentors when you were in your undergraduate >> Ruth: I did. >> days? Were they males, females, both? >> I'd say in undergraduate and graduate school, actually they were more males from an academic perspective. But as a graduate student, I worked in a programming unit and my mentors there were all females and one in particular became then my boss. And she was a lifelong mentor to me. And I found that really important. She believed in women. She believed that programming was not a male field. She did not believe that technology was the domain only of men. And she really was supportive throughout. And I think it's important for young women as well as mid-career women to continue to have mentors to help bounce ideas off of and to help encourage inquiries. >> Definitely, definitely. I'm always surprised every now and then when I'm interviewing females in tech, they'll say I didn't have a mentor. >> Lisa: Oh. >> So I had to become one. But I think you know we think maybe think of mentors in an earlier stage of our careers, but at a later stage we talked about that reinvigoration. Are you finding WiDS as a source of maybe not only for you to have the opportunity to mentor more women but also are you finding more mentors of different generations >> Oh sure. >> as being part of WiDS? >> Absolutely, think of Karen Mathis, not just Margot but Karen, getting to know her. And we go for sort of walks around the campus and bounce ideas of each other. I think it is a community for yes, for all of us. It's not just for the young women and we want to remain engaged in this. The fact that it's global now, I think a new challenge is how do we leverage this international community now. So our opportunities for mentorship and partnership aren't limited to our local WiDS. They're an important group. But how do we connect across those different communities? >> Lisa: Exactly. >> They're international now. >> Exactly. I think I was on Twitter last night and there was the WiDS New Zealand about to go live. >> Yeah, yeah. >> And I just thought, wow it's this great community. But you make a good point that it's reached such scale so quickly. Now it's about how can we learn from women in different industries in other parts of the world. How can they learn from us? To really grow this foundation of collaboration and to a word you said earlier, community. >> It really is amazing though that in three years WiDS has become what it has because if you think about other organizations, special interest groups and the like, often they really are, they're not parochial. But they tend to be local and if they're national, they're not at this scale. >> Right. >> And so again back to it's the right time, it's the right set of organizers. I mean Margot, anything that she touches, she puts it herself completely into it and it's almost always successful. The right people, the right time. And finding ways to harness and encourage enthusiasm in really productive ways. I think it's just been fabulous. >> I agree. Last question for you. Looking back at your career, what advice would you have given young Ruth? >> Oh gosh. That's a really great question. I think to try to connect as much as you can outside your comfort zone. Back to that idea of mentorship. You think when you're an undergraduate, you explore curricula, you take crazy classes, Chinese or, not that that's crazy, but you know if you're a math major and you go take art or something. To really explore not just your academic breadth but also career opportunities and career understanding earlier on that really, oh I want to be a doctor, actually what do you know about being a doctor. I don't want to be a statistician, well why not? So I think to encourage more curiosity outside the classroom in terms of thinking about what is the world about and how can you make a difference. >> I love that, getting out of the comfort zone. One of my mentors says get comfortably uncomfortable and I love that. >> Ruth: That's great, yeah. >> I love that. Well Ruth, thank you so much for joining us on theCube today. It's our pleasure to have you here and we hope you have a great time at the event. We look forward to talking with you next time. >> We'll see you next year. >> Lisa: Excellent. >> Thank you. Buh-bye. >> I'm Lisa Martin. You're watching theCube live from Stanford University at the third annual Women in Data Science conference. #WiDS2018, join the conversation. After this short break, I'll be right back with my next guest. Stick around. (techno music)

live from Stanford University in Palo Alto California it's the cube covering women in data science conference 2018 brought to you by Stanford welcome to the cube we are alive at Stanford University I'm Lisa Martin and we are at the 3rd annual women in data science conference or woods whiz if you're not familiar is a one-day technical conference that has keynote speakers technical vision talks as well as a career panel and we are fortunate to have guests from all three today it's also an environment it's really a movement that's aimed at inspiring and educating data scientists globally and supporting women in the field this event is remarkable in its third year they are expecting to reach sit down for this 100,000 people today we were here at Stanford this is the main event in person but there's over 150 plus regional events around the globe in 50 plus countries and I think those numbers will shift up during the day and I'll be sure to brief you on that we're excited to be joined by one of the speakers featured on mainstage this morning not only a cube alum not returning to us but also the first ever female president of Harvey Mudd College dr. Maria Klawe a maria welcome back to the cube thank you it's great to be here it's so exciting to have you here I love you representing with your t-shirt there I mentioned you are the first-ever female president of Harvey Mudd you've been in this role for about 12 years and you've made some pretty remarkable changes there supporting women in technology you gave some stats this morning in your talk a few minutes ago share with us what you've done to improve the percentages of females in faculty positions as well as in this student body well the first thing I should say is as president I do nothing nothing it's like a good job the whole thing that makes it work at Harvey Mudd is we are community that's committed to diversity and inclusion and so everything we do we try to figure out ways that we will attract people who are underrepresented so that's women in areas like computer science and engineering physics it's people of color in all areas of science and engineering and it's also LGTB q+ i mean it's you know it's it's muslims it's it's just like all kinds of things and our whole goal is to show that it doesn't matter what race you are doesn't matter what gender or anything else if you bring hard work and persistence and curiosity you can succeed i love that especially the curiosity part one of the things that you mentioned this morning was that for people don't worry about the things that you you might think you're not good at i thought that was a very important message as well as something that I heard you say previously on the cube as well and that is the best time that you found to reach women young women and to get them interested in stem as even a field of study is the first semester in college and I should with you off camera that was when I found stem in biology tell me a little bit more about that and how what are some of the key elements that you find about that time in a university career that are so I guess right for inspire inspiration so I think the thing is that when you're starting in college if somebody can introduce you to something you find fun engaging and if you can really discover that you can solve major issues in the world by using these ideas these concepts the skills you're probably going to stay in that and graduate in that field whereas if somebody does that to when you're in middle school there's still lots of time to get put off and so our whole idea is that we emphasize creativity teamwork and problem-solving and we do that whether it's in math or an engineering or computer science or biology we just in all of our fields and when we get young women and young men excited about these possibilities they stick with it and I love that you mentioned the word fun and curiosity I can remember exactly where I was and bio 101 and I was suddenly I'd like to biology but never occurred to me that I would ever have the ability to study it and it was a teacher that showed me this is fun and also and I think you probably do this too showed that you believe in someone you've got talent here and I think that that inspiration coming from a mentor whether you know it's a mentor or not is a key element there that is one that I hope all of the the viewers today and the women that are participating in which have the chance to find so one of the things every single one of us can do in our lives is encourage others and you know it's amazing how much impact you can have I met somebody who's now a faculty person at Stanford she did her PhD in mechanical engineering her name is Allison Marsden I hadn't seen her for I don't know probably almost 12 years and she said she came up to me and she said I met you just as I was finishing my PhD and you gave me a much-needed pep talk and you know that is so easy to do believing in people encouraging them and it makes so much difference it does I love that so wins is as I mentioned in the third annual and the growth that they have seen is unbelievable I've not seen anything quite like it in in tech in terms of events it's aimed at inspiring not just women and data science but but data science in general what is it about wizz that attracted you and what are some of the key things that you shared this morning in your opening remarks well so the thing that attracts me about weeds is the following data science is growing exponentially in terms of the job opportunities in terms of the impact on the world and what I love about withes is that they had the insight this flash of genius I think that they would do a conference where all the speakers would be women and just that they would show that there are women all over the world who are contributing to data science who are loving it who are being successful and it's it's the crazy thing because in some ways it's really easy to do but nobody had done it right and it's so clear that there's a need for this when you think about all of the different locations around the world that are are doing a width version in Nigeria in Mumbai in London in you know just all across the world there are people doing this yeah so the things I shared are number one oh my goodness this is a great time to get into data science it's just there's so many opportunities in terms of career opportunities but there's so many opportunities to make a difference in the world and that's really important number two I shared that it's you never too old to learn math and CS and you know my example is my younger sister who's 63 and who's learning math and computer science at the northern Alberta Institute of Technology Nate all the other students are 18 to 24 she suffers from fibromyalgia she's walked with a walker she's quite disabled she's getting A's and a-pluses it's so cool and you know I think for every single person in the world there's an opportunity to learn something new and the most important thing is hard work and perseverance that it's so much more important than absolutely anything else I agree with that so much it's it's such an inspiring time but I think that you said there was clearly a demand for this what Wits has done in such a short time period demonstrates massive demand the stats that I was reading the last couple of days that show that women with stem degrees only 26% of them are actually working in STEM fields that's very low and and even can start from things like how how companies are recruiting talent and the messages that they're sending may be the right ones maybe not so much so I have a great example for you about companies recruiting talent so about three years ago I was no actually almost four years ago now I was talking in a conference called HR 50 and it's a conference that's aimed at the chief human resource officers of 50 multinationals and my talk I was talking for 25 minutes on how to recruit and retain women in tech careers and afterwards the chief HR officer from Accenture came up to me and she said you know we hire 17,000 software engineers a year Justin India 17,000 and she said we've been coming in at 30 percent female and I want to get that up to 45 she said you told me some really good things I could use she she said you told me how to change the way we advertise jobs change the way we interview for jobs four months later her name is Ellen Chowk Ellen comes up to me at another conference this has happens to be the most powerful women's summit that's run by Fortune magazine every year and she comes up and she says Maria I implemented different job descriptions we changed the way we interview and I also we started actually recruiting at Women's College engineering colleges in India as well as co-ed once she said we came in at 42% Wow from 30 to 42 just making those changes crying I went Ellen you owe me you're joining my more my board and she did right and you know they have Accenture has now set a goal of being at 50/50 in technical roles by 2025 Wow they even continued to come in all around the world they're coming in over 40% and then they've started really looking at how many women are being promoted to partners and they've moved that number up to 30% in the most recent year so you know it's a such a great example of a company that just decided we're gonna think about how we advertise we're going to think about how we interview we're gonna think about how we do promotions and we're going to make it equitable and from a marketing perspective those aren't massive massive changes so whether it expects quite simple exactly yeah these are so the thing I think about so when I look at what's happening at Harvey Mudd and how we've gotten more women into computer science engineering physics into every discipline it's really all about encouragement and support it's about believing in people it's about having faculty who when they start teaching a class the perhaps is technically very rigorous they might say this is a really challenging course every student in this course who works hard is going to succeed it's setting that expectation that everyone can succeed it's so important I think back to physics and college and how the baseline was probably 60% in terms of of grades scoring and you went in with intimidation I don't know if I can do this and it sounds like again a such a simple yet revolutionary approach that you're taking let's make things simple let's be supportive and encouraging yet hopefully these people will get enough confidence that they'll be able to sustain that even within themselves as they graduate and go into careers whether they stay in academia or go in industry and I know you've got great experiences in both I have I so I've been very lucky and I've been able to work both in academia and in industry I will say so I worked for IBM Research for eight years early in my career and you know I tribute a lot of my success as a leader since then to the kind of professional development that I got as a manager at IBM Research and you know what I think is that I there's not that much difference between creating a great learning environment and a great work environment and one of the interesting results that came out of a study at Google sometime in the last few months is they looked at what made senior engineering managers successful and the least important thing was their knowledge of engineering of course they all have good knowledge of engineering but it was empathy ability to mentor communication skills ability to encourage all of these kinds of things that we think of as quote unquote soft skills but to actually change the world and and on those sasuke's you know we hear a lot about the hard skills if we're thinking about data scientists from a role perspective statistical analysis etcetera but those soft skills empathy and also the ability to kind of bring in different perspectives for analyzing data can really have a major impact on every sector and socially in the world today and that's why we need women and people of color and people who are not well represented in these fields because data science is changing everything in the world absolutely is and if we want those changes to be for the better we really need diverse perspectives and experiences influencing things that get made because you know algorithms are not algorithms can be hostile and negative as well as positive and you know good for the world and you need people who actually will raise the questions about the ethics of algorithms and how it gets used there's a great book about how math can be used for the bad of humanity as well as the good of humanity and until we get enough people with different perspectives into these roles nobody's going to be asking those questions right right well I think with the momentum that we're feeling in this movement today and it sounds like what you're being able to influence greatly at Mudd for the last twelve years plus there is there are our foundations that are being put in place with not just on the education perspective but on the personal perspective and in inspiring the next generation giving them helping them I should say achieve the confidence that they need to sustain them throughout their career summary I thank you so much for finding the time to join us this morning on the cube it's great to have you back and we can't wait to talk to you next year and hear what great things do you influence and well next twelve months well it's wonderful to have a chance to talk with you as well thank you so much excellent you've been watching the cube we're live at Stanford University for the third annual women in data science wins conference join the conversation hashtag wins 2018 I'm Lisa Martin stick around I'll be right back with my next guest after a short break