>> 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. 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)