 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 with, 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 Marenshaw, 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 the 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 at 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. Margo'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 Weds. Your third year here. It is. When you spoke with Margo Garrison, who's going to be joining us later today, about the idea for Weds, what were some of your thoughts about that? Did you expect it to make as big of 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 Margo had the idea to start Weds, I actually thought, what a nice campus event. There are women all over Stanford, across 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 discipline. 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's exactly, I love that word. It is a phenomenon, it's a movement. They're expecting, I said over 100,000 participants today at more than 150 regional events. I think that number will go up during the day in more than 50 countries. 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 Margo and her team have said. They 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 and inspire the next generation? Exactly. I think it's really a 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 may be 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 Margo 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. Tell me a little bit about your career path in the STEM field. What was your inspiration all those years ago to study this? Well, my background is actually computational social sciences. How interesting. And so from an undergraduate and graduate perspective, and this was the dawn of Western civilization long ago, not quite that long, but long ago. And even then I was drawn to programming and data analysis and data sort of discovery. As a graduate student, and then for a career, worked at a demographic research center at UNC Chapel Hill, where first hand, 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 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. I think 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, and 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 its 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 agents 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. How do you see that in your career as those two sides, the hard skills, the soft skills come in 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 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 WID's 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 Claway has said, the ideal time to inspire young females is for semester in college. But there's also sort of a flip side to that and that's I think reinvigorating. The women who've been in the STEM field or in technology for a while. What are some of the things you have found invigorating in your own career about WID's and the collaboration with other females in the industry? I think hearing inspirational speakers like Maria last year 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. That we're all lifelong learners and to hear women present and to see and meet with people at the breakout sessions in the lunch whose careers have been shaped by, in 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 they're 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. And to be connected to all of these women across generations is really, it's just invigorating. I love that. It's empowering, it is invigorating. Did you have mentors when you were in your undergrad days? Were they males, females, both? I would 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. Encourage. 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. So I had to become one. But I think 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 as a being part of WIDs? Absolutely, think of Karen Mappas, not just Margaret, but Karen getting to know her and we go for sort of walks around the campus and sort of bounce ideas off 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. And 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? They're international now. Exactly, I think I was on Twitter last night and there was the WIDs New Zealand about to go live 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 and 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. And so again, back to it's the right time. It's the right set of organizers. I mean, Margot, anything 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 I mean, not that that's crazy but if you're a map 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 could 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. 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 with the event. We look forward to talking to you next time. We'll see you next year. Thank you. Excellent, bye bye. I'm Lisa Martin, you're watching theCUBE live from Stanford University at the Third Annual Women in Data Science Conference. Hashtag with 2018, join the conversation. After this short break, I'll be right back with my next guest. Stick around.