>> Announcer: Live from Stanford University, it's the Cube, covering the Women in Data Science Conference 2017. (electronic music) >> Hi, and welcome back to the Cube. I'm Lisa Martin live at the Women in Data Science Conference, second annual, here at Stanford University, #WiDS2017. Fortunate to be joined next by Miriah Meyer, who is an Assistant Professor at the University of Utah in the School of Computing. Miriah, welcome to the Cube. >> Thank you. >> It's great to have you here. You're a speaker at this event this year. >> Yes. >> Tell us a little bit about how you got involved in WiDS and what excites you about being able to speak to this very passionate, invigorating audience? >> Yeah, so I got an invitation from one of the organizers, seems like quite some time ago, and when I looked into the conference, it just looked fantastic. I was so impressed with the speakers they had last year and the speakers for this year. It's a really amazing powerhouse of a community here. The fact that it's a great technical conference that, oh, just happens to be all women, it was pretty awesome, I was pretty flattered to get invited. Then the sort of, the energy in there is really awesome. It is different, it feels different than other technical conferences I go to. >> I completely agree. I love that you talked about just the community, because that's really what it is, and I think some of the, just the vibe that you can feel sitting here is one of excitement, it's one of passion of women who have been in this industry for a very long time in computer science, and then those young girls who are looking for inspiration. I think it's very symbiotic, right? They're learning from you, but I think you're probably also learning from them. >> Definitely. I find that every time I present my work to another group of people, a different community, I always have to come up against what my own assumptions are about how easy or not it is to understand the kind of work I do. I personally find it just so important to communicate clearly, it's probably partly why I do the work that I do. But I learn a lot every time I give a talk at a place like this. >> Wow, outstanding. Well, speaking of your talk, your research is in visualization systems. Share with us what you shared with the audience today, goals, outcomes, current outcomes of your visualization research. >> Mm-hmm. My research passion is around helping people make sense of complex data. I've particularly done a lot of work with scientists, particularly that in biology, where there's just been this amazing explosion of data and people are just trying to wrap their heads around what they have and what kinds of amazing discoveries they're sitting on. But it's really interesting, we've gotten so good at creating data, but then, that's wonderful, but if you can't make sense of it, who cares? >> Lisa: Right. >> I have this incredibly privileged position where I get to go and work with people who are at the cutting edge of their field and learn about this amazing work that they've been spending their lifetime on. Then I help them, I design tools with them that sometimes changes even the way that they're thinking about the problem. It's incredibly satisfying and it's very much in the spirit of team science and it's a lot of fun. I was talking about just some of the basics behind how do you create effective visualizations, which, for me, it also draws heavily on the notion of how do we collaborate effectively, how do you get at people's deep needs when it comes to making sense of data, when they often times can't articulate it themselves. I refer to it as data counseling, because it feels very much like, I talk with people who have problems but they can't articulate it, so I ask them lots of questions to help them uncover the root of their problems. >> Lisa: Right. >> That's basically what I do. >> That data counseling. That's fantastic. >> Yeah, and then you use what you discover in order to design tools. >> Share with us a little bit about the courses that you teach in Computer Science at the University of Utah. >> Yeah, so I teach a graduate level visualization course. It is just about the basic foundational principles we have behind perception and cognition and what that means for how we encode information, and then also, the process of how do you evaluate visualizations effectively. It's a really wonderful course where we have people from, actually, all across campus, so a lot of people are bringing problems that they have in other fields and trying to learn how to be more effective in their own exploration with visualizations. Then at the undergrad level, I actually teach our second semester programming course, so these things are worlds apart. This is one of our large 200 person introduction to data structures and algorithms. >> OK. What are some of the things that are inspiring? We'll talk about your graduate students for a moment. What are some of the things that you find are inspiring them to want to understand data in this way? Is it because they were kids that grew up in STEM programs, or they just had a computer since the time they were little, or are there other factors that you're finding that are really drivers of them wanting this type of education? >> So the students that I work with directly, I think, kind of fall into two camps. One camp is, they're a sort of non-traditional computer scientist, where they enjoy the engineering, they enjoy the programming, but they also really enjoy people and are passionate about making a difference. They also really enjoy the interaction that we have to go through in trying to understand what someone needs. There's also a design component, it's really fun to get to create things that feel good and look good. That's definitely one class, so it's the sort of non-traditional computer scientist. The other class, I have a couple of students who come from a science background, who love science, but find that they like building things more than they like doing the science itself, and visualization is kind of a wonderful place in the middle where you can be part of science but doing the making and building that we do in computer science, as opposed to doing the sort of experimentation and studying that you do as a scientist. That was definitely, for myself, I have a background in science and that's what really drew me, when I discovered computer science and visualization itself. >> What are some of the traditional skills that a good educated computer scientist needed maybe five years ago, and how are you seeing that change? Are there new behavioral traits or skills that really are going to be essential for these people going forward? >> Yeah, I think especially in the space of data science and remembering that at the end of the pipeline there's a person sitting there either bringing their knowledge to bear or that you're trying to tell a story to you from data. I think one trait is the idea of having empathy and being able to connect with people, and to just understand that as technologists, we're, not all of us, but largely creating technology for people. That's something that I think has traditionally been undervalued and perhaps a little bit filtered out by perceptions of what a computer scientist is. But as technology is becoming more ubiquitous and people are understanding the impact that they could have, I think it is bringing in a different group of people that have different motivations for coming to the field. >> What are some of the, as your graduate students finish their education and go on to different industries, what are some of the industries that you're seeing that they're using their skills in? >> Yeah, so a lot of it is getting hired in companies that, their core product that they develop isn't necessarily a piece of technology. But they're using data now to really understand their business needs and things like that. I have a student right now who's actually at a government organization in DC, working with some amazing global health specialists. But these are midwives and social workers and they don't have the deep skills in data analysis. So there's opportunities for people in visualization and data science to go and really make an impact in a whole variety of interesting fields. That's actually one of the things that I always love to tell undergrads who come to talk to me about, "Oh, should I do computer science?" The thing I love most about it is that, whatever your passion is in life, whether it's medicine or whether it's music, or whether it's skiing, there is a technology problem there. If you have those skill sets, you can go and apply it to anything that you care deeply about. >> I couldn't agree more. That's such an important message to get out. I mean, every company, we're sitting here in Silicon Valley, where car companies are technology companies, every company these days, Walmart is a technology company. I think that's an important message for those kids to understand, following their passion. I don't think that that can be repeated enough, because you're right, whatever it is, there's a technology component to that. With that tip, let me ask you, what were some of your passions when you were younger in school? You mentioned your science degrees. But what were some of the things that really helped or maybe people shape your career and where you are today? >> Yeah, growing up, I was, my dad's a scientist, my mother's an artist, so there's definitely, both of those. >> Lisa: Art and science, so yes. >> Yeah, influences of both, and I really wanted to be an astronaut, but it turns out I get really motion sick. >> Oh, that's a bummer. >> So I had to give up that dream. I studied science, but at the same time, my mom always had me creating and doing things with her in her studio. I think I found this love of just being able to make something and how satisfying that is. I think that was influential. Then also, when I was in college, I was an astronomy major, and I had the opportunity to take lots of electives, which, in hindsight, I think was really important, because it let me explore many things. I found myself taking a lot of women's studies classes. What was interesting about that is just the way that you think and problem solve in a discipline like that where it's all critical analysis. That, sort of coupled with the deep analytics that I was, skills I was learning in physics, made for this just really interesting, I think, multiple, gave me perspectives to look at problems in multiple different ways. I think that that's been really important for being able to bring that suite of perspectives to how we solve problems. It's not all just quantitative, and it's just all qualitative. But it's really a nice mixture of both, if it gets us to good places. >> Absolutely. I think that zigzag career path that you're sounding like you're talking about, I know I had one as well, gives you perspectives that you wouldn't have even thought to seek, had you not been on these trails. >> Mm-hmm. >> I think that's great advice that people that are, whether they're in your classes or they're being able to listen to you here, should be able to know that it's OK to try things. >> Yes. Yes, exactly. I think back to the person I was when I was, say, 18. I didn't know. I think the one sort of constant in my career trajectory has been just, wow, this thing looks really interesting, I don't know where it's going to go, but I'm going to follow that path. Inevitably, if it's something that catches your attention, there's going to be something interesting that can come out of it. I think sort of letting go of this need to have everything defined from day one and instead following your passions is, that's the theme I've heard over and over again from the speakers in here, too. >> Absolutely. Don't be afraid to fail is one of the themes that has come out from this morning. Diane Greene, SVP of Google Cloud, who was in morning keynote, had even said, "Don't be afraid to get fired." I mean, could you imagine your parents saying that to you? >> Yeah. >> I couldn't, but it's also something that just shows you that there is tremendous opportunity in many different disciplines and domains for this type. >> By the way, if you have a technical computer science background, you can always find another job. (laughter) >> That is true. What is next on your plate in terms of research, what are you looking forward to the rest of 2017? >> Wow. >> Lisa: Sorry, was that too big of a question? >> Yeah. We have a couple of really interesting problems around color, around some new tools for helping designers and journalists work with data. I think also, I'm starting to think about trying to focus more on K through 12 education and trying to understand what some of the roadblocks are to getting computer science to a younger community of people. In Utah, we have a lot of rural populations. We also have Native American reservations. I think there's some really interesting challenges with getting computer science into those communities. I'm sort of thinking about working with some folks to try to understand more about that. >> That's fantastic. I mean, you bring up a good point, that kind of depending, then, where you are, here we are sitting at Stanford University, one of the pre-eminent universities in the world, and there's a tremendous amount of technology and resources available. But then you look at, really, the needs of communities in Utah, and they need people like you to help, go, "You know what, we have challenges here, and we need to solve that." Because that's part of the next generation of the people that are here speaking at these types of events. >> Miriah: Right. >> Absolutely critical problem. Well, Miriah, thank you so much for being on the Cube. >> Thank you for the opportunity. >> It's been a pleasure, we wish you the best of luck with your big plans for 2017. >> Thanks. >> Lisa: Hopefully, we'll see you next time. >> Great. >> We thank you for watching the Cube again, Lisa Martin, live at Stanford University at the Women in Data Science Second Annual Conference. Stick around, we've got more, we'll be right back. (electronic music)