 Welcome back to theCUBE's coverage of WIDS 2023, the eighth annual Women in Data Science Conference which is held at Stanford University. I'm your host, Lisa Martin. I'm really excited to be having some great co-hosts today. I've got Hannah Freitag with me who is a data journalism master's student at Stanford. We have yet another inspiring woman in technology to bring to you today. Kelly Huang joins us, data scientist at Gilead. It's so great to have you Kelly. All right, thank you for having me today. I'm super excited to be here and share my journey with you guys. Let's talk about that journey. You recently got your PhD in Information Sciences. Congratulations. Thank you, yes. I just graduated, completed my PhD in Information Sciences from University of Illinois at Urbana-Champaign. And right now I moved to Bay Area and started my career as a data scientist at Gilead. And you're in better climate. Well, we do get still here. That's true. We've proved that a lot. The science can show us all the climate change that's going on here. That's the topic of the data found this year, right? To understand the changing in the climate. Yes. Talk a little bit about your background. You were mentioning before we went live that you come from a whole family of STEM students. So you had that kind of in your DNA. Well, I consider myself, maybe I was a lucky case. I did like grew up in a family in the STEM environment. My dad actually was a professor in computer science. So I remember when I was very, very young age, I already see like data, all of these computer science concepts. So grew up to be a data scientist is always something like in my mind. You aspire to be. Yes, yes. So I consider myself in a lucky place in that way. But also like during this journey is to become a data scientist. You need to navigate yourself too, right? Like you have these roots, like this foundation, but then you still need to kind of like figure out yourself. What is it? Is it really the career that you want to pursue? Yeah. So, but I'm happy that I'm end up here today and where I am right now. Well, we're happy to have you. Yes. Thank you. Yeah, so you're with Gilead now after you're completing your PhD. And were you always interested in the intersection of data science and health? Or is that something you explored throughout your studies? Oh, that's an excellent question. So I did have a background in computer science, but I only really get into biomedical domain when I did my PhD at school. So my research during my PhD was natural language processing, NLP, and machine learning, and their applications in biomedical domains. So, and then when I graduated, I got my first job in Gilead Science. It's super, super close and super relevant to what I, my research at school. And at Gilead, I am working in Advanced Analytics Department. And our focus is to bring artificial intelligence and machine learning into supporting clinical decision-making. And really the ultimate goal is to how to use AI to accelerate the pre-season medicine. So, yes, it's something like, I'm very lucky to get the first job, which is very close to my research at school. That's outstanding. You know, when we talk about AI, we can't not talk about ethics, bias. Right, equities, but in healthcare. Exactly, exactly. Equities and healthcare, equities and so many things. Talk a little bit about what excites you about AI, what you're doing at Gilead. So really influence, I mean, we're talking about something that's influencing life and death situations. Right. How do you, are you using AI in a way that is really maximizing the opportunities that AI can bring and maximizing the value in the data, but helping to dial down some of the challenges that come with AI? Yep. So, as you may know already with the digitalization of medical records, this is now a day is a, is a, we have tremendous opportunities to fulfill the dream of precision medicine. And what I mean by precision medicines mean now the treatments for people can be really tie-loss to individual patients depending on their own characteristic or demographic or whatever. And natural language processing and machine learning at AI in general really play a key role in that innovation, right? Because like there's a vast amount of information of patients and patient journeys or patient treatment is conducted and recorded in text. So, yes. So that's why like our group was established. Actually our department, Advanced Analytics Department in Gilead is pretty new. We established our department last year. Oh, wow. Really our mission is to bring AI into this field because we see the opportunity now. We have a vast amount of data about patient, about their treatments, how we can mine these data, how we can understand and tie-loss the treatment to individuals and give everyone better care. So... I love that you brought up precision medicine. You know, I always think if I kind of abstract everything, technology, data, connectivity, we have this expectation in our consumer lives we can get anything we want. Not only can we get anything we want, but we expect whoever we're engaging with, whether it's Amazon or Uber or Netflix to know enough about me to get me that precise next step. I don't think about precision medicine, but you bring up such a great point. We expect these tailored experiences in our personal lives. Why not expect that in medicine as well? And have a tailored treatment plan based on whatever you have based on data, your genetics and being able to use NLP, machine learning and AI to drive that is really exciting. Yeah, you recap it very well. But then you also bring up a good point about the challenges to bring AI into this field, right? Definitely, this is an emerging field, but also very challenging because we talk about human health. We are doing the work that has direct impact to human health. So everything needs to be whatever model, machine learning model that you are building, developing, it needs to be precise. It needs to be evaluated properly before using as a product, apply it into the real practice. So it's not like recommendation system for shopping or anything like that. We're talking about our actual health, so yes, it's challenging that way. Yeah, with that, you already answered one of the next questions I had because medical data and health data is very sensitive. And how you at Gilead tried to protect this data, to protect the human beings, who are the data in the end. The security aspect is critical. You bring up a great point about sensitive data. We think of healthcare as sensitive data or PII if you're doing a bank transaction. We have to be so careful with that. Where is security, data security in your everyday work practices within data science? Is it, I imagine it's a fundamental piece. Yes, for sure, at Gilead, for sure, for in data science organization, we have intensive trainings for employees about data privacy and security, how you use the data. But then also at the same time, when we work directly with data set, it's not that we have direct information about patient, it's like very granular level. Everything needs to be kind of like anonymized at some point to protect patient privacy. So we do have rules, policies, and to follow, to put that in place in our organization. Very much needed. So some of the conversations we heard, were you able to hear the keynote this morning? Yes, I did, I attended, I listened to all of them. Isn't it fantastic? Yes, yes, especially hearing these women from different backgrounds, a different level of their professional life, sharing their journeys, it's really inspiring. And Hannah and I've been talking about, a lot of those journeys look like this. I know. Just kind of go, it's very, you're just linear, but you're kind of the exception. Yeah, that's why I consider my case, I was lucky to grow up in that STEM environment. But then again, back to my point at the beginning, sometimes you need to navigate yourself too. Like I did mention about, I did my bachelor degree in Vietnam in STEM field in computer science. And that time, there's only five girls in a class of 100 students. So I was not the smartest person in the room, and I kind of my minority in that area, right? So at some point I asked myself like, huh, I don't know, is this really my career? It seems that others like male people or students, they did better than me. But then you're kind of like, I always have this passion of data, data, like, so you just like navigate yourself, keep pushing yourself over those journey, and like being where I am right now. And look what you've accomplished. Yeah, thank you. Yeah, that's very inspiring. And yeah, you mentioned how you were in the classroom and you were only one of the few women in the room. And what inspired or motivated you to keep going, even though sometimes you were at these points where you're like, okay, is this the right thing? Is this the right thing for me? What motivated you to keep going? Well, I think personally for me as a data scientist or for women working in data science in general, I always try to find a good story from data. It's not, when you have a data set, well, it's important for you to come up with methodologies how you, like, what are you going to do with the data set. But I think it's even more important to, kind of like getting the context of the data set. Like think about it, like what is the story behind this data set? What is the thing that you can get out of it and what is the meaning behind? How can we use it to be helped, use it in a useful way to have in some certain use case. So I always have that kind of curiosity and encouragement in myself. Like every time someone handed me a data set, I would always think about that. So it's helped me to build up this passion for me and then become a data set. So you had that internal drive, I think it's in your DNA as well, when you were five percent women in your computer science undergrad in Vietnam, yet as Hannah was asking you, you found a lot of motivation from within. You embrace that, which is so key. When we look at some of the statistics, speaking of data, of women in technical roles, we've seen it hover around 25% the last few years, probably five to 10. I was reading some data from AnitaB.org over the weekend and it shows that it's now, in 2022, the number of women in technical roles rose slightly, but it rose 27.6%. So we're seeing the needle move slowly, but one of the challenges that still remains is attrition. Women who are leaving the role, you've got your PhD, you have a 10 month old, you've got more than one child. What would you advise to women who might be at that crossroads of not knowing should I continue my career and climbing the ladder, or do I just go be with my family or do something else? What's your advice to them in terms of staying the path? I think it's really doused to that you need to follow your passion, but in any kind of job, not only like in data science, if you want to be a baker, or you want to be a chef, or you want to be a software engineer, it's really like you need to ask yourself, is it something that you're really passionate about? Because if you're really passionate about something, regardless how difficult it is, like regardless like you have made so many kids to take care of, you have the whole family to take care of, you have this and that, you still can find your time to spend on it. So it's really like let yourself drive your own passion, drive the way where you're leading to. I guess that's my advice. Kind of like following your own North Star, right, is what you're suggesting. What role have mentors played in your career path to where you are now? Have you had mentors on the way or people who inspired you? Well, I did, I certainly met quite a lot of women who inspired me during my journey. But right now at this moment, one person, particular person that I, just probably into my mind is my current managers. She's also data scientist. She's originally from Caribbean and then came to the US, did her PhDs too and now led a group of all women. So believe it or not, I am in a group of all women working in data science. So she's really like someone inspiring me a lot. Like someone I look into up to in this career. I love that. You went from being one of five females in a class of 100 to now having a PhD in information sciences and being on an all-female data science team. That's pretty cool. No, it's great. Yeah, it's great. And then you see how fascinating that whole thing shift, right? Now today we are here in a conference that all our women in data science thought. Yeah. It's extraordinary. So this year, so we're fortunate to have WIDDS coincide this year with the actual International Women's Day, March 8th, which is so exciting. WIDDS is always around this time of year, but it's great to have it on the day. The theme of this International Women's Day this year is embrace equity. When you think of that theme and your career path and what you're doing now and who inspires you, how can companies like Gilead benefit from embracing equity? What are your thoughts on that as a theme? So I feel like I'm very lucky to get my first job at Gilead, not only because the work that we are doing here very close to my research at school, but also because of the working environment at Gilead. Inclusions actually is one of the five core values of Gilead. Nice. So by that, we try to create a working environment that all the differences are valued, like regardless of your background, your gender. So at Gilead, we have women at Gilead, which is a global network of female employees that help us to strengthen our inclusion culture and also to influence our voices into the company culture, company policy and practice. So yeah, I'm very lucky to work in that environment nowadays. It's impressive to not only hear that you're on an all-female data science team, but what Gilead is doing and the actions they're taking. It's one thing we've talked about this, Hannah, for companies and regardless of industry to say we're going to have 50% women in our workforce by 2030, 2035, 2040. It's a whole other ballgame for companies like Gilead to actually be putting pen to paper, to actually be creating a strategy that they're executing on. That's awesome, and it must feel good to be a part of a company who's really adapting its culture to be more inclusive because there's so much value that comes from inclusivity, thought diversity that ultimately will help Gilead produce better products and services. Yeah, yes, yeah. Actually, this here is the first here. Gilead is a sponsor of the Witch Conference, and we are so excited to establish this relationship and looking forward to having more collaboration with Witch in the future. Excellent, Kelly, we've had such a pleasure having you on the program. Thank you for sharing your linear path. You are definitely a unicorn. We appreciate your insights and your advice to those who might be navigating similar situations. Thank you for being on theCUBE today. Thank you so much for having me. Oh, it was our pleasure. For our guests and Hannah Freitag, this is Lisa Martin from theCUBE, coming to you from WIDS 2023, the eighth annual conference. Stick around, our final guest joins us in just a minute.