 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 Tigare, and we're live at Stanford University covering the fifth annual WIDS Women in Data Science Conference. Joining us today is Talithia Williams, who's the Associate Professor of Mathematics at Harvey Mudd College, and host of Nova Wonders at PBS. Talithia, welcome to theCUBE. Happy to be here, thanks for having me. So, you have a lot of roles, so let's first tell us about being an Associate Professor at Harvey Mudd. Yeah, I've been at Harvey Mudd now for 11 years, so it's been really a lot of fun. I'm in the math department, but I'm a statistician by training, so I teach a lot of courses in statistics, and data science, and things like that. Very cool. Yeah. And you're also a host of a PBS show called Nova Wonders. Yeah, yeah, that came about a couple years ago. Folks at PBS reached out, they'd seen my Ted Talk, and they said, hey, it looks like you could be a fun host of this science documentary show, so Nova Wonders is a six-episode series that kinda takes viewers on a journey of what the cutting-edge questions in science are. So I got to host the show with a couple other co-hosts, and really think about what are animals saying, and so we got some really fun episodes to do what's the universe made of, was one of them, what's living inside of us. That was definitely a gross one to do, but yeah, fun to figure out all the different microorganisms that live inside our body. So yeah, it's been fun to host that show as well. And do you talk about data science and AI, and all that stuff on? Yeah, oh yeah, yeah. One of the episodes, Can We Build a Brain, was dealt with a lot of data, big data, and artificial intelligence, and how good can computers get, and really sort of compared it to what we see in the movies. We're a long way away from that, but it seems like we're getting better every year at building technology that is truly intelligent. And you gave a talk today about mining for your own personal data, so give us some highlights from your talk. Yeah, so that talk sort of stemmed out of the TED talk that I gave on owning your body's data, and it's really challenging people to think about how they can use data that they collect about their bodies to help make better health decisions. And so ways that you can use your temperature data or your heart rate data, or what does this data say over time? What does it say about your body's health? And really challenging the audience to get excited about looking at that data. We have so many devices that collect data automatically for us, and often we don't pause long enough to actually look at that historical data. And so that was what the talk was about today, like here's what you can find when you actually sit down and look at that data. Well, what's the most important data you think people should be collecting about themselves? Well, definitely not your weight, because I don't wanna know what that is everyday. It depends, you know, I think for women who are in the fertile years of life, taking your daily waking temperature can tell you when your body's fertile, when you're ovulating, it can, so that information could give women during that time period really critical information. But in general, I think it's just a matter of being aware of how your body is changing. So for some people, maybe it's your blood pressure or your blood sugar, if you have high blood pressure or high blood sugar, those things become really critical to keep an eye on. And I really encourage people, whatever data they take, to be active in the understanding of and interpretation of the data. So it's not like if you take this data, you'll be healthy, you'll live to 100. It's really a matter of challenging people to own the data that they have and get excited about understanding the data that they are taking. Absolutely, putting people in charge of their own bodies. That's right, yeah, yeah. And actually speaking about that in your TED Talk, you mentioned how your doctor told you to have a C-section and you looked at the data and you said, no, I'm going to have this baby naturally. So tell us more about that. Yes, you should always listen to your medical professionals. But in this case, I will say that it was definitely more of a dialogue. And so I wasn't just sort of trying to lean on the fact that I have a PhD in statistics and I know data. It was really kind of objectively with the uncalled doctor at the time looking at the data and talking about it. And this doctor was, this was his first time seeing me. And so I think it would have been different had my personal midwife or my doctor been telling me that. But this person sort of only looked at this one chart and was making a decision without thinking about my historical data. And so I tried to bring that to the conversation and say like, let me tell you more about my body this is pregnancy number three. Like, here's how my body works. And I think this person in particular just wasn't really hearing any of that. It was like, here's my advice. We just need to do this. And I'm like, well, you know. And so as gently as possible, I tried to really share that data. And then it got to the point where it was sort of like, either you're gonna do what I say or you're gonna have to sign a waiver. And we were like, I think we'll just sign the waiver. And so that caused quite a buzz in the hospital that day. But we came back and had a very successful labor and delivery. And so yeah, it was a good decision at the time. But, you know, with that caveat that you should listen to what your doctor said. Yeah, I mean, there's a really interesting like, what's the boundary between like, what the numbers tell you and what a professional tells you. That's right, because I don't have an MD, right? And so, you know, I'm cautious not to overstep that. But I felt like in that case, the doctor wasn't really even considering the data that I was bringing. I was, we were actually induced with our first son. But again, that was more of a conversation, more of a dialogue, here's what's happening, here's what we're concerned about, and the data to really back it up. And so I felt like in that case, like, yeah, I'm happy to go with your suggestion. But by kid number three, it was just like, no, this isn't really, yeah. Great. So you also wrote a book called Power in Numbers, The Rebel Women of Mathematics. So what inspired you to write this book and what do you hope readers take away from it? Yeah, a couple different things. I remember when I saw the movie Hidden Figures, and I spent three summers at NASA working at JPL, the Jet Propulsion Laboratory. And so I had this very fond connection to, you know, having worked at NASA. And when this movie came out, and I'm sitting there watching it, and I'm like balling, just crying, and you know, I'm like, I didn't know that there were black women who worked at NASA, like before me, you know? And so it felt, it felt, it was just so transformative for me to see these stories just sort of unfold. And I thought like, well, why didn't I learn about these women growing up? Like imagine had I known about the Catherine Johnson's of the world, maybe that would have really inspired, not just me, but you know, thinking of all the women of color who aren't in mathematics or who don't see themselves working at NASA. And so for me, the book was really a way to leave that legacy to the generation that's coming up and say like there have been women who've done mathematics and statistics and data science for years. And there are women who are doing it now. So a lot of the, about a third of the book are women who are still here and like active in the field and doing great things. And so I really wanted to highlight sort of where we've been, where we've been, but also where we're going and the amazing women that are doing work in it. And it's very visual. So some folks are like, oh my gosh, women in math. And it's really like a very picturesque book of showing these beautiful images of the women and their mathematics and their work. And yeah, so I'm really proud of it. That's awesome. And even though there is like greater diversity now in the tech industry, there's still very few African-American women especially who are part of this industry. So what advice would you give to those women who feel like they don't belong? Yeah, well, A, they really do belong. And I think it's also incumbent of people in the industry to sort of recognize ways that they can be advocate for women and especially for women of color because often it takes someone who's already at the table to invite other people to the table. Like I can't just walk up and be like, move over, get out the way I'm here now. But really being thoughtful about who's not represented and how do we get those voices here? And so I think the onus is often more on people who occupy those spaces already to think about how they can be more intentional in bringing diversity into those spaces. And going back to your talk a little bit, how should people use their data? Yeah, so I mean, I think the ways that we've used our data have been to change our lifestyle practices. And so for example, when I first got a Fitbit, it wasn't really that I was like, oh, I have a goal. It was just like, yeah, I want something to keep track of my steps. And then I look at them and feel like, well, gosh, I didn't even do anything today. And so I think having sort of even that baseline data gave me a place to say, okay, let me see if I can hit 10,000 steps in a day or... And so in some ways, having the data allows you to set goals. Some people come in knowing like, I've got this goal. I want to hit it. But for me, it was just sort of like... And so I think that's also how I've started to use additional data. So when I take my heart rate data or my pulse, I'm really trying to see if I can get lower than how it was before. So the push is really like, how is my exercise and my diet changing so that I can bring my resting heart rate down? And so having the data gives me a goal to push toward and it also gives me that historical information to see like, oh, this is how far I've come. I can't stop now. That's a great social impact. That's right, yeah, absolutely. And do you think that... So in terms of a security and privacy point of view, if you're recording all your personal data on these devices, how do you navigate that? Yeah, yeah, that's a tough one. I mean, because you are giving up that data privacy, I usually make sure that the data that I'm allowing access to is sort of data that I wouldn't care if it got published on the cover of the New York Times. Maybe I wouldn't want everyone to see what my weight is, but... And so in some ways, while it is my personal data, there's something that's a bit abstract from it. Like it could be anyone's data, as opposed to say my DNA, like I'm not gonna do a DNA test. You know, I don't want my DNA to be mapped and out there for the world. But I think that's increasingly become a concern because people are giving access to their information to different companies. It's not clear how companies would use that information. So if they're using my data to build a product or make a product better, you know, we don't see any royalties from that. We don't have the benefit of it, but they have access to our data. And so I think in terms of data privacy and data ethics, there's a huge conversation to have around that. And we're only kind of at the beginning of understanding what that is. Yeah. Well, thank you so much for being on theCUBE. Really awesome to have you here. Thank you. Thanks for having me. Of course. I'm Sonia Tigari. Thanks so much for watching theCUBE and stay tuned for more.