 Live from Stanford University, it's theCUBE. Covering Stanford Women in Data Science 2020. Brought to you by SiliconANGLE Media. Hi, and welcome to theCUBE. I'm your host, Sonia Tagare, and we're live at Stanford University, covering WID's Women in Data Science Conference, the fifth annual one. And joining us today is Daphne Kohler, who is the co-founder, who, sorry, is the CEO and founder of Incetro. Daphne, welcome to theCUBE. Nice to be here, Sonia. Thank you for having me. So tell us a little bit about Incetro, how you got founded, and more about your role. So I've been working in the intersection of machine learning and biology and health for quite a while, and it was always a bit of an interesting journey in that the data sets were quite small and limited. We are now in a different world, where there's tools that are allowing us to create massive biological data sets that I think can help us solve really significant societal problems. And one of those problems that I think is really important is drug discovery and development, where despite many important advancements, the costs just keep going up and up and up. And the question is, can we use machine learning to solve that problem better? And you talk about this more in your keynote, so give us a few highlights of what you talked about. So in the last, you can think of drug discovery and development in the last 50 to 70 years as being a bit of a glass half full, glass half empty. The glass half full is the fact that there's diseases that used to be a death sentence, or a sentence, still a lifelong of pain and suffering that are now addressed by some of the modern day medicines. And I think that's absolutely amazing. The other side of it is that the cost of developing new drugs has been growing exponentially in what's come to be known as Ear Room's Law, being the inverse of Moore's Law, which is the one we're all familiar with, because the number of drugs approved per billion US dollars just keeps going down exponentially. So the question is, can we change that curve? And you talk in your keynote about the interdisciplinary culture, so tell us more about that. I think in order to address some of the critical problems that we're facing, one needs to really build a culture of people who work together from different disciplines, each bringing their own insights and their own ideas into the mix. So at Incetro, we actually have a company that's half life scientists, many of whom are producing data for the purpose of driving machine learning models and the other half are machine learning people and data scientists who are working on those, but it's not a handoff where one group produces the data and the other one consumes and interprets it, but really they start from the very beginning to understand what are the problems that one could solve together, how do you design the experiment, how do you build the model, and how do you derive insights from that that can help us make better medicines for people? And I also wanted to ask you, you co-founded Coursera, so tell us a little bit more about that platform. So I founded Coursera as a result of work that I'd been doing at Stanford, working on how technology can make education better and more accessible. This was a project that I did here, a number of my colleagues as well, and at some point in the fall of 2011, there was an experiment of let's take some of the content that we've been developing within Stanford and put it out there for people to just benefit from and we didn't know what would happen, would it be a few thousand people, but within a matter of weeks with minimal advertising other than one New York Times article that went viral, we had 100,000 people in each of those courses, and that was a moment in time where we looked at this and said, can we just go back to writing more papers or is there an incredible opportunity to transform access to education to people all over the world? And so I ended up taking what was supposed to be a two year leave of absence from Stanford to go and co-found Coursera, and I thought I'd go back after two years, but at the end of that two year period, there was just so much more to be done and so much more impact that we could bring to people all over the world, people of both genders, people of different social economic status, every single country around the world, I just felt like this was something that I couldn't not do. And why did you decide to go from an educational platform to then going into machine learning and biomedicine? So I'd been doing Coursera for about five years in 2016 and the company was on a great trajectory, but it's primarily a content company and around me machine learning was transforming the world and I wanted to come back and be part of that and when I looked around I saw machine learning being applied to e-commerce and to natural language and to self-driving cars, but there really wasn't a lot of impact being made on the life science area and I wanted to be part of making that happen, partly because I felt like coming back to our earlier comment that in order to really have that impact, you need to have someone who speaks both languages and while there's a new generation of researchers who are bilingual in biology and in machine learning, there's still a small group and there are very few of those in kind of my age cohort and I thought that I would be able to have a real impact by building a company in this space. So it sounds like your background is pretty varied. What advice would you give to women who are just starting college now who may be interested in a similar field? Would you tell them they have to major in math or do you think that maybe there are some other majors that may be influential as well? I think there's a lot of ways to get into data science. Math is one of them, but there's also statistics and I would say that especially for the field that I'm currently in, which is at the intersection of machine learning data science on the one hand and biology and health on the other, one can get there from biology or medicine as well. But what I think is important is not to shy away from the more mathematically oriented courses in whatever major you're in because that foundation is a really strong one. There's a lot of people out there who are basically lightweight consumers of data science and you don't really understand how the methods that they're deploying, how they work and that limits them in their ability to advance the field and come up with new methods that are better suited perhaps to the problems that they're tackling. So I think it's totally fine and in fact there's a lot of value to coming into data science from fields other than math or computer science. But I think taking courses in those fields even while you're majoring in whatever field you're interested in is going to make you a much better person who lives at that intersection. And how do you think having a technology background has helped you in founding your companies and has helped you become a successful CEO? In companies that are very strongly R&D focused like in Citro and others, having a technical co-founder is absolutely essential because it's fine to have an understanding of whatever the user needs and so on and come from the business side of it and a lot of companies have a business co-founder but not understanding what the technology can actually do is highly limiting because you end up hallucinating, oh if we could only do this and that would be great but you can't and people end up oftentimes making ridiculous promises about what the technology will or will not do because they just don't understand where the landmines sit and where you're going to hit real obstacles in the path. So I think it's really important to have a strong technical foundation in these companies. And that being said, where do you see in Citro in the future and how do you see it solving say, Nash that you talked about in your keynote? So we hope that in Citro will be a fully integrated drug discovery and development company that is based on a completely different foundation than a traditional pharma company where they grew up in the old approach of that is very much a bespoke scientific analysis of the biology of different diseases and then going after targets or ways of dealing with a disease that are driven by human intuition where I think we have the opportunity to go today is to build a very data-driven approach that collects massive amounts of data and then let analysis of those data really reveal new hypotheses that might not be the ones that accord with people's preconceptions of what matters and what doesn't. And so hopefully we'll be able to over time create enough data and apply machine learning to address key bottlenecks in the drug discovery and development process so we can bring better drugs to people and we can do it faster and hopefully at much lower cost. That's great. And you also mentioned in your keynote that you think the 2020s is like a digital biology era. So tell us more about that. So I think if you look, if you take a historical perspective on science and think back, you'll realize that there's periods in history where one discipline has made a tremendous amount of progress in relatively short amount of time because of a new technology or a new way of looking at things. In the 1870s, that discipline was chemistry with the understanding of the periodic table and that you actually couldn't turn lead into gold. In the 1900s, that was physics with understanding the connection between matter and energy and between space and time. In the 1950s, that was computing where silicone chips were suddenly able to perform calculations that up until that point only people have been able to do. And then in 1990s, there was an interesting bifurcation. One was the era of data which is related to computing but also involves elements, statistics and optimization and neuroscience. And the other one was quantitative biology in which biology moved from a descriptive science of taxonomizing phenomena to really probing and measuring biology in a very detailed and high throughput way using techniques like microarrays that measure the activity of 20,000 genes at once or the human genome, sequencing of the human genome and many others. But these two fields kind of evolved in parallel and what I think is coming now 30 years later is the convergence of those two fields into one field that I like to think of as digital biology where we are able using the tools that have and continue to be developed measure biology in entirely new levels of detail, of fidelity, of scale. We can use the techniques of machine learning and data science to interpret what we're seeing and then use some of the technologies that are also emerging to engineer biology to do things that it otherwise wouldn't do and that will have implications in biomaterials and energy in the environment, in agriculture and I think also in human health and it's an incredibly exciting space to be in right now because just so much is happening and the opportunities to make a difference and make the world a better place are just so large. That sounds awesome. Daphne, thank you for your insight and thank you for being on theCUBE. Thank you. I'm Sonya Tagaria, thanks for watching. Stay tuned for more. Okay, great.