 Live from Stanford University, it's theCUBE covering Global Women in Data Science Conference, brought to you by SiliconANGLE Media. Welcome back to theCUBE's coverage of the fourth annual Women in Data Science Conference. This Global WIDS event is the fourth annual, our fourth year here, covering it for theCUBE. I'm Lisa Martin, joined by Gianluca Icarino, the director of the Stanford Institute for Computational and Mathematical Engineering. Gianluca, it's a pleasure to have you on the program. Thank you. It's a pleasure to be here. So the Institute for Computational and Mathematical Engineering, ICME, tell us a little bit about that and its involvement in WIDS. Yes, so the institute has been, was funded 15 years ago at Stanford as a real hub for computational mathematics at Stanford. The intention was to connect computations and, in general, the disciplines around campus towards using computing for evolution, for starting new ideas, for pursuing new endeavors. And I think it's been extremely successful over the years in creating a number of different opportunities. Now, WIDS started four years ago, as you mentioned, as part of an idea that the prior director of ICME, Margot Gerritsen, had with few others. And I think the position of ICME at the center of campus really helped bring these events sort of across different fields and different disciplines. And I think it has been extremely successful in expanding and creating a new, completely new movement, a completely new way of engaging with a large, very large community. And I think ICME has been very happy to play this role and I'm continuing to be excited about the opportunities. You mentioned expansion and movement. Two things that jump out, expansion. We mentioned fourth annual. Only started, this is three and a half years ago, November 2015. And we had the pleasure of having Margot Gerritsen, one of the co-founders of WIDS on theCUBE last year at WIDS. And I loved how she actually said, very cheeky, WIDS really started sort of as a revenge conference for her and the co-founders, looking at all of the technology events and industry events and seeing a lack of diversity. But in terms of expansion, there are 150 plus regional WIDS events this year. In 50 plus countries, they're expecting over 100,000 people to engage. So this expansion and this movement that you mentioned, it's palpable. Tell us about your, what is the impetus for you to be involved in the WIDS movement? Well, I think my interest in data science and in WIDS in particular is because of the role that ICME has in the education at Stanford. We obviously have a very important opportunity to renew and remodel our curriculum and provide new opportunities for education of the new generations. And clearly starting with the opportunity of having such an audience and reaching so many different disciplines and so many different fields helps us understand exactly how to put that curriculum together. And so my focus and my interest has been mostly on making sure that ICME aligns with these new directions. And when we established the institute, computational mathematics didn't really not have data as a very, very critical component, but we are adjusting to that. Clearly it's becoming more and more important and we want to make sure we are ready for it and we make sure that the students through our curriculum are ready for the world out there. So let's talk about the students and the curriculum. You've been a professor at Stanford for a very long time. Before we get into the specifics of today's curriculum, tell me a little bit about how you have seen that evolve over time as we know that we're sort of, in terms of where the involvement in women and technology and STEM fields was in the 80s and how that's dropped off. Tell me a little bit about the evolution in that curriculum that you've seen and where the ICME is today with that adaptation. Yes, certainly the evolution has been very quick in the last few years. We have seen a number of opportunity emerging because of the technology that is out there. The fact that certainly for data science, both the software and the hardware and the technology, the methodology, the algorithms are all in the open so that there is no real barrier into sort of getting started. And I think that helps everybody sort of getting excited about the idea and the opportunity very, very quickly. So we don't really need to go through an extensive curriculum to be able to already solve problems and have an impact. And I think that perhaps is one other reason why we are sort of in a level playing field, right? Everything is available to everybody with relatively minor investment at the beginning. And so I think that's certainly a difference with respect to other disciplines where instead it was a much more laborious process to go through before you can actually start having an impact, start having a real opportunity to change world and to have sort of your vision, sort of impact in the world. So I think that's definitely something that data science and the recent development in data science have created. And so I think in terms of our role, sort of continuing role in this is to perhaps expand the view of data science as not just the algorithm, the technology, the statistical learning that you need to accomplish as a student, as a newcomer into the field, but also is other elements. And I would say certainly the challenges that we are, that are posed to data science today are challenges that have to do with the ethics, with privacy. And so these are clearly difficult to handle because they require knowledge across disciplines that typically are not related to STEM in a traditional sense. But then on the other end, I think it's the opportunity to be really creative. Data is not analyzing on its own, right? It needs the input, our sort of help in creating a story. And I think that's another element that I think makes data science a little bit different than other STEM disciplines that tend to be much more aesthetic, much more sort of a cold if you like. I think the- That's one of the things too that I find really interesting is if you look at all this statistical and computational skills, as you mentioned that a good data scientist needs to have is we look at some of the challenges with the amount of data being created. So you mentioned privacy, ethics, cyber security issues. The creative element is key for the analysis. Other things too that interest me and I'd love to get your thoughts on how you see this being developed. And the curriculum helping is empathy, collaboration, communication skills. Where is that in the curriculum? Like how important are those other skills to the hard skills? That's a great question. And I think where is in the curriculum, I think we are lagging behind. And this is one of the opportunities that we have to actually connect to other places on campus where instead the education is built much more closely around some of these topics that you mentioned. So I think, again, the real advantage and the real opportunity we have is that the data science in general reaches out to all these different disciplines in a very, very new way, if you like. I think it's probably one of the reason why it's so attractive to younger generation is the fact that it's not just the hard skills. You do need to have a lot of understanding of the technology, the foundational statistics and mathematics and so on. But it's much more than that. The communication is very important. Teamwork is extremely important. Transparency is very important. There are really all these elements that do not really make, they really didn't have a place in some of the more traditional disciplines. And I think that's definitely a great way of sort of refreshing our way of even considering education and curriculum. When you talk to something like the next or the younger generations, is that one of the things that they find or are they pleasantly surprised knowing that I need to actually be pretty well rounded to be a successful data scientist. How I analyze the data, how I tell a story. Is that something that you still find that excites but surprises this younger generation of data scientists? Certainly it's a very important component of the excitement that I see out there. The fact that you can really build a story, tell a story, communicate a story and have an impact immediately, quickly. I think it's something that the newer generation really see as a great opportunity. And rightly so, I mean it has been very difficult for more traditional disciplines to have the same level of impact. Partly because the communities tend to be very close, very limited with a lot of scrutiny. I think what we have in data science is that it's really a lot of, can do attitude, a lot of really creative force that is behind basically this movement, but in general data science. I think that may be helpful. But you're right, the impact is so potent and we've seen it and we're seeing it in every industry across the globe. And it's such an exciting time. Well, John, look, we thank you so much for sharing some of your time on the program this morning and look forward to hearing more great things that the ICME is helping with respect to women in STEM over the next year. Absolutely, thank you very much. My pleasure. We want to thank you. You're watching theCUBE live from the fourth annual Women in Data Science Conference here at Stanford University. I'm Lisa Martin. Stick around. My next guest will join me in just a moment.