>>Welcome everyone to the cubes coverage of women in data science, worldwide conference 2022. I'm Lisa Martin, coming to you live from Stanford university at the Arriaga alumni center. It's great to be back at widths in person, and I'm pleased to welcome fresh from the main stage Tiara Bill's assistant professor at UCLA Tierra. Welcome to the program. >>I'm glad to be here. Thank you for having me. Tell >>Me a little bit about your background. You're a civil engineer and I was telling you, so it was my dad. So I'm, I'm partial to civil engineers, but give our audience an overview of your background, what you studied and all that. Good. >>Yeah. So I'm a civil engineer, um, specifically transportation engineer, um, at UCLA. I also have an appointment in the public policy department. And so, um, I'm split between the two, my work focuses on travel demand modeling and how to use these tools to better inform, uh, and learn more about transportation equity and how to advance transportation equity. Um, and what that means is that we are prioritizing the needs of vulnerable communities, um, in terms of the data that we're using, the models that we're using to guide decision-making, um, in terms of the very projects that we evaluate and ultimately the decisions that we make to invest in certain transportation improvements. How >>Did you get interested in transportation equity? >>Yeah, so I think it, it stems from growing up, uh, in Detroit, some or Detroit born and raised native, and it stems from growing up in an environment where it was very clear that space matters that where you live the most, that you have access to, uh, whether you have a car or not. Um, whether you have flexibility in your, in your travel, it all matters. And it all governs the opportunities that you have access to. So it was very clear to me, um, when I would realize that certain certain kids didn't really leave their neighborhood, you know, they didn't travel about the city, let alone outside of the city and abroad. And so, um, and there are also other, you know, examples of, um, there are examples and cases after case where it's clear that communities are, um, being exposed to a high level of emissions, for example, um, that might result from transportation, but they're not positioned to benefit, um, in the same ways that the people who own the infrastructure on the freight or what have you. So, um, these are all very real experiences that have motivated my interest in transportation equity. >>Interesting. It's something I actually had never thought about, but you bring up a great point. How are talk to me about the travel demand models, how they're relevant and, and where some of the biases are in travel data, >>Right? So travel demand models, they are they're computational tools. They're empirically estimated meaning that their estimated from raw data, um, everything about them is driven by the data that you have access to. And how they're used is in largely in regional transportation planning, when it is necessary for regions to assess 10, maybe 15, 20 years into the future. Um, how is transportation going to change as a result of changes in travel patterns, growth in the population, um, changes and how firms are distributed across the landscape. Um, environmental changes, all sorts of changes that, um, that guide and direct our transportation decisions at an individual level. So regions are assessing these things over time and they need these powerful travel demand models in order to perform those assessments. And then they also, once they have an understanding of what the need is, because for example, they expect traffic congestion to improve, or sorry to increase over time. Um, there needs to be a means of assessing alternatives for mitigating those issues. And so they use the same types of models to understand if we expand highway capacity, if we, uh, build a new form of transit, is that going to mitigate, uh, the challenges that we're going to face in the future >>And travel demand, modeling and equity? What's the connection there? I imagine there's a pretty good >>Deep connection, right? So the connection is that. So we're using these tools to decide on the future of transportation investments and because of a history of understanding that we have around how ignoring the conditions for vulnerable communities, ignoring how, um, uh, transportation decisions might differentially impact different, different groups, different segments. Um, if we ignore that, then it can lead to devastating outcomes. And so I'm citing, um, examples of the construction of the Eisenhower interstate system back in the fifties and sixties, where, uh, we know today that there were millions of black and minority communities that were, uh, displace. Um, they weren't fairly compensated all because of lack of consideration for, for outcomes to these communities and the planning process. And so we are aware that these kinds of things can happen. Um, and because of that, we now have federal regulations that require, uh, equity analysis to occur for any project that's going to leverage federal funding. And so it's, it's tied to our understanding of what can happen when we don't focus on equity is also tied to what the current regulations are, but challenge is that we need better guidance on how to do this, how to perform the equity analysis. What types of improvements are actually going to move the needle and advance us toward a state where we can prioritize the needs of the vulnerable travelers and residents? What >>Excites you about the work that you're doing? >>You know, I, I have a vested interest in seeing conditions improve for, um, for the underdog, if you will, for folks who, um, they, they work hard, but they still struggle, um, for folks who experience discrimination in different forms. Um, and so I have a vested interest in seeing conditions improve for them. And so I'm really excited about, uh, the time that we're in, I'm excited that equity is now at the height of many discussions, um, because it's opening up resources, right? To have, uh, more folks paying attention, more folks, researching more folks, developing methods and processes that will actually help to advance equity, >>Advancing equity. We definitely need that. And you're right. There's, there's good V visibility on it right now. And let's take advantage of that for the good things that can come out of it. Talk to me a little bit about what you talked about in your talk earlier today here at widths. >>Right? So today I got a chance to elaborate on how travel demand models can end up, um, uh, with, with issues of bias and under-representation, and it's tied to a number of things, but one of them is the data that reusing, because these are, uh, empirically estimated tools. They take their form, they take their, uh, significance. Everything about them is shaped by the data that we use. Um, and at the same time, we are aware that vulnerable communities are more prone to issues that contribute to data bias. And under-representation so issues, for example, like non-response, um, issues like coverage bias with means that, um, certain groups are for whatever reason, not in the sample link frame. Um, and so, because we know that these types of errors are more prevalent for vulnerable communities, it brings, uh, it raises questions about, um, the quality of the decisions that come out of these models that we estimate based on these data. >>And so I'm interested in weaving these parts together. Um, and part of it has to do with understanding the conditions that, um, that underlie the data. So what do I mean by conditions? I gave an example of, uh, cases where there is discrimination and as evidenced by the data that we have available as evidenced, uh, for example, by examining, um, the quality of service across racial groups, um, using Uber and Lyft, right? So we have information that, that, that presents this to us, but that information is still outside of what we typically use to estimate travel, demand models. That information is not being used to understand the context under which people are making decisions. It's not being used to better understand the constraints that people are facing when they're making, uh, decisions. And so what is the connection that means that we are using data, um, that does not will capture the target group. >>People who are low income, elderly, um, transit dependent, uh, we're not capturing these groups very well because of the prevalence of, of various types of survey bias. Um, and it is shaping our models in unknown ways. And so my group is really trying to make that connection between, okay, how do we collect Bader, better data, first of all, but second, what does that mean? What are the ramifications for prediction, accuracy for VR, for various groups, and then beyond that, what are the policy implications? Right. Um, I think that the risk is that we might be making wrong decisions, right? We might be assuming that, uh, certain types of improvements are actually going to improve quality of service for vulnerable communities when they actually don't. Right. Um, and so that's the worry and that's part of the unknown, and that's why I'm working in this >>Part of the anonymity. Also, I'm sure part of your passion and your interest international women's day is tomorrow. And the theme this year is break the bias of breaking the bias with >>Mercy back >>To travel equity. Where do you think we are on, on being able to start mitigating some of the biases that you've talked about? >>I think that it's all about phasing. I think that there are things that we can do now, right? And so, um, at the point of making decisions, um, we can view the results that we have through this lens, that it might be an incomplete picture. We can view it through a historical lens. We can also view it, um, using emerging data that allows for us to explore some of these constraints that, you know, might be exogenous to the models or X, not in, not included in how we estimate the models. Um, and so that's one thing that we can do in practice is okay. We already know that there are some challenges let's view this from a different lens, as opposed to assuming that it's giving us the complete picture. Right. Um, and that's kind of been my theme, uh, today is that, you know, as decision-makers, as analysts, as data scientists, as researchers, we do have tendency of assuming that the data that we have, the results that we have is giving us the complete picture when we know, but it's not, we know that we act as if it is, but we know that it's not right. >>So, you know, we need to, there's a lot of learning and changing of behaviors, um, that that has to happen. >>Changing behaviors is challenging. >>It is behavior changes is tough, but it's necessary, but it's necessary. It's necessary. And it's urgent. And it's critical, especially if you're going to, uh, improve conditions for vulnerable community. >>What are some of the things that excite you, that looking at where we are now, we've got a nice visibility on equity. There, there's the conscious understanding of the bias and data and the work to help to mitigate that. What are some of the things that excite you about what you're doing and maybe even some of the policies that you think should be enacted as a result of more encompassing datasets? >>It's a good question. Um, one thing I will say is what excites me is it's also tied to the emerging data that we have available. So I'm trying to go back to an example that I gave about measuring constraints. Think that we can now do that in interesting ways, because we're collecting data about everything we're collecting data about, um, not just about where we travel, but how we travel, why we travel. Um, you know, we, we collect information on who we're traveling with, you know, so there's a lot more information that we can make use of, um, in particular to understand constraints. So it's, it's really exciting to me. And when I say that again, um, talking about, um, how would we make a choice to take a certain mode of transportation or to leave our house at a certain time in the morning to, to get to work. >>Um, we're making that under some conditions, right? Right. And those conditions aren't always observed and traditional data sets. I think now we're at a time where emerging data sources can start to capture some of that. And so we can ask questions that we weren't able to, or answer questions that we weren't able to answer before. And the reason why it's important in the modeling is because in the models, you have this sort of choice driven side and you have the alternatives. So you're making a choice amongst some set of alternatives. We model the choices and we spend a lot of time and pay a lot of attention to the decision process. And what factors goes into making the choice, assuming that everyone really has the same set of universal choices. Right. I think that we need to take a little, pay a little more intention, um, to understanding the constraints that people have, um, and how that guides the overall outcomes. Right? So, so that's what I'm excited about. I mean, it's basically leveraging the new data in new ways that we weren't able to before >>Leveraging the data in new ways. Love it. Tierra, thank you for joining me, talking about transportation equity, what you're doing there, the opportunities and kind of where we are on that road. If you will. Thank you so much for having me, my pleasure. I'm Lisa Martin. You're watching the cubes coverage of women in data science conference, 2022. We'll be right back with our next guest.

>> from Palo Alto, California It's the Cube covering the em where women transforming technology twenty nineteen Brought to You by V. M. >> Where >> Hi, Lisa Martin on the ground of'Em were in Palo Alto, California, at the fourth Annual Women Transforming Technology Event, or W T. Squared one of my absolute favorite events to cover. And I'm pleased to welcome from one of the sponsors, Jennifer Cohen, the vice president of operations at Toyota Research Institute. Welcome to the Cube. >> Thank you, is that I'm really excited to be here to >> This is such a great event. It's It's morning time. You and I both have a lot of energy coming from even before you walk into the keynote here. Collaboration. The positive spirit, the energy, all of these women talking about and menas well past experiences. It's you walk in, and the energy of Deputy squared is palpable. This is your fourth year. So you being here now at all four >> have, and that's why I keep coming back because the energy here is so good because every year I walk away with tips I can use at work and in my personal life, championing diversity >> and diversity inclusion one of the tracks here, as well as trucks like helping emerging leadership the younger generation, which is key because the attrition rates in technology are so, so high. Tell me a little bit about Tech Toyota Research Institute, Terra What you guys doing? And what made it important for tea Right to sponsor W T Square this year. So Toyota Research >> Institute is a subsidiary of China. We're working on a really exciting things like autonomous driving robotics to help elders, agent place and material sciences. So it's really exciting next level stuff. And it's thrilling to kind of coming to work every day on things that we've been hearing about in the world. And now they're real world things, not just the Jetsons, you know? Yes. >> And so you were here as I mentioned the last three years. But last year, uh, when you were here, you were saying a minute ago. You leave this event every year with really useful kind of we'LL put it into tech terms act personal insights, absolutely clueless about your conversations at Tier I that where they said yes, this is an important event for us to >> sponsor, absolutely so that when I When I came back last year, I had brought a couple of folks from T. Ry to attend the event because I've been attending since the beginning. And as I said, every year I find something that I can bring back to the teams, if not multiple things. Andi weaken our chief diversity officer, Our senior chief of staff is also our diversity inclusion Head. She was very passionate about also supportive event. We're involved with Grace Hopper. We have a women's employee resource group. We're really putting our efforts our time here. They were glad to sponsor. And what was so exciting to walk into that room full of energy today and to see t rise logo up there? It was amazing. >> And I'm sure that for that you mentioned that there's about twelve of your your folks that are here that probably feel it's great that you're not just it's not just a logo. Now, this isn't just branding. This is actual. We're here, You're here. It's a focused, concerted effort. That tiara has an in fact when you join Tiara on the last couple of years, one of the things that inspired you was there's a Chena female leadership here, which is not >> common. No, it's definitely not definite, not common in my career. So one of the reasons I started at here I was because of my manager. Who's her name is Kelly K. She's our EVP and CFO, and she's an amazing leader and so on having the opportunity to go to another company. I wanted to go to one that makes a difference. Like tea, right? Look working to improve the quality of human life. And I wanted to work for somebody that I really respect. It could learn from on. It's been pretty rare in my career tohave women, female leaders to report to. So it's been amazing. And that, I think shows in the role that I have the role, that our chief of staff has Kelly's role and the fact that we're here today. It all flows through. >> So talking. Let's talk about more about flow as VP of operations tell me, like, for example, last year's W T squared what were some of the learnings that you brought back and used in your team, whether it's your management style or even hiring the next generation, >> so a few things that I've learned and not all of them are from last year. I'LL be honest. I'm not. All of them are ones I've just up like at you write. But some of them are things about management. Patty Vargas was here a couple years ago, talking about winds and challenges and really highlighting wins and every team meeting that something that it took back. And it well, it's not necessarily diversity. It's been transformational for me as a leader and really helpful to my team's. Then something. Other things I learned were about on, especially in a few years ago, about saying tohr, I'm not accepting any candidates until you have a diverse candidate pool. That's made a really big difference. And it's hard to say it's hard to stick with because it is hard to find women in technology. However, sticking with that has really helped in my career, hiring folks to have a more diverse team, >> so sticking with it, you've been in a technology for a long time. Tell me a little bit about your career path where you stem from the time you were a kid knowing I love computer science, or was it more zigzag ee >> Ah, little's exactly I was actually history, major say, But I always love technology. Back when we had trs eighties, I love technology. And so I actually started doing that to put myself through school, and I loved it so much. It's what I've stopped what's happened in technology for twenty five years, starting as health desk and systems administrator and moving my way up in my career over time, and every so often they still let me touch something technology and a firewall or some of my best. I keep a little bit of that skill set, but it is quarter who I am, and it's quarter Why I made it. Twenty five years sets >> a milestone. Congratulations, by >> the way, twenty five years in any industry that techno technology industry. I was reading some reports the other day upwards of forty five percent contrition, which is higher than any other industry. What have been some of the secrets to your Obviously I'm imagining persistence, but twenty five years is a long time to stick with anything, but you clearly have a passion for this, but I'm sure it hasn't been easy. Give us a little bit of an understanding and maybe some of those more challenging times you encountered. And how did you just kind of with that internal rules also know I'm I like technology. This is what I wanted. >> So, you know, it's always tough being the only woman in a room that's happened the bulk of my career, although thankfully, not a tear I but it has happened across and actually was the only woman at one company, and I thought it was gonna be a great opportunity. And I love the technology that we were doing. And I was excited Teo to infrastructure in operations and support it. And it was really a bad experience. And it wasn't imagine purposeful, but it was not great. And I was there a very short period time when I realized it wasn't gonna work and I had to take a real hard look. Don't want to keep doing this for a living. I do. I don't want to give up technology. So the right thing was to give up that company, right? And the right thing was t make sure that I stayed and what I loved, but not in the wrong spot. So I think being stubborn and persistent. Not being willing to give up the stuff that I love because the environment wasn't right was a huge part of why I have made it this far. And my daughter is a computer science major, and so I really want for her not to have to go through those things apart. The reason I come here today, what I'm excited about W T two is I want to make sure she has a far easier time of it than I had growing up. >> So was your daughter always >> an interested Or did she? Is she kind of following in Mom's footsteps? She >> wasn't the beginning. Actually, she don't want anything to do with it. And my mom's a c P A. And I don't want to do anything to find >> a way. >> So maybe a cool and her uncle, but never the parent, >> exactly. But as she took coding classes, she actually did Girls who code the seven week immersion camp she found like me that she loves it. So I think she'd like to not compare it to Mom. She doesn't want to hear Mom wars, but she absolutely has that same passion. She she loves to code and see the output and see the changes it can make in her life and potentially others. >> So she'd underground. Currently she is. You should give you anything back on the diversity in her. Yes, is she >> does. And I wish I could give you something inspiring. But unfortunately, she it's for four girls to forty guys. >> Okay, so maybe she has that. Maybe it's a DNA thing where she has that some people might say Stubbornness bad. However, I think you're a great example of how that can be, you know, sort of flipped that coin and look at it is persistence. What keeps her saying, I don't care that I'm for forty? >> I'm not sure. I think e think it's similarly the same thing that it's she's passing around and also she's had everybody's in lovely to her. She's had no mistreatment, so she's definitely loving it, but does notice that she's one of, you know, four out of forty. So but would you >> would you advise? And I, I know not like to say the next generation like your daughter's generation, but it's It's the generation of US women who are in technology now with the attrition rates. If they're in a situation, how would you advise him to recognize the experience that you shared with us? That this is situational? This is an industry wide. I'm not going to make a generalization. What would your advice be to them in terms of making that decision to not not leave? >> So I would say, actually, a mentor of mine told me when I was years ago at a company says, Do you like the work or do you do not like the work? Do you like the people do not like the people. If you don't like the people, you need to go somewhere else. But if you like the war, if you don't like the work here in the wrong industry and I like the work and I always have So I would say if you'd like the work, find the right opportunity and see what change you, Khun, doing the company that you're at. If you're at a company and things aren't right, have you to talk to a man in your manager HR there's ways tto see if you could fix it and if you can't, it's okay. Go somewhere else and do what you love. >> I love that it is. Okay, So one of the things that I'd loved digging on as well as you had gone to Terry's a HR and said, I'm not going to be looking at any candidates until you actually did >> a previous companies. But that is my stance since then, >> you know, >> it's without a diverse school, >> okay? And so what is diverse mean to you? What do you say to them? I know you can find us. >> Yes, Well, I diverse. I don't I don't want to dictate it. I just don't wanna have to, you know, the team's all be the same person. I think Joy is talking up the keynote right now about how important it is that we be careful of bias and that we look at those things and that we are having the people who build the technology be well rounded because this technology that's built here in the Valley goes all over the world has to serve everyone, not just the folks who build it. So I think it's having that same mindset going into it, goingto hiring >> one of and that's so important. And there's also debated. Is it a pipeline problem? I just read Emily changed Look proto Pia and where she kind of documents where that pipeline problem was created? Yes, many, many, many decades ago. And a lot of people would say it's a pipeline problem. But the majorities, the underrepresented, which isn't just women and people of absolutely well who say it's not a piper and problem this. And even if we look at a I, there's so many exciting possibilities. All the autonomous vehicle weren't that tear eyes doing, for example, that will impact everybody and jurors facial recognition? You know, there's probably people in the baby boomer, a generation that have iPhones with facial recognition. But the things that joy wish areas about the bias Easter thes malls being trained on, really, it gives me goose bumps. Didn't mind blowing more. People need to understand. We need better data and more diverse data, not just that to train the models to recognize more agree, but there needs to be lots of different, uh, data sets. So this inclusiveness and I think of diversity, inclusion. One of the things that I thought of when Joy was talking about inclusivity is its inclusivity of different data sets and different technologies, so that ultimately going forward, we can start reducing these biases and this technology that is all for good. >> And I think one of things that we've done is, you know, for our company, we actually had on all hands doing unconscious bias training like we are absolutely committed to making sure that we're thinking about those things on the idea if it's pipeline or if it's or or if it's not, I think it's a combination because the fact is, my daughter is in a class with four girls in forty men, and that's not necessarily, you know, there's no judgment there, but that's the reality. So there's pipeline. But I also think we can demand is hiring managers to have a diverse pool come to us? University isn't just I speak to women because that's what you know. That's my story. But there's not. There's, You know, we had those other kinds of diversity inclusion, you know, we have our G d l G B T. Q plus energy starts a lot of letters to get out at once. We have our women than allies. Yogi Employee resource Scripts were supporting that. It's here, I But I think, you know, we see people out there in the world all trying toe push forward on this. I think if we come out of these conferences and take those actions, that's how overtime it's going to get better. So that's my personal thought. >> I love that last question. What are you looking forward to? Taking away from Debbie U T squared for inclusive innovators as the >> well being of a company doing innovation? I'm really curious to see what's presented today, and I know that we've heard studies that talk about women, run companies and with women on board that profitability and innovation go up. So I think that the more inclusive we are, the better. All of our technology that comes out of the Valley is going to be so I'm looking forward to the whatever thought leadership is here today. That's different from each year that there's something different here that I learned it's not the same thing was Pipelines four years ago, right? Like the last year. It was a lot about women's leadership, so I'm really excited to see what comes out today. >> Well, Jennifer, I thank you so much for sharing some of your time on the kid with me today. And I think a lot of people are going to be able to learn a lot from us. Well, we appreciate your time. Thank you. My pleasure. Lisa Martin on the ground with the Cube. Thanks. For what?