>> Live from Orlando, Florida, it's theCUBE covering Grace Hopper's Celebration of women in computing. Brought to you by SiliconANGLE Media. >> Hey welcome back everybody. Jeff Frick here at theCUBE. We're at the Grace Hopper Celebration of Women in Computing, the best name in tech conferences. 18,000 women here in Orlando, filling up the Orange County Conference Center. We're excited to be here for our fourth year, and part of the whole program is getting some of the leadership from AnitaB.org on to give us an update and we're really excited to have Elizabeth Ames. She's the SVP of Marketing and Alliances and Programs but we just think of her as Elizabeth at AnitaB.org. So, Elizabeth, great to see you. >> Great to be here. >> Absolutely >> We're thrilled to have you here at the Celebration. >> I can't believe it's been four years. I've been telling so many people. There are still so many people that have never been here. I was amazed at the keynote, the first day, there was the call, the houselights went up, how many people it's their first time, and as big as this conference is, as much the people that know it love it, there's still a lot of people that have not been exposed to this show. >> It's absolutely the case. We have every year it seems like more and more sort of first timers. Which is great because we love to have them come but we'd love to have them come back. I think it's really an expression of how this issue has become a big issue and that the women are really engaged and excited and they want to be a part of it, so it's great. >> The other thing I don't think a lot of people know is there's obviously a lot of recruiting going on, there's a lot of young people here which is really what I think gives it its flavor, but we had Workday on. They said they had 140 people here from Workday. I talked to a guy last night at dinner from Google, I think they had 180 people and I said to her, "Do you have any show "that you bring that many people to "that's not your own show, so the amount of investment" And then I said, it's all young, fresh out of school No, it's all ranges, all ages. So again, I think there's a lot going on here that people are just not that exposed to. >> Yeah, that's absolutely true. So, if you look at our attendance overall, about 70% are industry and a lot of those are companies that are bringing their women and some of them are their younger women who have maybe been in the firm, in the company for a year or two or three or something like that, but the place where a lot of women drop out of the industry is more mid-career and so I think more and more companies are seeing this as a way to help their mid-career women recommit to the field and make those connections with the community at large and get a little bit more reinvigorated so we definitely see companies bringing all kinds of women out of their organization, and they like to bring a mix, so that they have some of their senior women that are sort of mentoring women who are mid-career or women who are more junior and it just gives them a really good mix. And then about 30% of our attendees are academic, we call it academic, but it's primarily students, so undergraduate, graduate, post doc, and research type people, and then some amount of professors and teaching assistants, those types of people. >> Yeah, and I really think it's the youth that give this show its special vibe. I mean there's a lot of great keynotes and some fantastic stories and really great global representation, a ton of African representation. But I do think it's the youth, it's the youngsters that bring a really unique and positive energy that you don't really see at many other conferences. >> Yeah, and I think part of that is that the community at large, you know women that are in the field they care about the women coming up and they want them to succeed and they want them to have every single opportunity so everybody's kind of invested in them and interested in nurturing and helping them along. So it does create this really, I don't know, positive environment, right. We always jokingly say there's a reason we call it a celebration. We don't call it a conference, we call it a celebration. >> Everyone's a delegate too. I like that too. It's not attendees. And that's come up on a number of interviews too where when people have reflected back on people that have helped them along the way the payback, it's almost like it's been scripted is, OK, now you need to do this to the next person to really pay it forward and that again is a consistent theme that we have also heard from the keynotes earlier today, that it is about paying it forward, which is funny because sometimes you'll hear kind of a catty women reputation that they're trying to keep each other down, you know that that was kind of a classic, another hurdle that women had to face in the professional world that they weren't necessarily supporting each other, and that is not the case here, at all. It's very much a supportive environment. >> We may have a self selection bias going on here >> Well that's okay >> But I think there's nothing but support for one another in the community and everybody recognizes that we all have to pull together. >> Right. So interesting times at AnitaB.org, the organization that puts on Grace Hopper, change of leadership, we had Brenda on, so kind of a fresh face, fresh energy. Telle. I'm going to see if I can get her a horse tomorrow to ride off into the sunset if the sun breaks out here in Orlando, so it's exciting times. It's a time of transition, always a little kind of mixed feelings, but also tremendous excitement and kind of new chapter, if you will. So tell us a little bit about what's going on at AnitaB.org >> It's an incredibly exciting time. First of all, a nod to Telle. She's been at the helm for 15 years. She's seen an incredible amount of growth. She took this on really as a favor to her dear dear friend and then took on the mantle upon Anita's death. She's done an amazing job. She's certainly an icon within the community overall I'm sure you'll hear more from her in the future. It's been great. Brenda is new fresh face. She has accomplished some pretty amazing things with the Chicago Public Schools. She's really invigorated to step into this space and it's great having her. I think the thing that you really, hopefully you got from her when she was here is that she is just this incredibly genuine person. She's lived the experience. She can relate to what all of these women have gone through. She has this profound commitment to make things different. And just the biggest heart that you could possibly imagine. >> Right, and a little chip on her shoulder. Which she talked about and it's come up time and time again where when people are told they can't do things for a lot of people, there's no greater motivator than being told you can't do this, you shouldn't do this, you're not qualified. She said "I've been in positions "where I've been told I can't be there." So to have that little chip on her shoulder I think is a real driver for many folks. >> It is. We recently did a little written piece it hasn't actually gotten published yet where we kind of went back and looked at a lot of the language that we're hearing today about women are not biologically suited to be programmers or women aren't this or women aren't that. And we did this little let's look back historically, and when did women get certain rights, and one of the things that really stood out for us in looking at that was women weren't admitted to all of the premier colleges, Harvard, Yale, whatever, until the 1960s. Which is kind of shocking when you think about it. >> Yeah, it's like yesterday practically. >> The language that was used at the time was almost identical to the language that we're hearing today. Women weren't biologically suited for this, it's really not in the right makeup for them. And yet today, half the students at those schools are women. And women have earned their way there. I just kind of laughingly say it's like deja vu all over again. We've heard all of that. we've heard people tell us you can't do that, you shouldn't do that, no you're not welcome and I think women they're not going to back down. >> It's interesting times too, because the classic gates, the distribution gate, the financing gate, the investment gate, to build companies, to create companies, they've all been broken down and kudos or serendipitously computing is the vehicle that's broken down a lot of those traditional barriers. You used to be, you couldn't start a new company because you had to get into distribution. You couldn't be a writer, there was only a few newspaper editors that controlled everything. That's all completely changed and now ubiquitous distribution, democratization of software, open source, you don't have to raise a bunch of money and buy a bunch of servers. It's so much easier to go out and affect the world and there's no easier way to affect the world than writing a great piece of software. >> Yeah, I think you're spot on on that. There's so much more leverage out there for people that want to start something. I believe that will accrue to the advantage of women. I always end up saying women are going to do great things and then I have to stop myself and say they are doing great things today. I think we've seen that already with some of the keynotes. Fei-Fei Li, and yet you hear her story as an immigrant and as a mother, as an Asian woman. She's had her challenges and she told her personal story not like with a woe is me but with a clear eye towards the things that she had to overcome to get where she was. >> And a lot of hard work, just a flat out a lot of hard work including working at the dry cleaners while she was going to school. >> Yeah, exactly. And yet there she is, one of the leaders in that space and doing incredible things. So I think you're starting to hear more and more about those women. I think they've always been there. I think that we just don't hear as much about them. So, this venue is such a great opportunity for us to hear more of their stories. >> Right, and we learned a lot about that last year with the whole Hidden Figures thing that we had on here as well as the movie and that was again, in the 60's. So we're in October, it's kind of the end the year. As you look forward to 2018, what are some of your priorities for AnitaB.org? I won't put you on the hook to tell us where Grace Hopper will be next year. You can tell us if you want. >> I saw it posted at Pride someplace. >> Is it posted already? >> I saw that and it was like whoa, I didn't know that was in the wild yet. >> But give us kind of a look. What are your priorities for next year? I know AVI Local has been a thing that's been growing over time. What are you kind of looking at as you're doing your 2018 planning? >> As amazing as it is to have 18,000 people here, which just blows our mind, we hope it continues to grow. We also know that no matter how big this conference gets that not everyone will be able to come here for a variety of reasons and so building out the local communities and making it so that, empowering those local communities to have smaller versions of this type of thing and growing this movement to a bigger scale that really encompasses all the women that are out there because even though people here say "Oh, 18,000 women, holy cow" it's a tip of the iceberg. There are thousands and thousands more women out there, we know there are. We really want to find a way to reach every single one of them and bring support and connection and inspiration to every single one of them so that they stay in the field, can achieve their dreams and their highest potential. That will have an impact on them and on the communities they live in. That's really what our focus is. >> Well, Elizabeth, again. Always great to see you. Congratulations on a phenomenal conference. And thank for inviting us to be here. It's really, honestly, one of our favorite places to be. >> We love having you here. I would just end by saying all you people out there, come join us next year. >> There you go. Are you going to tell them where? >> Houston, Texas. >> In Houston. - Back in Houston. >> Good barbecue. Ask me, I'll tell you where to go. Alright, she's Elizabeth Ames. I'm Jeff Frick. You're watching theCUBE from the Grace Hopper Celebration of Women in Computing 2017. Thanks for watching. [Upbeat Techno Music]

[Techno Music] >> Narrator: Live, from Orlando, Florida it's the Cube covering Grace Hopper's celebration of women in computing. Brought to you by SiliconANGLE Media >> Hey welcome back everybody, Jeff Frick here with the Cube. We're at the Grace Hopper Celebration of women in computing 2017, 18,000 women and men here at the Orlando Convention Center it gets bigger and bigger every year and we're really excited to have our next guest, the soon-to-be looking for a new job, and former CEO but still employed by AnitaB.org, Telle Whitney, the founder of this fantastic organization and really, the force behind turning it from, as you said, an okay non-profit to really a force. >> Yes So Telle, as always, fantastic to see you. >> Oh it's great to see you, glad to welcome you back and glad to have you here. >> Yes, thank you. So, interesting times, so you're going to be stepping down at the end of the year, you've passed the baton to Brenda. So as you kind of look back, get a moment to reflect, which I guess you can't do until January, they're still working you, what an unbelievable legacy, what an unbelievable baton that you are passing on for Brenda's stewardship for the next chapter. >> Yes, I mean, I've been CEO for the last 15 years and under that time period, we've grown into a global force with impact, well over 700,000 people. We have well over 100,000 people who participated with the Grace Hopper or the Grace Hopper India. It's grown, and what's been really exciting the last few days, is hearing the stories. >> Jeff: Right, right. >> Of how, the impact that this, the AnitaB.org has had on the lives of young women but also mid-career and senior executives. It's very inspiring to me. >> It is, it's fantastic, and I think the mid-career and more senior executive part of the story isn't as well-known, and we've talked to, Work Day was here, I think they said they had 140 people I think I talked to Google, I think they had like 180. And I asked them, I said, is there any other show, besides your own, that you bring that many people to from the company for their own professional development, and growth. And there's nothing like it. >> That's true. The reason why the Grace Hopper celebration has grown as significantly as it has is because more and more organizations, companies, bring a large part of their workforce. I mean, there are some companies that have brought up to 800 people, and sometimes even 1,000. >> Jeff: Wow >> And there's a reason why, because they see the impact that the conference has on retention and advancement of the women who work for them. >> And that's really a growing and increasing important part of the conversation, >> It is. >> Is retention, and two, getting the women who maybe left to have a baby, or talk about military veterans getting back in, so there's a whole group of people kind of outside of the traditional took my four years of college, I got a CS degree, now I need a job, that are also leveraging the benefits of this conference to make that way back in to tech. So important now as autonomous vehicles are coming on board and all these other things that are going to displace a bunch of traditional jobs. The message here is, you can actually get into CS later in life and find a successful career. >> Yes, we have a real diversity of attendees. So about a third of them are students, and that's really, they're brought here by their universities because that makes a difference. We have a great group from the government. So there's this real effort to bring state-of-the-art technology into our government, initially spearheaded by Megan Smith but really has grown. And the government brought quite a few women. And yes, we do have re-entry people. The companies are looking for women who are very interested in getting back in the workforce. The wonder about our profession, is that they're in desperate need of talented computer scientists. And so, because of that, more and more organizations are being innovative in how they reach out to different audiences. >> And outside of you, I don't know that anyone is more enthusiastic about this conference than Megan Smith. >> Yeah (laughs) >> She is a force of nature. We saw her last year, we were fortunate to get her on the Cube this year, which was really exciting. And she just brings so much energy. We're seeing so much activity on the government side, regardless of your partisanship, of using cloud, using new technology, and that's really driving, again, more innovation, more computing, and demand for more great people. >> Yes, we're very blessed that Megan has continued to come here every year. She came back this year, she sat on the main stage, and she has really been, her message to so many of the young women is that, consider government technology as something you do, at least for a while. And I think that that's a very important message if you think about how that impacts our lives. >> Right, for the good. >> Telle: Yes. >> And that was a big part of her message, she went through a classic legal resume, and some other classic resumes where you have that chapter in your career where you do go into government and you do make a contribution to something a little bit bigger than potentially your regular job. It does strike me though, how technology and software engineering specifically is such an unbelievable vehicle in which to change the world. The traditional barriers of distribution, access to capital, the amount of funding that you used to have to have to build a company, all those things are gone now through cloud, and the internet, and now you can write software and change the world pretty easily. >> Yes. Technology has the possibility of being equal access for anybody. Open-source, anybody can start to code through open-source. There are many ways for anybody, but particularly women to get back in. But I also like to think about many of the companies here who bring their diversity, they bring their senior executives, they bring this large number of women and they create this view across the entire company of how to create a company that's impactful as well as, you know, developing the products that they are invested in. >> Jeff: Right. >> I mean you can have impact in many different ways, through companies, through non-profits, through government, through many different ways. >> Right, and not only the diversity of the people, but one of the other things we love about this show is the diversity of the companies that are here. Like you said, as government, as I look out there's industrial equipment companies, there's entertainment companies, MLB is right across from us and has been there the three days. So it's really a fantastic display of this kind of horizontal impact of technology, and then of course, as we know, it does make better business to have diversity in teams. It's not about doing just the right thing, it's actually about having better bottom-line impact and better bottom-line results. And that's been proven time and time again. >> Well yes, and, so what I know is that every company is a technology company. If you think about the entire banking industry, they have this huge technology workforce. Certainly classic technology companies have a lot of engineers, but insurance, and banking, and almost anything. I mean, we have a lot increasing amount of retail, Target, Best Buy, places like that. >> Right. Okay so I tried to order in a horse so you could ride off into the sunset at the end of this interview, but they wouldn't let me get it through security. >> Okay >> But before I let you go, I'd just love to get your thoughts on Brenda, and the passing of the baton. How did you find her, what are some of the things that you feel comfortable, feel good about, beyond comfortable, to give her the mantle, the baton, if you will, for the next chapter of AnitaB.org? >> I've been very blessed to lead this organization for 15 years, and this is my baby. But there is nothing more heart-warming than to be able to talk to a visionary leader like Brenda. Brenda is extraordinary. She really believes in computer science for all. She believes that all women should be at the table creating technologies. She has a vision of where she wants to take it and yes, she just started last Sunday, so we have to give her a little time. (laughs) >> You were right into the deep end right? Swim! (laughs) >> But she is just, I mean, I just feel very blessed to have Brenda in my life and I will be there in any way that she needs for me to be there to work with her. But she is going to be a great leader. >> Oh absolutely. Well Telle as always, great, and as you said, you're more busy than maybe you expected to be here, so to find a few minutes to stop by the Cube again, thank you for inviting us to be here. It is really one of our favorite places to be every year. Finally my youngest daughter turns 18 next year, so I can bring her too. And congratulations for everything you've accomplished. >> I love to be here, thank you for coming. Glad we could talk. >> Alright, she's Telle Whitney, I'm Jeff Frick, if you're looking for a highly-qualified woman in tech, she might be on the market in 2018. (Telle laughs) Give me a call, I'll set you up. Alright, you're watching the Cube, from the Grace Hopper Celebration of women in computing. Thanks for watching. (techno music)

(upbeat music) >> Welcome back to The Cubes 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 Freytag with me, who is a data journalism master student at Stanford. We have yet another inspiring woman in technology to bring to you today. Kelly Hoang joins us, data scientist at Gilead. It's so great to have you, Kelly. >> Hi, 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, I completed my PhD in information sciences from University of Illinois 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 snow here. >> Kelly: That's true. >> We proved that the last... And data science can show us all the climate change that's going on here. >> That's true. That's the topic of the data fund this year, right? To understand the changes in the climate. >> Yeah. 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 grew up in a family in the STEM environment. My dad actually was a professor in computer science. So I remember when I was at a very young age, I already see like datas, all of these computer science concepts. So grew up to be a data scientist is always something like in my mind. >> You aspired to be. >> Yes. >> I love that. >> So I consider myself in a lucky place in that way. But also, like during this journey to become a data scientist you need to navigate yourself too, right? Like you have this 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? But I'm happy that I'm end up here today and where I am right now. >> Oh, we're happy to have 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 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. And then when I graduated, I got my first job in Gilead Science. Is super, super close and super relevant to what my research at school. And at Gilead, I am working in the 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 how to use AI to accelerate the precision medicine. So yes, it's something very like... I'm very lucky to get the first job that 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. >> Kelly: Right. >> We know there's (crosstalk) Yes. >> Kelly: In healthcare. >> Exactly. Exactly. Equities in healthcare, equities in so many things. Talk a little bit about what excites you about AI, what you're doing at Gilead to really influence... I mean this, we're talking about something that's influencing life and death situations. >> Kelly: Right. >> How 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 nowaday, we have a tremendous opportunities to fulfill the dream of precision medicine. And what I mean by precision medicines, means now the treatments for people can be really tailored to individual patients depending on their own like characteristic or demographic or whatever. And nature language processing and machine learning, and 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 that's why our group was established. Actually our department, advanced analytic department in Gilead is pretty new. We established our department last year. >> Oh wow. >> But 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 tailor the treatment to individuals. And give everyone better care. >> 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 have direct impact to human health. So everything need to be... Whatever model, machine learning model that you are building, developing you need to be precise. It need to be evaluated properly before like using as a product, apply into the real practice. So it's not like recommendation systems 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 like medical data and health data is very sensitive. And how you at Gilead, you know, try to protect this data to protect like the human beings, you know, 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. We at Gilead, for sure, in data science organization we have like 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 dataset, it's not that we have like direct information about patient at like very granular level. Everything is need to be kind of like anonymized at some points to protect patient privacy. So we do have rules, policies 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. Like I listened to all of them. >> Isn't it fantastic? >> Yes, yes. Especially hearing these women from different backgrounds, at 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 >> You just kind of go... It's very... Yours is linear, but you're kind of the exception. >> Yeah, this is why I consider my case as I was lucky to grow up in 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 pa... Sorry, my bachelor degree in Vietnam, in STEM and 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 kept my minority in that areas, right? So at some point I asked myself like, "Huh, I don't know. Is this really my careers." It seems that others, like male people or students, they did better than me. But then you kind of like, I always have this passion of datas. 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. >> 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 woman working in data science in general, I always try to find a good story from data. Like it's not, when you have a data set, well it's important for you to come up with methodologies, what are you going to do with the dataset? But I think it's even more important to kind of like getting the context of the dataset. Like think about it like what is the story behind this dataset? What is the thing that you can get out of it and what is the meaning behind? How can we use it to help use it in a useful way. To have in some certain use case. So I always have that like curiosity and encouragement in myself. Like every time someone handed me a data set, I always think about that. So it's helped me to like build up this kind of like passion for me. And then yeah. And then become a data scientist. >> So you had that internal drive. I think it's in your DNA as well. When you were one of five. You were 5% 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 in 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 down to that you need to follow your passion. Like in any kind of job, not only like in data science right? 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 really passionate about something, regardless how difficult it is, like regardless like you have 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 leading to. I guess that's my advice. >> Kind of like following your own North Star, right? Is what you're suggesting. >> Yeah. >> 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 popped into my mind is my current manager. 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, 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 inspire me a lot, like someone I look 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. >> It's great. Yeah, it's great. And then you see how fascinating that, how things shift right? And now today we are here in a conference that all are women in data science. >> Yeah. >> It's extraordinary. >> So this year we're fortunate to have WIDS coincide this year with the actual International Women's Day, March 8th which is so exciting. Which 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. Inclusion actually is one of the five core values of Gilead. >> Nice. >> So by that, we means we try to create and creating a working environment that all of the differences are valued. Like regardless 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 cultural company policy and practice. So yeah, I'm very lucky to work in the 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 year Gilead is a sponsor of the WIDS Conference. And we are so excited to establish this relationship, and looking forward to like having more collaboration with WIDS 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 Freytag 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.

(energetic music) >> Everyone, welcome back to theCUBE's coverage of WiDS 2023. This is the eighth annual Women in Data Science Conference. As you know, WiDS is not just a conference or an event, it's a movement. This is going to include over 100,000 people in the next year WiDS 2023 in 200-plus countries. It is such a powerful movement. If you've had a chance to be part of the Livestream or even be here in person with us at Stanford University, you know what I'm talking about. This is Lisa Martin. I have had the pleasure all day of working with two fantastic graduate students in Stanford's Data Journalism Master's Program. Hannah Freitag has been here. Tracy Zhang, ladies, it's been such a pleasure working with you today. >> Same wise. >> I want to ask you both what are, as we wrap the day, I'm so inspired, I feel like I could go build an airplane. >> Exactly. >> Probably can't. But WiDS is just the inspiration that comes from this event. When you walk in the front door, you can feel it. >> Mm-hmm. >> Tracy, talk a little bit about what some of the things are that you heard today that really inspired you. >> I think one of the keyword that's like in my mind right now is like finding a mentor. >> Yeah. >> And I think, like if I leave this conference if I leave the talks, the conversations with one thing is that I'm very positive that if I want to switch, say someday, from Journalism to being a Data Analyst, to being like in Data Science, I'm sure that there are great role models for me to look up to, and I'm sure there are like mentors who can guide me through the way. So, like that, I feel reassured for some reason. >> It's a good feeling, isn't it? What do you, Hannah, what about you? What's your takeaway so far of the day? >> Yeah, one of my key takeaways is that anything's possible. >> Mm-hmm. >> So, if you have your vision, you have the role model, someone you look up to, and even if you have like a different background, not in Data Science, Data Engineering, or Computer Science but you're like, "Wow, this is really inspiring. I would love to do that." As long as you love it, you're passionate about it, and you are willing to, you know, take this path even though it won't be easy. >> Yeah. >> Then you can achieve it, and as you said, Tracy, it's important to have mentors on the way there. >> Exactly. >> But as long as you speak up, you know, you raise your voice, you ask questions, and you're curious, you can make it. >> Yeah. >> And I think that's one of my key takeaways, and I was just so inspiring to hear like all these women speaking on stage, and also here in our conversations and learning about their, you know, career path and what they learned on their way. >> Yeah, you bring up curiosity, and I think that is such an important skill. >> Mm-hmm. >> You know, you could think of Data Science and think about all the hard skills that you need. >> Mm, like coding. >> But as some of our guests said today, you don't have to be a statistician or an engineer, or a developer to get into this. Data Science applies to every facet of every part of the world. >> Mm-hmm. >> Finances, marketing, retail, manufacturing, healthcare, you name it, Data Science has the power and the potential to unlock massive achievements. >> Exactly. >> It's like we're scratching the surface. >> Yeah. >> But that curiosity, I think, is a great skill to bring to anything that you do. >> Mm-hmm. >> And I think we... For the female leaders that we're on stage, and that we had a chance to talk to on theCUBE today, I think they all probably had that I think as a common denominator. >> Exactly. >> That curious mindset, and also something that I think as hard is the courage to raise your hand. I like this, I'm interested in this. I don't see anybody that looks like me. >> But that doesn't mean I shouldn't do it. >> Exactly. >> Exactly, in addition to the curiosity that all the women, you know, bring to the table is that, in addition to that, being optimistic, and even though we don't see gender equality or like general equality in companies yet, we make progress and we're optimistic about it, and we're not like negative and complaining the whole time. But you know, this positive attitude towards a trend that is going in the right direction, and even though there's still a lot to be done- >> Exactly. >> We're moving it that way. >> Right. >> Being optimistic about this. >> Yeah, exactly, like even if it means that it's hard. Even if it means you need to be your own role model it's still like worth a try. And I think they, like all of the great women speakers, all the female leaders, they all have that in them, like they have the courage to like raise their hand and be like, "I want to do this, and I'm going to make it." And they're role models right now, so- >> Absolutely, they have drive. >> They do. >> Right. They have that ambition to take something that's challenging and complicated, and help abstract end users from that. Like we were talking to Intuit. I use Intuit in my small business for financial management, and she was talking about how they can from a machine learning standpoint, pull all this data off of documents that you upload and make that, abstract that, all that complexity from the end user, make something that's painful taxes. >> Mm-hmm. >> Maybe slightly less painful. It's still painful when you have to go, "Do I have to write you a check again?" >> Yeah. (laughs) >> Okay. >> But talking about just all the different applications of Data Science in the world, I found that to be very inspiring and really eye-opening. >> Definitely. >> I hadn't thought about, you know, we talk about climate change all the time, especially here in California, but I never thought about Data Science as a facilitator of the experts being able to make sense of what's going on historically and in real-time, or the application of Data Science in police violence. We see far too many cases of police violence on the news. It's an epidemic that's a horrible problem. Data Science can be applied to that to help us learn from that, and hopefully, start moving the needle in the right direction. >> Absolutely. >> Exactly. >> And especially like one sentence from Guitry from the very beginnings I still have in my mind is then when she said that arguments, no, that data beats arguments. >> Yes. >> In a conversation that if you be like, okay, I have this data set and it can actually show you this or that, it's much more powerful than just like being, okay, this is my position or opinion on this. And I think in a world where increasing like misinformation, and sometimes, censorship as we heard in one of the talks, it's so important to have like data, reliable data, but also acknowledge, and we talked about it with one of our interviewees that there's spices in data and we also need to be aware of this, and how to, you know, move this forward and use Data Science for social good. >> Mm-hmm. >> Yeah, for social good. >> Yeah, definitely, I think they like data, and the question about, or like the problem-solving part about like the social issues, or like some just questions, they definitely go hand-in-hand. Like either of them standing alone won't be anything that's going to be having an impact, but combining them together, you have a data set that illustrate a point or like solves the problem. I think, yeah, that's definitely like where Data Set Science is headed to, and I'm glad to see all these great women like making their impact and combining those two aspects together. >> It was interesting in the keynote this morning. We were all there when Margot Gerritsen who's one of the founders of WiDS, and Margot's been on the program before and she's a huge supporter of what we do and vice versa. She asked the non-women in the room, "Those who don't identify as women, stand up," and there was a handful of men, and she said, "That's what it's like to be a female in technology." >> Oh, my God. >> And I thought that vision give me goosebumps. >> Powerful. (laughs) >> Very powerful. But she's right, and one of the things I think that thematically another common denominator that I think we heard, I want to get your opinions as well from our conversations today, is the importance of community. >> Mm-hmm. >> You know, I was mentioning this stuff from AnitaB.org that showed that in 2022, the percentage of females and technical roles is 27.6%. It's a little bit of an increase. It's been hovering around 25% for a while. But one of the things that's still a problem is attrition. It doubled last year. >> Right. >> And I was asking some of the guests, and we've all done that today, "How would you advise companies to start moving the needle down on attrition?" >> Mm-hmm. >> And I think the common theme was network, community. >> Exactly. >> It takes a village like this. >> Mm-hmm. >> So you can see what you can be to help start moving that needle and that's, I think, what underscores the value of what WiDS delivers, and what we're able to showcase on theCUBE. >> Yeah, absolutely. >> I think it's very important to like if you're like a woman in tech to be able to know that there's someone for you, that there's a whole community you can rely on, and that like you are, you have the same mindset, you're working towards the same goal. And it's just reassuring and like it feels very nice and warm to have all these women for you. >> Lisa: It's definitely a warm fuzzy, isn't it? >> Yeah, and both the community within the workplace but also outside, like a network of family and friends who support you to- >> Yes. >> To pursue your career goals. I think that was also a common theme we heard that it's, yeah, necessary to both have, you know your community within your company or organization you're working but also outside. >> Definitely, I think that's also like how, why, the reason why we feel like this in like at WiDS, like I think we all feel very positive right now. So, yeah, I think that's like the power of the connection and the community, yeah. >> And the nice thing is this is like I said, WiDS is a movement. >> Yes. >> This is global. >> Mm-hmm. >> We've had some WiDS ambassadors on the program who started WiDS and Tel Aviv, for example, in their small communities. Or in Singapore and Mumbai that are bringing it here and becoming more of a visible part of the community. >> Tracy: Right. >> I loved seeing all the young faces when we walked in the keynote this morning. You know, we come here from a journalistic perspective. You guys are Journalism students. But seeing all the potential in the faces in that room just seeing, and hearing stories, and starting to make tangible connections between Facebook and data, and the end user and the perspectives, and the privacy and the responsibility of AI is all... They're all positive messages that need to be reinforced, and we need to have more platforms like this to be able to not just raise awareness, but sustain it. >> Exactly. >> Right. It's about the long-term, it's about how do we dial down that attrition, what can we do? What can we do? How can we help? >> Mm-hmm. >> Both awareness, but also giving women like a place where they can connect, you know, also outside of conferences. Okay, how do we make this like a long-term thing? So, I think WiDS is a great way to, you know, encourage this connectivity and these women teaming up. >> Yeah, (chuckles) girls help girls. >> Yeah. (laughs) >> It's true. There's a lot of organizations out there, girls who Code, Girls Inc., et cetera, that are all aimed at helping women kind of find their, I think, find their voice. >> Exactly. >> And find that curiosity. >> Yeah. Unlock that somewhere back there. Get some courage- >> Mm-hmm. >> To raise your hand and say, "I think I want to do this," or "I have a question. You explained something and I didn't understand it." Like, that's the advice I would always give to my younger self is never be afraid to raise your hand in a meeting. >> Mm-hmm. >> I guarantee you half the people weren't listening or, and the other half may not have understood what was being talked about. >> Exactly. >> So, raise your hand, there goes Margot Gerritsen, the founder of WiDS, hey, Margot. >> Hi. >> Keep alumni as you know, raise your hand, ask the question, there's no question that's stupid. >> Mm-hmm. >> And I promise you, if you just take that chance once it will open up so many doors, you won't even know which door to go in because there's so many that are opening. >> And if you have a question, there's at least one more person in the room who has the exact same question. >> Exact same question. >> Yeah, we'll definitely keep that in mind as students- >> Well, I'm curious how Data Journalism, what you heard today, Tracy, we'll start with you, and then, Hannah, to you. >> Mm-hmm. How has it influenced how you approach data-driven, and storytelling? Has it inspired you? I imagine it has, or has it given you any new ideas for, as you round out your Master's Program in the next few months? >> I think like one keyword that I found really helpful from like all the conversations today, was problem-solving. >> Yeah. >> Because I think, like we talked a lot about in our program about how to put a face on data sets. How to put a face, put a name on a story that's like coming from like big data, a lot of numbers but you need to like narrow it down to like one person or one anecdote that represents a bigger problem. And I think essentially that's problem-solving. That's like there is a community, there is like say maybe even just one person who has, well, some problem about something, and then we're using data. We're, by giving them a voice, by portraying them in news and like representing them in the media, we're solving this problem somehow. We're at least trying to solve this problem, trying to make some impact. And I think that's like what Data Science is about, is problem-solving, and, yeah, I think I heard a lot from today's conversation, also today's speakers. So, yeah, I think that's like something we should also think about as Journalists when we do pitches or like what kind of problem are we solving? >> I love that. >> Or like kind of what community are we trying to make an impact in? >> Yes. >> Absolutely. Yeah, I think one of the main learnings for me that I want to apply like to my career in Data Journalism is that I don't shy away from complexity because like Data Science is oftentimes very complex. >> Complex. >> And also data, you're using for your stories is complex. >> Mm-hmm. >> So, how can we, on the one hand, reduce complexity in a way that we make it accessible for broader audience? 'Cause, we don't want to be this like tech bubble talking in data jargon, we want to, you know, make it accessible for a broader audience. >> Yeah. >> I think that's like my purpose as a Data Journalist. But at the same time, don't reduce complexity when it's needed, you know, and be open to dive into new topics, and data sets and circling back to this of like raising your hand and asking questions if you don't understand like a certain part. >> Yeah. >> So, that's definitely a main learning from this conference. >> Definitely. >> That like, people are willing to talk to you and explain complex topics, and this will definitely facilitate your work as a Data Journalist. >> Mm-hmm. >> So, that inspired me. >> Well, I can't wait to see where you guys go from here. I've loved co-hosting with you today, thank you. >> Thank you. >> For joining me at our conference. >> Wasn't it fun? >> Thank you. >> It's a great event. It's, we, I think we've all been very inspired and I'm going to leave here probably floating above the ground a few inches, high on the inspiration of what this community can deliver, isn't that great? >> It feels great, I don't know, I just feel great. >> Me too. (laughs) >> So much good energy, positive energy, we love it. >> Yeah, so we want to thank all the organizers of WiDS, Judy Logan, Margot Gerritsen in particular. We also want to thank John Furrier who is here. And if you know Johnny, know he gets FOMO when he is not hosting. But John and Dave Vellante are such great supporters of women in technology, women in technical roles. We wouldn't be here without them. So, shout out to my bosses. Thank you for giving me the keys to theCube at this event. I know it's painful sometimes, but we hope that we brought you great stories all day. We hope we inspired you with the females and the one male that we had on the program today in terms of raise your hand, ask a question, be curious, don't be afraid to pursue what you're interested in. That's my soapbox moment for now. So, for my co-host, I'm Lisa Martin, we want to thank you so much for watching our program today. You can watch all of this on-demand on thecube.net. You'll find write-ups on siliconeangle.com, and, of course, YouTube. Thanks, everyone, stay safe and we'll see you next time. (energetic music)

(upbeat music) >> Welcome back to theCUBE's coverage of Women in Data Science 2023 live from Stanford University. This is Lisa Martin. My co-host is Tracy Yuan. We're excited to be having great conversations all day but you know, 'cause you've been watching. We've been interviewing some very inspiring women and some men as well, talking about all of the amazing applications of data science. You're not going to want to miss this next conversation. Our guest is Gabriela de Queiroz, Principal Cloud Advocate Manager of Microsoft. Welcome, Gabriela. We're excited to have you. >> Thank you very much. I'm so excited to be talking to you. >> Yeah, you're on theCUBE. >> Yeah, finally. (Lisa laughing) Like a dream come true. (laughs) >> I know and we love that. We're so thrilled to have you. So you have a ton of experience in the data space. I was doing some research on you. You've worked in software, financial advertisement, health. Talk to us a little bit about you. What's your background in? >> So I was trained in statistics. So I'm a statistician and then I worked in epidemiology. I worked with air pollution and public health. So I was a researcher before moving into the industry. So as I was talking today, the weekly paths, it's exactly who I am. I went back and forth and back and forth and stopped and tried something else until I figured out that I want to do data science and that I want to do different things because with data science we can... The beauty of data science is that you can move across domains. So I worked in healthcare, financial, and then different technology companies. >> Well the nice thing, one of the exciting things that data science, that I geek out about and Tracy knows 'cause we've been talking about this all day, it's just all the different, to your point, diverse, pun intended, applications of data science. You know, this morning we were talking about, we had the VP of data science from Meta as a keynote. She came to theCUBE talking and really kind of explaining from a content perspective, from a monetization perspective, and of course so many people in the world are users of Facebook. It makes it tangible. But we also heard today conversations about the applications of data science in police violence, in climate change. We're in California, we're expecting a massive rainstorm and we don't know what to do when it rains or snows. But climate change is real. Everyone's talking about it, and there's data science at its foundation. That's one of the things that I love. But you also have a lot of experience building diverse teams. Talk a little bit about that. You've created some very sophisticated data science solutions. Talk about your recommendation to others to build diverse teams. What's in it for them? And maybe share some data science project or two that you really found inspirational. >> Yeah, absolutely. So I do love building teams. Every time I'm given the task of building teams, I feel the luckiest person in the world because you have the option to pick like different backgrounds and all the diverse set of like people that you can find. I don't think it's easy, like people say, yeah, it's very hard. You have to be intentional. You have to go from the very first part when you are writing the job description through the interview process. So you have to be very intentional in every step. And you have to think through when you are doing that. And I love, like my last team, we had like 10 people and we were so diverse. Like just talking about languages. We had like 15 languages inside a team. So how beautiful it is. Like all different backgrounds, like myself as a statistician, but we had people from engineering background, biology, languages, and so on. So it's, yeah, like every time thinking about building a team, if you wanted your team to be diverse, you need to be intentional. >> I'm so glad you brought up that intention point because that is the fundamental requirement really is to build it with intention. >> Exactly, and I love to hear like how there's different languages. So like I'm assuming, or like different backgrounds, I'm assuming everybody just zig zags their way into the team and now you're all women in data science and I think that's so precious. >> Exactly. And not only woman, right. >> Tracy: Not only woman, you're right. >> The team was diverse not only in terms of like gender, but like background, ethnicity, and spoken languages, and language that they use to program and backgrounds. Like as I mentioned, not everybody did the statistics in school or computer science. And it was like one of my best teams was when we had this combination also like things that I'm good at the other person is not as good and we have this knowledge sharing all the time. Every day I would feel like I'm learning something. In a small talk or if I was reviewing something, there was always something new because of like the richness of the diverse set of people that were in your team. >> Well what you've done is so impressive, because not only have you been intentional with it, but you sound like the hallmark of a great leader of someone who hires and builds teams to fill gaps. They don't have to know less than I do for me to be the leader. They have to have different skills, different areas of expertise. That is really, honestly Gabriela, that's the hallmark of a great leader. And that's not easy to come by. So tell me, who were some of your mentors and sponsors along the way that maybe influenced you in that direction? Or is that just who you are? >> That's a great question. And I joke that I want to be the role model that I never had, right. So growing up, I didn't have anyone that I could see other than my mom probably or my sister. But there was no one that I could see, I want to become that person one day. And once I was tracing my path, I started to see people looking at me and like, you inspire me so much, and I'm like, oh wow, this is amazing and I want to do do this over and over and over again. So I want to be that person to inspire others. And no matter, like I'll be like a VP, CEO, whoever, you know, I want to be, I want to keep inspiring people because that's so valuable. >> Lisa: Oh, that's huge. >> And I feel like when we grow professionally and then go to the next level, we sometimes we lose that, you know, thing that's essential. And I think also like, it's part of who I am as I was building and all my experiences as I was going through, I became what I mentioned is unique person that I think we all are unique somehow. >> You're a rockstar. Isn't she a rockstar? >> You dropping quotes out. >> I'm loving this. I'm like, I've inspired Gabriela. (Gabriela laughing) >> Oh my God. But yeah, 'cause we were asking our other guests about the same question, like, who are your role models? And then we're talking about how like it's very important for women to see that there is a representation, that there is someone they look up to and they want to be. And so that like, it motivates them to stay in this field and to start in this field to begin with. So yeah, I think like you are definitely filling a void and for all these women who dream to be in data science. And I think that's just amazing. >> And you're a founder too. In 2012, you founded R Ladies. Talk a little bit about that. This is present in more than 200 cities in 55 plus countries. Talk about R Ladies and maybe the catalyst to launch it. >> Yes, so you always start, so I'm from Brazil, I always talk about this because it's such, again, I grew up over there. So I was there my whole life and then I moved to here, Silicon Valley. And when I moved to San Francisco, like the doors opened. So many things happening in the city. That was back in 2012. Data science was exploding. And I found out something about Meetup.com, it's a website that you can join and go in all these events. And I was going to this event and I joke that it was kind of like going to the Disneyland, where you don't know if I should go that direction or the other direction. >> Yeah, yeah. >> And I was like, should I go and learn about data visualization? Should I go and learn about SQL or should I go and learn about Hadoop, right? So I would go every day to those meetups. And I was a student back then, so you know, the budget was very restricted as a student. So we don't have much to spend. And then they would serve dinner and you would learn for free. And then I got to a point where I was like, hey, they are doing all of this as a volunteer. Like they are running this meetup and events for free. And I felt like it's a cycle. I need to do something, right. I'm taking all this in. I'm having this huge opportunity to be here. I want to give back. So that's what how everything started. I was like, no, I have to think about something. I need to think about something that I can give back. And I was using R back then and I'm like how about I do something with R. I love R, I'm so passionate about R, what about if I create a community around R but not a regular community, because by going to this events, I felt that as a Latina and as a woman, I was always in the corner and I was not being able to participate and to, you know, be myself and to network and ask questions. I would be in the corner. So I said to myself, what about if I do something where everybody feel included, where everybody can participate, can share, can ask questions without judgment? So that's how R ladies all came together. >> That's awesome. >> Talk about intentions, like you have to, you had that go in mind, but yeah, I wanted to dive a little bit into R. So could you please talk more about where did the passion for R come from, and like how did the special connection between you and R the language, like born, how did that come from? >> It was not a love at first sight. >> No. >> Not at all. Not at all. Because that was back in Brazil. So all the documentation were in English, all the tutorials, only two. We had like very few tutorials. It was not like nowadays that we have so many tutorials and courses. There were like two tutorials, other documentation in English. So it's was hard for me like as someone that didn't know much English to go through the language and then to learn to program was not easy task. But then as I was going through the language and learning and reading books and finding the people behind the language, I don't know how I felt in love. And then when I came to to San Francisco, I saw some of like the main contributors who are speaking in person and I'm like, wow, they are like humans. I don't know, it was like, I have no idea why I had this love. But I think the the people and then the community was the thing that kept me with the R language. >> Yeah, the community factors is so important. And it's so, at WIDS it's so palpable. I mean I literally walk in the door, every WIDS I've done, I think I've been doing them for theCUBE since 2017. theCUBE has been here since the beginning in 2015 with our co-founders. But you walk in, you get this sense of belonging. And this sense of I can do anything, why not? Why not me? Look at her up there, and now look at you speaking in the technical talk today on theCUBE. So inspiring. One of the things that I always think is you can't be what you can't see. We need to be able to see more people that look like you and sound like you and like me and like you as well. And WIDS gives us that opportunity, which is fantastic, but it's also helping to move the needle, really. And I was looking at some of the Anitab.org stats just yesterday about 2022. And they're showing, you know, the percentage of females in technical roles has been hovering around 25% for a while. It's a little higher now. I think it's 27.6 according to any to Anitab. We're seeing more women hired in roles. But what are the challenges, and I would love to get your advice on this, for those that might be in this situation is attrition, women who are leaving roles. What would your advice be to a woman who might be trying to navigate family and work and career ladder to stay in that role and keep pushing forward? >> I'll go back to the community. If you don't have a community around you, it's so hard to navigate. >> That's a great point. >> You are lonely. There is no one that you can bounce ideas off, that you can share what you are feeling or like that you can learn as well. So sometimes you feel like you are the only person that is going through that problem or like, you maybe have a family or you are planning to have a family and you have to make a decision. But you've never seen anyone going through this. So when you have a community, you see people like you, right. So that's where we were saying about having different people and people like you so they can share as well. And you feel like, oh yeah, so they went through this, they succeed. I can also go through this and succeed. So I think the attrition problem is still big problem. And I'm sure will be worse now with everything that is happening in Tech with layoffs. >> Yes and the great resignation. >> Yeah. >> We are going back, you know, a few steps, like a lot of like advancements that we did. I feel like we are going back unfortunately, but I always tell this, make sure that you have a community. Make sure that you have a mentor. Make sure that you have someone or some people, not only one mentor, different mentors, that can support you through this trajectory. Because it's not easy. But there are a lot of us out there. >> There really are. And that's a great point. I love everything about the community. It's all about that network effect and feeling like you belong- >> That's all WIDS is about. >> Yeah. >> Yes. Absolutely. >> Like coming over here, it's like seeing the old friends again. It's like I'm so glad that I'm coming because I'm all my old friends that I only see like maybe once a year. >> Tracy: Reunion. >> Yeah, exactly. And I feel like that our tank get, you know- >> Lisa: Replenished. >> Exactly. For the rest of the year. >> Yes. >> Oh, that's precious. >> I love that. >> I agree with that. I think one of the things that when I say, you know, you can't see, I think, well, how many females in technology would I be able to recognize? And of course you can be female technology working in the healthcare sector or working in finance or manufacturing, but, you know, we need to be able to have more that we can see and identify. And one of the things that I recently found out, I was telling Tracy this earlier that I geeked out about was finding out that the CTO of Open AI, ChatGPT, is a female. I'm like, (gasps) why aren't we talking about this more? She was profiled on Fast Company. I've seen a few pieces on her, Mira Murati. But we're hearing so much about ChatJTP being... ChatGPT, I always get that wrong, about being like, likening it to the launch of the iPhone, which revolutionized mobile and connectivity. And here we have a female in the technical role. Let's put her on a pedestal because that is hugely inspiring. >> Exactly, like let's bring everybody to the front. >> Yes. >> Right. >> And let's have them talk to us because like, you didn't know. I didn't know probably about this, right. You didn't know. Like, we don't know about this. It's kind of like we are hidden. We need to give them the spotlight. Every woman to give the spotlight, so they can keep aspiring the new generation. >> Or Susan Wojcicki who ran, how long does she run YouTube? All the YouTube influencers that probably have no idea who are influential for whatever they're doing on YouTube in different social platforms that don't realize, do you realize there was a female behind the helm that for a long time that turned it into what it is today? That's outstanding. Why aren't we talking about this more? >> How about Megan Smith, was the first CTO on the Obama administration. >> That's right. I knew it had to do with Obama. Couldn't remember. Yes. Let's let's find more pedestals. But organizations like WIDS, your involvement as a speaker, showing more people you can be this because you can see it, >> Yeah, exactly. is the right direction that will help hopefully bring us back to some of the pre-pandemic levels, and keep moving forward because there's so much potential with data science that can impact everyone's lives. I always think, you know, we have this expectation that we have our mobile phone and we can get whatever we want wherever we are in the world and whatever time of day it is. And that's all data driven. The regular average person that's not in tech thinks about data as a, well I'm paying for it. What's all these data charges? But it's powering the world. It's powering those experiences that we all want as consumers or in our business lives or we expect to be able to do a transaction, whether it's something in a CRM system or an Uber transaction like that, and have the app respond, maybe even know me a little bit better than I know myself. And that's all data. So I think we're just at the precipice of the massive impact that data science will make in our lives. And luckily we have leaders like you who can help navigate us along this path. >> Thank you. >> What advice for, last question for you is advice for those in the audience who might be nervous or maybe lack a little bit of confidence to go I really like data science, or I really like engineering, but I don't see a lot of me out there. What would you say to them? >> Especially for people who are from like a non-linear track where like going onto that track. >> Yeah, I would say keep going. Keep going. I don't think it's easy. It's not easy. But keep going because the more you go the more, again, you advance and there are opportunities out there. Sometimes it takes a little bit, but just keep going. Keep going and following your dreams, that you get there, right. So again, data science, such a broad field that doesn't require you to come from a specific background. And I think the beauty of data science exactly is this is like the combination, the most successful data science teams are the teams that have all these different backgrounds. So if you think that we as data scientists, we started programming when we were nine, that's not true, right. You can be 30, 40, shifting careers, starting to program right now. It doesn't matter. Like you get there no matter how old you are. And no matter what's your background. >> There's no limit. >> There was no limits. >> I love that, Gabriela, >> Thank so much. for inspiring. I know you inspired me. I'm pretty sure you probably inspired Tracy with your story. And sometimes like what you just said, you have to be your own mentor and that's okay. Because eventually you're going to turn into a mentor for many, many others and sounds like you're already paving that path and we so appreciate it. You are now officially a CUBE alumni. >> Yes. Thank you. >> Yay. We've loved having you. Thank you so much for your time. >> Thank you. Thank you. >> For our guest and for Tracy's Yuan, this is Lisa Martin. We are live at WIDS 23, the eighth annual Women in Data Science Conference at Stanford. Stick around. Our next guest joins us in just a few minutes. (upbeat music)

(gentle upbeat music) >> Hey, friends of theCUBE. It's Lisa Martin live at Stanford University covering the Eighth Annual Women In Data Science. But you've been a Cube fan for a long time. So you know that we've been here since the beginning of WiDS, which is 2015. We always loved to come and cover this event. We learned great things about data science, about women leaders, underrepresented minorities. And this year we have a special component. We've got two grad students from Stanford's Master's program and Data Journalism joining. One of my them is here with me, Hannah Freitag, my co-host. Great to have you. And we are pleased to welcome from Intuit for the first time, Shir Meir Lador Group Manager at Data Science. Shir, it's great to have you. Thank you for joining us. >> Thank you for having me. >> And I was just secrets girl talking with my boss of theCUBE who informed me that you're in great company. Intuit's Chief Technology Officer, Marianna Tessel is an alumni of theCUBE. She was on at our Supercloud event in January. So welcome back into it. >> Thank you very much. We're happy to be with you. >> Tell us a little bit about what you're doing. You're a data science group manager as I mentioned, but also you've had you've done some cool things I want to share with the audience. You're the co-founder of the PyData Tel Aviv Meetups the co-host of the unsupervised podcast about data science in Israel. You give talks, about machine learning, about data science. Tell us a little bit about your background. Were you always interested in STEM studies from the time you were small? >> So I was always interested in mathematics when I was small, I went to this special program for youth going to university. So I did my test in mathematics earlier and studied in university some courses. And that's when I understood I want to do something in that field. And then when I got to go to university, I went to electrical engineering when I found out about algorithms and how interested it is to be able to find solutions to problems, to difficult problems with math. And this is how I found my way into machine learning. >> Very cool. There's so much, we love talking about machine learning and AI on theCUBE. There's so much potential. Of course, we have to have data. One of the things that I love about WiDS and Hannah and I and our co-host Tracy, have been talking about this all day is the impact of data in everyone's life. If you break it down, I was at Mobile World Congress last week, all about connectivity telecom, and of course we have these expectation that we're going to be connected 24/7 from wherever we are in the world and we can do whatever we want. I can do an Uber transaction, I can watch Netflix, I can do a bank transaction. It all is powered by data. And data science is, some of the great applications of it is what it's being applied to. Things like climate change or police violence or health inequities. Talk about some of the data science projects that you're working on at Intuit. I'm an intuit user myself, but talk to me about some of those things. Give the audience really a feel for what you're doing. >> So if you are a Intuit product user, you probably use TurboTax. >> I do >> In the past. So for those who are not familiar, TurboTax help customers submit their taxes. Basically my group is in charge of getting all the information automatically from your documents, the documents that you upload to TurboTax. We extract that information to accelerate your tax submission to make it less work for our customers. So- >> Thank you. >> Yeah, and this is why I'm so proud to be working at this team because our focus is really to help our customers to simplify all the you know, financial heavy lifting with taxes and also with small businesses. We also do a lot of work in extracting information from small business documents like bill, receipts, different bank statements. Yeah, so this is really exciting for me, the opportunity to work to apply data science and machine learning to solution that actually help people. Yeah >> Yeah, in the past years there have been more and more digital products emerging that needs some sort of data security. And how did your team, or has your team developed in the past years with more and more products or companies offering digital services? >> Yeah, so can you clarify the question again? Sorry. >> Yeah, have you seen that you have more customers? Like has your team expanded in the past years with more digital companies starting that need kind of data security? >> Well, definitely. I think, you know, since I joined Intuit, I joined like five and a half years ago back when I was in Tel Aviv. I recently moved to the Bay Area. So when I joined, there were like a dozens of data scientists and machine learning engineers on Intuit. And now there are a few hundreds. So we've definitely grown with the year and there are so many new places we can apply machine learning to help our customers. So this is amazing, so much we can do with machine learning to get more money in the pocket of our customers and make them do less work. >> I like both of those. More money in my pocket and less work. That's awesome. >> Exactly. >> So keep going Intuit. But one of the things that is so cool is just the the abstraction of the complexity that Intuit's doing. I upload documents or it scans my receipts. I was just in Barcelona last week all these receipts and conversion euros to dollars and it takes that complexity away from the end user who doesn't know all that's going on in the background, but you're making people's lives simpler. Unfortunately, we all have to pay taxes, most of us should. And of course we're in tax season right now. And so it's really cool what you're doing with ML and data science to make fundamental processes to people's lives easier and just a little bit less complicated. >> Definitely. And I think that's what's also really amazing about Intuit it, is how it combines human in the loop as well as AI. Because in some of the tax situation it's very complicated maybe to do it yourself. And then there's an option to work with an expert online that goes on a video with you and helps you do your taxes. And the expert's work is also accelerated by AI because we build tools for those experts to do the work more efficiently. >> And that's what it's all about is you know, using data to be more efficient, to be faster, to be smarter, but also to make complicated processes in our daily lives, in our business lives just a little bit easier. One of the things I've been geeking out about recently is ChatGPT. I was using it yesterday. I was telling everyone I was asking it what's hot in data science and I didn't know would it know what hot is and it did, it gave me trends. But one of the things that I was so, and Hannah knows I've been telling this all day, I was so excited to learn over the weekend that the the CTO of OpenAI is a female. I didn't know that. And I thought why are we not putting her on a pedestal? Because people are likening ChatGPT to like the launch of the iPhone. I mean revolutionary. And here we have what I think is exciting for all of us females, whether you're in tech or not, is another role model. Because really ultimately what WiDS is great at doing is showcasing women in technical roles. Because I always say you can't be what you can't see. We need to be able to see more role models, female role role models, underrepresented minorities of course men, because a lot of my sponsors and mentors are men, but we need more women that we can look up to and see ah, she's doing this, why can't I? Talk to me about how you stay the course in data science. What excites you about the potential, the opportunities based on what you've already accomplished what inspires you to continue and be one of those females that we say oh my God, I could be like Shir. >> I think that what inspires me the most is the endless opportunities that we have. I think we haven't even started tapping into everything that we can do with generative AI, for example. There's so much that can be done to further help you know, people make more money and do less work because there's still so much work that we do that we don't need to. You know, this is with Intuit, but also there are so many other use cases like I heard today you know, with the talk about the police. So that was really exciting how you can apply machine learning and data to actually help people, to help people that been through wrongful things. So I was really moved by that. And I'm also really excited about all the medical applications that we can have with data. >> Yeah, yeah. It's true that data science is so diverse in terms of what fields it can cover but it's equally important to have diverse teams and have like equity and inclusion in your teams. Where is Intuit at promoting women, non-binary minorities in your teams to progress data science? >> Yeah, so I have so much to say on this. >> Good. >> But in my work in Tel Aviv, I had the opportunity to start with Intuit women in data science branch in Tel Aviv. So that's why I'm super excited to be here today for that because basically this is the original conference, but as you know, there are branches all over the world and I got the opportunity to lead the Tel Aviv branch with Israel since 2018. And we've been through already this year it's going to be it's next week, it's going to be the sixth conference. And every year our number of submission to make talk in the conference doubled itself. >> Nice. >> We started with 20 submission, then 50, then 100. This year we have over 200 submissions of females to give talk at the conference. >> Ah, that's fantastic. >> And beyond the fact that there's so much traction, I also feel the great impact it has on the community in Israel because one of the reason we started WiDS was that when I was going to conferences I was seeing so little women on stage in all the technical conferences. You know, kind of the reason why I guess you know, Margaret and team started the WiDS conference. So I saw the same thing in Israel and I was always frustrated. I was organizing PyData Meetups as you mentioned and I was always having such a hard time to get female speakers to talk. I was trying to role model, but that's not enough, you know. We need more. So once we started WiDS and people saw you know, so many examples on the stage and also you know females got opportunity to talk in a place for that. Then it also started spreading and you can see more and more female speakers across other conferences, which are not women in data science. So I think just the fact that Intuits started this conference back in Israel and also in Bangalore and also the support Intuit does for WiDS in Stanford here, it shows how much WiDS values are aligned with our values. Yeah, and I think that to chauffeur that I think we have over 35% females in the data science and machine learning engineering roles, which is pretty amazing I think compared to the industry. >> Way above average. Yeah, absolutely. I was just, we've been talking about some of the AnitaB.org stats from 2022 showing that 'cause usually if we look at the industry to you point, over the last, I don't know, probably five, 10 years we're seeing the number of female technologists around like a quarter, 25% or so. 2022 data from AnitaB.org showed that that number is now 27.6%. So it's very slowly- >> It's very slowly increasing. >> Going in the right direction. >> Too slow. >> And that representation of women technologists increase at every level, except intern, which I thought was really interesting. And I wonder is there a covid relation there? >> I don't know. >> What do we need to do to start opening up the the top of the pipeline, the funnel to go downstream to find kids like you when you were younger and always interested in engineering and things like that. But the good news is that the hiring we've seen improvements, but it sounds like Intuit is way ahead of the curve there with 35% women in data science or technical roles. And what's always nice and refreshing that we've talked, Hannah about this too is seeing companies actually put action into initiatives. It's one thing for a company to say we're going to have you know, 50% females in our organization by 2030. It's a whole other ball game to actually create a strategy, execute on it, and share progress. So kudos to Intuit for what it's doing because that is more companies need to adopt that same sort of philosophy. And that's really cultural. >> Yeah. >> At an organization and culture can be hard to change, but it sounds like you guys kind of have it dialed in. >> I think we definitely do. That's why I really like working and Intuit. And I think that a lot of it is with the role modeling, diversity and inclusion, and by having women leaders. When you see a woman in leadership position, as a woman it makes you want to come work at this place. And as an evidence, when I build the team I started in Israel at Intuit, I have over 50% women in my team. >> Nice. >> Yeah, because when you have a woman in the interviewers panel, it's much easier, it's more inclusive. That's why we always try to have at least you know, one woman and also other minorities represented in our interviews panel. Yeah, and I think that in general it's very important as a leader to kind of know your own biases and trying to have defined standard and rubrics in how you evaluate people to avoid for those biases. So all of that inclusiveness and leadership really helps to get more diversity in your teams. >> It's critical. That thought diversity is so critical, especially if we talk about AI and we're almost out of time, I just wanted to bring up, you brought up a great point about the diversity and equity. With respect to data science and AI, we know in AI there's biases in data. We need to have more inclusivity, more representation to help start shifting that so the biases start to be dialed down and I think a conference like WiDS and it sounds like someone like you and what you've already done so far in the work that you're doing having so many females raise their hands to want to do talks at events is a good situation. It's a good scenario and hopefully it will continue to move the needle on the percentage of females in technical roles. So we thank you Shir for your time sharing with us your story, what you're doing, how Intuit and WiDS are working together. It sounds like there's great alignment there and I think we're at the tip of the iceberg with what we can do with data science and inclusion and equity. So we appreciate all of your insights and your time. >> Thank you very much. >> All right. >> I enjoyed very, very much >> Good. We hope, we aim to please. Thank you for our guests and for Hannah Freitag. This is Lisa Martin coming to you live from Stanford University. This is our coverage of the eighth Annual Women in Data Science Conference. Stick around, next guest will be here in just a minute.

(gentle music) >> Hey! Welcome back to theCUBE's coverage of WiDS 2023, the eighth Annual Women In Data Science Conference. I'm your host, Lisa Martin. We are at Stanford University, as you know we are every year, having some wonderful conversations with some very inspiring women and men in data science and technical roles. I'm very pleased to introduce Tracy Zhang, my co-host, who is in the Data Journalism program at Stanford. And Tracy and I are pleased to welcome our next guest, Rhonda Crate, Principal Data Scientist at Boeing. Great to have you on the program, Rhonda. >> Tracy: Welcome. >> Hey, thanks for having me. >> Were you always interested in data science or STEM from the time you were young? >> No, actually. I was always interested in archeology and anthropology. >> That's right, we were talking about that, anthropology. Interesting. >> We saw the anthropology background, not even a bachelor's degree, but also a master's degree in anthropology. >> So you were committed for a while. >> I was, I was. I actually started college as a fine arts major, but I always wanted to be an archeologist. So at the last minute, 11 credits in, left to switch to anthropology. And then when I did my master's, I focused a little bit more on quantitative research methods and then I got my Stat Degree. >> Interesting. Talk about some of the data science projects that you're working on. When I think of Boeing, I always think of aircraft. But you are doing a lot of really cool things in IT, data analytics. Talk about some of those intriguing data science projects that you're working on. >> Yeah. So when I first started at Boeing, I worked in information technology and data analytics. And Boeing, at the time, had cored up data science in there. And so we worked as a function across the enterprise working on anything from shared services to user experience in IT products, to airplane programs. So, it has a wide range. I worked on environment health and safety projects for a long time as well. So looking at ergonomics and how people actually put parts onto airplanes, along with things like scheduling and production line, part failures, software testing. Yeah, there's a wide spectrum of things. >> But I think that's so fantastic. We've been talking, Tracy, today about just what we often see at WiDS, which is this breadth of diversity in people's background. You talked about anthropology, archeology, you're doing data science. But also all of the different opportunities that you've had at Boeing. To see so many facets of that organization. I always think that breadth of thought diversity can be hugely impactful. >> Yeah. So I will say my anthropology degree has actually worked to my benefit. I'm a huge proponent of integrating liberal arts and sciences together. And it actually helps me. I'm in the Technical Fellowship program at Boeing, so we have different career paths. So you can go into management, you can be a regular employee, or you can go into the Fellowship program. So right now I'm an Associate Technical Fellow. And part of how I got into the Fellowship program was that diversity in my background, what made me different, what made me stand out on projects. Even applying a human aspect to things like ergonomics, as silly as that sounds, but how does a person actually interact in the space along with, here are the actual measurements coming off of whatever system it is that you're working on. So, I think there's a lot of opportunities, especially in safety as well, which is a big initiative for Boeing right now, as you can imagine. >> Tracy: Yeah, definitely. >> I can't go into too specifics. >> No, 'cause we were like, I think a theme for today that kind of we brought up in in all of our talk is how data is about people, how data is about how people understand the world and how these data can make impact on people's lives. So yeah, I think it's great that you brought this up, and I'm very happy that your anthropology background can tap into that and help in your day-to-day data work too. >> Yeah. And currently, right now, I actually switched over to Strategic Workforce Planning. So it's more how we understand our workforce, how we work towards retaining the talent, how do we get the right talent in our space, and making sure overall that we offer a culture and work environment that is great for our employees to come to. >> That culture is so important. You know, I was looking at some anitab.org stats from 2022 and you know, we always talk about the number of women in technical roles. For a long time it's been hovering around that 25% range. The data from anitab.org showed from '22, it's now 27.6%. So, a little increase. But one of the biggest challenges still, and Tracy and I and our other co-host, Hannah, have been talking about this, is attrition. Attrition more than doubled last year. What are some of the things that Boeing is doing on the retention side, because that is so important especially as, you know, there's this pipeline leakage of women leaving technical roles. Tell us about what Boeing's, how they're invested. >> Yeah, sure. We actually have a publicly available Global Diversity Report that anybody can go and look at and see our statistics for our organization. Right now, off the top of my head, I think we're hovering at about 24% in the US for women in our company. It has been a male majority company for many years. We've invested heavily in increasing the number of women in roles. One interesting thing about this year that came out is that even though with the great resignation and those types of things, the attrition level between men and women were actually pretty close to being equal, which is like the first time in our history. Usually it tends on more women leaving. >> Lisa: That's a good sign. >> Right. >> Yes, that's a good sign. >> And we've actually focused on hiring and bringing in more women and diversity in our company. >> Yeah, some of the stats too from anitab.org talked about the increase, and I have to scroll back and find my notes, the increase in 51% more women being hired in 2022 than 2021 for technical roles. So the data, pun intended, is showing us. I mean, the data is there to show the impact that having females in executive leadership positions make from a revenue perspective. >> Tracy: Definitely. >> Companies are more profitable when there's women at the head, or at least in senior leadership roles. But we're seeing some positive trends, especially in terms of representation of women technologists. One of the things though that I found interesting, and I'm curious to get your thoughts on this, Rhonda, is that the representation of women technologists is growing in all areas, except interns. >> Rhonda: Hmm. >> So I think, we've got to go downstream. You teach, I have to go back to my notes on you, did my due diligence, R programming classes through Boeings Ed Wells program, this is for WSU College of Arts and Sciences, talk about what you teach and how do you think that intern kind of glut could be solved? >> Yeah. So, they're actually two separate programs. So I teach a data analytics course at Washington State University as an Adjunct Professor. And then the Ed Wells program is a SPEEA, which is an Aerospace Union, focused on bringing up more technology and skills to the actual workforce itself. So it's kind of a couple different audiences. One is more seasoned employees, right? The other one is our undergraduates. I teach a Capstone class, so it's a great way to introduce students to what it's actually like to work on an industry project. We partner with Google and Microsoft and Boeing on those. The idea is also that maybe those companies have openings for the students when they're done. Since it's Senior Capstone, there's not a lot of opportunities for internships. But the opportunities to actually get hired increase a little bit. In regards to Boeing, we've actually invested a lot in hiring more women interns. I think the number was 40%, but you'd have to double check. >> Lisa: That's great, that's fantastic. >> Tracy: That's way above average, I think. >> That's a good point. Yeah, it is above average. >> Double check on that. That's all from my memory. >> Is this your first WiDS, or have you been before? >> I did virtually last year. >> Okay. One of the things that I love, I love covering this event every year. theCUBE's been covering it since it's inception in 2015. But it's just the inspiration, the vibe here at Stanford is so positive. WiDS is a movement. It's not an initiative, an organization. There are going to be, I think annually this year, there will be 200 different events. Obviously today we're live on International Women's Day. 60 plus countries, 100,000 plus people involved. So, this is such a positive environment for women and men, because we need everybody, underrepresented minorities, to be able to understand the implication that data has across our lives. If we think about stripping away titles in industries, everybody is a consumer, not everybody, most of mobile devices. And we have this expectation, I was in Barcelona last week at a Mobile World Congress, we have this expectation that we're going to be connected 24/7. I can get whatever I want wherever I am in the world, and that's all data driven. And the average person that isn't involved in data science wouldn't understand that. At the same time, they have expectations that depend on organizations like Boeing being data driven so that they can get that experience that they expect in their consumer lives in any aspect of their lives. And that's one of the things I find so interesting and inspiring about data science. What are some of the things that keep you motivated to continue pursuing this? >> Yeah I will say along those lines, I think it's great to invest in K-12 programs for Data Literacy. I know one of my mentors and directors of the Data Analytics program, Dr. Nairanjana Dasgupta, we're really familiar with each other. So, she runs a WSU program for K-12 Data Literacy. It's also something that we strive for at Boeing, and we have an internal Data Literacy program because, believe it or not, most people are in business. And there's a lot of disconnect between interpreting and understanding data. For me, what kind of drives me to continue data science is that connection between people and data and how we use it to improve our world, which is partly why I work at Boeing too 'cause I feel that they produce products that people need like satellites and airplanes, >> Absolutely. >> and everything. >> Well, it's tangible, it's relatable. We can understand it. Can you do me a quick favor and define data literacy for anyone that might not understand what that means? >> Yeah, so it's just being able to understand elements of data, whether that's a bar chart or even in a sentence, like how to read a statistic and interpret a statistic in a sentence, for example. >> Very cool. >> Yeah. And sounds like Boeing's doing a great job in these programs, and also trying to hire more women. So yeah, I wanted to ask, do you think there's something that Boeing needs to work on? Or where do you see yourself working on say the next five years? >> Yeah, I think as a company, we always think that there's always room for improvement. >> It never, never stops. >> Tracy: Definitely. (laughs) >> I know workforce strategy is an area that they're currently really heavily investing in, along with safety. How do we build safer products for people? How do we help inform the public about things like Covid transmission in airports? For example, we had the Confident Traveler Initiative which was a big push that we had, and we had to be able to inform people about data models around Covid, right? So yeah, I would say our future is more about an investment in our people and in our culture from my perspective >> That's so important. One of the hardest things to change especially for a legacy organization like Boeing, is culture. You know, when I talk with CEO's or CIO's or COO's about what's your company's vision, what's your strategy? Especially those companies that are on that digital journey that have no choice these days. Everybody expects to have a digital experience, whether you're transacting an an Uber ride, you're buying groceries, or you're traveling by air. That culture sounds like Boeing is really focused on that. And that's impressive because that's one of the hardest things to morph and mold, but it's so essential. You know, as we look around the room here at WiDS it's obviously mostly females, but we're talking about women, underrepresented minorities. We're talking about men as well who are mentors and sponsors to us. I'd love to get your advice to your younger self. What would you tell yourself in terms of where you are now to become a leader in the technology field? >> Yeah, I mean, it's kind of an interesting question because I always try to think, live with no regrets to an extent. >> Lisa: I like that. >> But, there's lots of failures along the way. (Tracy laughing) I don't know if I would tell myself anything different because honestly, if I did, I wouldn't be where I am. >> Lisa: Good for you. >> I started out in fine arts, and I didn't end up there. >> That's good. >> Such a good point, yeah. >> We've been talking about that and I find that a lot at events like WiDS, is women have these zigzaggy patterns. I studied biology, I have a master's in molecular biology, I'm in media and marketing. We talked about transportable skills. There's a case I made many years ago when I got into tech about, well in science you learn the art of interpreting esoteric data and creating a story from it. And that's a transportable skill. But I always say, you mentioned failure, I always say failure is not a bad F word. It allows us to kind of zig and zag and learn along the way. And I think that really fosters thought diversity. And in data science, that is one of the things we absolutely need to have is that diversity and thought. You know, we talk about AI models being biased, we need the data and we need the diverse brains to help ensure that the biases are identified, extracted, and removed. Speaking of AI, I've been geeking out with ChatGPT. So, I'm on it yesterday and I ask it, "What's hot in data science?" And I was like, is it going to get that? What's hot? And it did it, it came back with trends. I think if I ask anything, "What's hot?", I should be to Paris Hilton, but I didn't. And so I was geeking out. One of the things I learned recently that I thought was so super cool is the CTO of OpenAI is a woman, Mira Murati, which I didn't know until over the weekend. Because I always think if I had to name top females in tech, who would they be? And I always default to Sheryl Sandberg, Carly Fiorina, Susan Wojcicki running YouTube. Who are some of the people in your history, in your current, that are really inspiring to you? Men, women, indifferent. >> Sure. I think Boeing is one of the companies where you actually do see a lot of women in leadership roles. I think we're one of the top companies with a number of women executives, actually. Susan Doniz, who's our Chief Information Officer, I believe she's actually slotted to speak at a WiDS event come fall. >> Lisa: Cool. >> So that will be exciting. Susan's actually relatively newer to Boeing in some ways. A Boeing time skill is like three years is still kind of new. (laughs) But she's been around for a while and she's done a lot of inspiring things, I think, for women in the organization. She does a lot with Latino communities and things like that as well. For me personally, you know, when I started at Boeing Ahmad Yaghoobi was one of my mentors and my Technical Lead. He came from Iran during a lot of hard times in the 1980s. His brother actually wrote a memoir, (laughs) which is just a fun, interesting fact. >> Tracy: Oh my God! >> Lisa: Wow! >> And so, I kind of gravitate to people that I can learn from that's not in my sphere, that might make me uncomfortable. >> And you probably don't even think about how many people you're influencing along the way. >> No. >> We just keep going and learning from our mentors and probably lose sight of, "I wonder how many people actually admire me?" And I'm sure there are many that admire you, Rhonda, for what you've done, going from anthropology to archeology. You mentioned before we went live you were really interested in photography. Keep going and really gathering all that breadth 'cause it's only making you more inspiring to people like us. >> Exactly. >> We thank you so much for joining us on the program and sharing a little bit about you and what brought you to WiDS. Thank you so much, Rhonda. >> Yeah, thank you. >> Tracy: Thank you so much for being here. >> Lisa: Yeah. >> Alright. >> For our guests, and for Tracy Zhang, this is Lisa Martin live at Stanford University covering the eighth Annual Women In Data Science Conference. Stick around. Next guest will be here in just a second. (gentle music)

(upbeat music) >> Hey, girls and guys. Welcome back to theCUBE. We are live at Stanford University, covering the 8th Annual Women in Data Science Conference. One of my favorite events. Lisa Martin here. Got a couple of guests from Total Energies. We're going to be talking all things data science, and I think you're going to find this pretty interesting and inspirational. Please welcome Alexandre Lapene, Tech Advisor Data Science at Total Energy. It's great to have you. >> Thank you. >> And Myriam Fayad is here as well, product and value manager at Total Energies. Great to have you guys on theCUBE today. Thank you for your time. >> Thank you for - >> Thank you for receiving us. >> Give the audience, Alexandre, we'll start with you, a little bit about Total Energies, so they understand the industry, and what it is that you guys are doing. >> Yeah, sure, sure. So Total Energies, is a former Total, so we changed name two years ago. So we are a multi-energy company now, working over 130 countries in the world, and more than 100,000 employees. >> Lisa: Oh, wow, big ... >> So we're a quite big company, and if you look at our new logo, you will see there are like seven colors. That's the seven energy that we basically that our business. So you will see the red for the oil, the blue for the gas, because we still have, I mean, a lot of oil and gas, but you will see other color, like blue for hydrogen. >> Lisa: Okay. >> Green for gas, for biogas. >> Lisa: Yeah. >> And a lot of other solar and wind. So we're definitely multi-energy company now. >> Excellent, and you're both from Paris? I'm jealous, I was supposed to go. I'm not going to be there next month. Myriam, talk a little bit about yourself. I'd love to know a little bit about your role. You're also a WiDS ambassador this year. >> Myriam: Yes. >> Lisa: Which is outstanding, but give us a little bit of your background. >> Yes, so today I'm a product manager at the Total Energies' Digital Factory. And at the Digital Factory, our role is to develop digital solutions for all of the businesses of Total Energies. And as a background, I did engineering school. So, and before that I, I would say, I wasn't really aware of, I had never asked myself if being a woman could stop me from being, from doing what I want to do in the professional career. But when I started my engineering school, I started seeing that women are becoming, I would say, increasingly rare in the environment >> Lisa: Yes. >> that, where I was evolving. >> Lisa: Yes. >> So that's why I was, I started to think about, about such initiatives. And then when I started working in the tech field, that conferred me that women are really rare in the tech field and data science field. So, and at Total Energies, I met ambassadors of, of the WiDS initiatives. And that's how I, I decided to be a WiDS Ambassador, too. So our role is to organize events locally in the countries where we work to raise awareness about the importance of having women in the tech and data fields. And also to talk about the WiDS initiative more globally. >> One of my favorite things about WiDS is it's this global movement, it started back in 2015. theCUBE has been covering it since then. I think I've been covering it for theCUBE since 2017. It's always a great day full of really positive messages. One of the things that we talk a lot about when we're focusing on the Q1 Women in Tech, or women in technical roles is you can't be what you can't see. We need to be able to see these role models, but also it, we're not just talking about women, we're talking about underrepresented minorities, we're talking about men like you, Alexander. Talk to us a little bit about what your thoughts are about being at a Women and Data Science Conference and your sponsorship, I'm sure, of many women in Total, and other industries that appreciate having you as a guide. >> Yeah, yeah, sure. First I'm very happy because I'm back to Stanford. So I did my PhD, postdoc, sorry, with Margot, I mean, back in 20, in 2010, so like last decade. >> Lisa: Yeah, yep. >> I'm a film mechanics person, so I didn't start as data scientist, but yeah, WiDS is always, I mean, this great event as you describe it, I mean, to see, I mean it's growing every year. I mean, it's fantastic. And it's very, I mean, I mean, it's always also good as a man, I mean, to, to be in the, in the situation of most of the women in data science conferences. And when Margo, she asked at the beginning of the conference, "Okay, how many men do we have? Okay, can you stand up?" >> Lisa: Yes. I saw that >> It was very interesting because - >> Lisa: I could count on one hand. >> What, like 10 or ... >> Lisa: Yeah. >> Maximum. >> Lisa: Yeah. >> And, and I mean, you feel that, I mean, I mean you could feel what what it is to to be a woman in the field and - >> Lisa: Absolutely. >> Alexandre: That's ... >> And you, sounds like you experienced it. I experienced the same thing. But one of the things that fascinates me about data science is all of the different real world problems it's helping to solve. Like, I keep saying this, we're, we're in California, I'm a native Californian, and we've been in an extreme drought for years. Well, we're getting a ton of rain and snow this year. Climate change. >> Guests: Yeah. We're not used to driving in the rain. We are not very good at it either. But the, just thinking about data science as a facilitator of its understanding climate change better; to be able to make better decisions, predictions, drive better outcomes, or things like, police violence or healthcare inequities. I think the power of data science to help unlock a lot of the unknown is so great. And, and we need that thought diversity. Miriam, you're talking about being in engineering. Talk to me a little bit about what projects interest you with respect to data science, and how you are involved in really creating more diversity and thought. >> Hmm. In fact, at Total Energies in addition to being an energy company we're also a data company in the sense that we produce a lot of data in our activities. For example with the sensors on the fuel on the platforms. >> Lisa: Yes. >> Or on the wind turbines, solar panels and even data related to our clients. So what, what is really exciting about being, working in the data science field at Total Energies is that we really feel the impact of of the project that we're working on. And we really work with the business to understand their problems. >> Lisa: Yeah. >> Or their issues and try to translate it to a technical problem and to solve it with the data that we have. So that's really exciting, to feel the impact of the projects we're working on. So, to take an example, maybe, we know that one of the challenges of the energy transition is the storage of of energy coming from renewable power. >> Yes. >> So I'm working currently on a project to improve the process of creating larger batteries that will help store this energy, by collecting the data, and helping the business to improve the process of creating these batteries. To make it more reliable, and with a better quality. So this is a really interesting project we're working on. >> Amazing, amazing project. And, you know, it's, it's fun I think to think of all of the different people, communities, countries, that are impacted by what you're doing. Everyone, everyone knows about data. Sometimes we think about it as we're paying we're always paying for a lot of data on our phone or "data rates may apply" but we may not be thinking about all of the real world impact that data science is making in our lives. We have this expectation in our personal lives that we're connected 24/7. >> Myriam: Yeah. >> I can get whatever I want from my phone wherever I am in the world. And that's all data driven. And we expect that if I'm dealing with Total Energies, or a retailer, or a car dealer that they're going to have the data, the data to have a personal conversation, conversation with me. We have this expectation. I don't think a lot of people that aren't in data science or technology really realize the impact of data all around their lives. Alexander, talk about some of the interesting data science projects that you're working on. >> There's one that I'm working right now, so I stake advisor. I mean, I'm not the one directly working on it. >> Lisa: Okay. >> But we have, you know, we, we are from the digital factory where we, we make digital products. >> Lisa: Okay. >> And we have different squads. I mean, it's a group of different people with different skills. And one of, one of the, this squad, they're, they're working on the on, on the project that is about safety. We have a lot of site, work site on over the world where we deploy solar panels on on parkings, on, on buildings everywhere. >> Lisa: Okay. Yeah. >> And there's, I mean, a huge, I mean, but I mean, we, we have a lot of, of worker and in term of safety we want to make sure that the, they work safely and, and we want to prevent accidents. So what we, what we do is we, we develop some computer vision approach to help them at improving, you know, the, the, the way they work. I mean the, the basic things is, is detecting, detecting some equipment like the, the the mean the, the vest and so on. But we, we, we, we are working, we're working to really extend that to more concrete recommendation. And that's one a very exciting project. >> Lisa: Yeah. >> Because it's very concrete. >> Yeah. >> And also, I, I'm coming from the R&D of the company and that's one, that's one of this project that started in R&D and is now into the Digital Factory. And it will become a real product deployed over the world on, on our assets. So that's very great. >> The influence and the impact that data can have on every business always is something that, we could talk about that for a very long time. >> Yeah. >> But one of the things I want to address is there, I'm not sure if you're familiar with AnitaB.org the Grace Hopper Institute? It's here in the States and they do this great event every year. It's very pro-women in technology and technical roles. They do a lot of, of survey of, of studies. So they have data demonstrating where are we with respect to women in technical roles. And we've been talking about it for years. It's been, for a while hovering around 25% of technical roles are held by women. I noticed in the AnitaB.org research findings from 2022, It's up to 27.6% I believe. So we're seeing those numbers slowly go up. But one of the things that's a challenge is attrition; of women getting in the roles and then leaving. Miryam, as a woman in, in technology. What inspires you to continue doing what you're doing and to elevate your career in data science? >> What motivates me, is that data science, we really have to look at it as a mean to solve a problem and not a, a fine, a goal in itself. So the fact that we can apply data science to so many fields and so many different projects. So here, for example we took examples of more industrial, maybe, applications. But for example, recently I worked on, on a study, on a data science study to understand what to, to analyze Google reviews of our clients on the service stations and to see what are the the topics that, that are really important to them. So we really have a, a large range of topics, and a diversity of topics that are really interesting, so. >> And that's so important, the diversity of topics alone. There's, I think we're just scratching the surface. We're just at the very beginning of what data science can empower for our daily lives. For businesses, small businesses, large businesses. I'd love to get your perspective as our only male on the show today, Alexandre, you have that elite title. The theme of International Women's Day this year which is today, March 8th, is "Embrace equity." >> Alexandre: Yes. >> Lisa: What is that, when you hear that theme as as a male in technology, as a male in the, in a role where you can actually elevate women and really bring in that thought diversity, what is embracing equity, what does it look like to you? >> To me, it, it's really, I mean, because we, we always talk about how we can, you know, I mean improve, but actually we are fixing a problem, an issue. I mean, it's such a reality. I mean, and the, the reality and and I mean, and force in, in the company. And that's, I think in Total Energy, we, we still have, I mean things, I mean, we, we haven't reached our objective but we're working hard and especially at the Digital Factory to, to, to improve on that. And for example, we have 40% of our women in tech. >> Lisa: 40? >> 40% of our tech people that are women. >> Lisa: Wow, that's fantastic! >> Yeah. That's, that's ... >> You're way ahead of, of the global average. >> Alexandre: Yeah. Yeah. >> That outstanding. >> We're quite proud of that. >> You should be. >> But we, we still, we still know that we, we have at least 10% >> Lisa: Yes. because it's not 50. The target is, the target is to 50 or more. And, and, but I want to insist on the fact that we have, we are correcting an issue. We are fixing an issue. We're not trying to improve something. I mean, that, that's important to have that in mind. >> Lisa: It is. Absolutely. >> Yeah. >> Miryam, I'd love to get your advice to your younger self, before you studied engineering. Obviously you had an interest when you were younger. What advice would you give to young Miriam now, looking back at what you've accomplished and being one of our female, visible females, in a technical role? What do you, what would you say to your younger self? >> Maybe I would say to continue as I started. So as I was saying at the beginning of the interview, when I was at high school, I have never felt like being a woman could stop me from doing anything. >> Lisa: Yeah. Yeah. >> So maybe to continue thinking this way, and yeah. And to, to stay here for, to, to continue this way. Yeah. >> Lisa: That's excellent. Sounds like you have the confidence. >> Mm. Yeah. >> And that's something that, that a lot of people ... I struggled with it when I was younger, have the confidence, "Can I do this?" >> Alexandre: Yeah. >> "Should I do this?" >> Myriam: Yeah. >> And you kind of went, "Why not?" >> Myriam: Yes. >> Which is, that is such a great message to get out to our audience and to everybody else's. Just, "I'm interested in this. I find it fascinating. Why not me?" >> Myriam: Yeah. >> Right? >> Alexandre: Yeah, true. >> And by bringing out, I think, role models as we do here at the conference, it's a, it's a way to to help young girls to be inspired and yeah. >> Alexandre: Yeah. >> We need to have women in leadership positions that we can see, because there's a saying here that we say a lot in the States, which is: "You can't be what you can't see." >> Alexandre: Yeah, that's true. >> And so we need more women and, and men supporting women and underrepresented minorities. And the great thing about WiDS is it does just that. So we thank you so much for your involvement in WiDS, Ambassador, our only male on the program today, Alexander, we thank you. >> I'm very proud of it. >> Awesome to hear that Total Energies has about 40% of females in technical roles and you're on that path to 50% or more. We, we look forward to watching that journey and we thank you so much for joining us on the show today. >> Alexandre: Thank you. >> Myriam: Thank you. >> Lisa: All right. For my guests, I'm Lisa Martin. You're watching theCUBE Live from Stanford University. This is our coverage of the eighth Annual Women in Data Science Conference. We'll be back after a short break, so stick around. (upbeat music)

(upbeat music) >> Morning guys and girls, welcome back to theCUBE's live coverage of Women in Data Science WIDS 2023 live at Stanford University. Lisa Martin here with my co-host for this segment, Tracy Zhang. We're really excited to be talking with a great female rockstar. You're going to learn a lot from her next, Jacqueline Kuo, solutions engineer at Dataiku. Welcome, Jacqueline. Great to have you. >> Thank you so much. >> Thank for being here. >> I'm so excited to be here. >> So one of the things I have to start out with, 'cause my mom Kathy Dahlia is watching, she's a New Yorker. You are a born and raised New Yorker and I learned from my mom and others. If you're born in New York no matter how long you've moved away, you are a New Yorker. There's you guys have like a secret club. (group laughs) >> I am definitely very proud of being born and raised in New York. My family immigrated to New York, New Jersey from Taiwan. So very proud Taiwanese American as well. But I absolutely love New York and I can't imagine living anywhere else. >> Yeah, yeah. >> I love it. >> So you studied, I was doing some research on you you studied mechanical engineering at MIT. >> Yes. >> That's huge. And you discovered your passion for all things data-related. You worked at IBM as an analytics consultant. Talk to us a little bit about your career path. Were you always interested in engineering STEM-related subjects from the time you were a child? >> I feel like my interests were ranging in many different things and I ended up landing in engineering, 'cause I felt like I wanted to gain a toolkit like a toolset to make some sort of change with or use my career to make some sort of change in this world. And I landed on engineering and mechanical engineering specifically, because I felt like I got to, in my undergrad do a lot of hands-on projects, learn every part of the engineering and design process to build products which is super-transferable and transferable skills sort of is like the trend in my career so far. Where after undergrad I wanted to move back to New York and mechanical engineering jobs are kind of few and fall far in between in the city. And I ended up landing at IBM doing analytics consulting, because I wanted to understand how to use data. I knew that data was really powerful and I knew that working with it could allow me to tell better stories to influence people across different industries. And that's also how I kind of landed at Dataiku to my current role, because it really does allow me to work across different industries and work on different problems that are just interesting. >> Yeah, I like the way that, how you mentioned building a toolkit when doing your studies at school. Do you think a lot of skills are still very relevant to your job at Dataiku right now? >> I think that at the core of it is just problem solving and asking questions and continuing to be curious or trying to challenge what is is currently given to you. And I think in an engineering degree you get a lot of that. >> Yeah, I'm sure. >> But I think that we've actually seen that a lot in the panels today already, that you get that through all different types of work and research and that kind of thoughtfulness comes across in all different industries too. >> Talk a little bit about some of the challenges, that data science is solving, because every company these days, whether it's an enterprise in manufacturing or a small business in retail, everybody has to be data-driven, because the end user, the end customer, whoever that is whether it's a person, an individual, a company, a B2B, expects to have a personalized custom experience and that comes from data. But you have to be able to understand that data treated properly, responsibly. Talk about some of the interesting projects that you're doing at Dataiku or maybe some that you've done in the past that are really kind of transformative across things climate change or police violence, some of the things that data science really is impacting these days. >> Yeah, absolutely. I think that what I love about coming to these conferences is that you hear about those really impactful social impact projects that I think everybody who's in data science wants to be working on. And I think at Dataiku what's great is that we do have this program called Ikig.AI where we work with nonprofits and we support them in their data and analytics projects. And so, a project I worked on was with the Clean Water, oh my goodness, the Ocean Cleanup project, Ocean Cleanup organization, which was amazing, because it was sort of outside of my day-to-day and it allowed me to work with them and help them understand better where plastic is being aggregated across the world and where it appears, whether that's on beaches or in lakes and rivers. So using data to help them better understand that. I feel like from a day-to-day though, we, in terms of our customers, they're really looking at very basic problems with data. And I say basic, not to diminish it, but really just to kind of say that it's high impact, but basic problems around how do they forecast sales better? That's a really kind of, sort of basic problem, but it's actually super-complex and really impactful for people, for companies when it comes to forecasting how much headcount they need to have in the next year or how much inventory to have if they're retail. And all of those are going to, especially for smaller companies, make a huge impact on whether they make profit or not. And so, what's great about working at Dataiku is you get to work on these high-impact projects and oftentimes I think from my perspective, I work as a solutions engineer on the commercial team. So it's just, we work generally with smaller customers and sometimes talking to them, me talking to them is like their first introduction to what data science is and what they can do with that data. And sort of using our platform to show them what the possibilities are and help them build a strategy around how they can implement data in their day-to-day. >> What's the difference? You were a data scientist by title and function, now you're a solutions engineer. Talk about the ascendancy into that and also some of the things that you and Tracy will talk about as those transferable, those transportable skills that probably maybe you learned in engineering, you brought data science now you're bringing to solutions engineering. >> Yeah, absolutely. So data science, I love working with data. I love getting in the weeds of things and I love, oftentimes that means debugging things or looking line by line at your code and trying to make it better. I found that on in the data science role, while those things I really loved, sometimes it also meant that I didn't, couldn't see or didn't have visibility into the broader picture of well like, well why are we doing this project? And who is it impacting? And because oftentimes your day-to-day is very much in the weeds. And so, I moved into sales or solutions engineering at Dataiku to get that perspective, because what a sales engineer does is support the sale from a technical perspective. And so, you really truly understand well, what is the customer looking for and what is going to influence them to make a purchase? And how do you tell the story of the impact of data? Because oftentimes they need to quantify well, if I purchase a software like Dataiku then I'm able to build this project and make this X impact on the business. And that is really powerful. That's where the storytelling comes in and that I feel like a lot of what we've been hearing today about connecting data with people who can actually do something with that data. That's really the bridge that we as sales engineers are trying to connect in that sales process. >> It's all about connectivity, isn't it? >> Yeah, definitely. We were talking about this earlier that it's about making impact and it's about people who we are analyzing data is like influencing. And I saw that one of the keywords or one of the biggest thing at Dataiku is everyday AI, so I wanted to just ask, could you please talk more about how does that weave into the problem solving and then day-to-day making an impact process? >> Yes, so I started working on Dataiku around three years ago and I fell in love with the product itself. The product that we have is we allow for people with different backgrounds. If you're coming from a data analyst background, data science, data engineering, maybe you are more of like a business subject matter expert, to all work in one unified central platform, one user interface. And why that's powerful is that when you're working with data, it's not just that data scientist working on their own and their own computer coding. We've heard today that it's all about connecting the data scientists with those business people, with maybe the data engineers and IT people who are actually going to put that model into production or other folks. And so, they all use different languages. Data scientists might use Python and R, your business people are using PowerPoint and Excel, everyone's using different tools. How do we bring them all in one place so that you can have conversations faster? So the business people can understand exactly what you're building with the data and can get their hands on that data and that model prediction faster. So that's what Dataiku does. That's the product that we have. And I completely forgot your question, 'cause I got so invested in talking about this. Oh, everyday AI. Yeah, so the goal of of Dataiku is really to allow for those maybe less technical people with less traditional data science backgrounds. Maybe they're data experts and they understand the data really well and they've been working in SQL for all their career. Maybe they're just subject matter experts and want to get more into working with data. We allow those people to do that through our no and low-code tools within our platform. Platform is very visual as well. And so, I've seen a lot of people learn data science, learn machine learning by working in the tool itself. And that's sort of, that's where everyday AI comes in, 'cause we truly believe that there are a lot of, there's a lot of unutilized expertise out there that we can bring in. And if we did give them access to data, imagine what we could do in the kind of work that they can do and become empowered basically with that. >> Yeah, we're just scratching the surface. I find data science so fascinating, especially when you talk about some of the real world applications, police violence, health inequities, climate change. Here we are in California and I don't know if you know, we're experiencing an atmospheric river again tomorrow. Californians and the rain- >> Storm is coming. >> We are not good... And I'm a native Californian, but we all know about climate change. People probably don't associate all of the data that is helping us understand it, make decisions based on what's coming what's happened in the past. I just find that so fascinating. But I really think we're truly at the beginning of really understanding the impact that being data-driven can actually mean whether you are investigating climate change or police violence or health inequities or your a grocery store that needs to become data-driven, because your consumer is expecting a personalized relevant experience. I want you to offer me up things that I know I was doing online grocery shopping, yesterday, I just got back from Europe and I was so thankful that my grocer is data-driven, because they made the process so easy for me. And but we have that expectation as consumers that it's going to be that easy, it's going to be that personalized. And what a lot of folks don't understand is the data the democratization of data, the AI that's helping make that a possibility that makes our lives easier. >> Yeah, I love that point around data is everywhere and the more we have, the actually the more access we actually are providing. 'cause now compute is cheaper, data is literally everywhere, you can get access to it very easily. And so, I feel like more people are just getting themselves involved and that's, I mean this whole conference around just bringing more women into this industry and more people with different backgrounds from minority groups so that we get their thoughts, their opinions into the work is so important and it's becoming a lot easier with all of the technology and tools just being open source being easier to access, being cheaper. And that I feel really hopeful about in this field. >> That's good. Hope is good, isn't it? >> Yes, that's all we need. But yeah, I'm glad to see that we're working towards that direction. I'm excited to see what lies in the future. >> We've been talking about numbers of women, percentages of women in technical roles for years and we've seen it hover around 25%. I was looking at some, I need to AnitaB.org stats from 2022 was just looking at this yesterday and the numbers are going up. I think the number was 26, 27.6% of women in technical roles. So we're seeing a growth there especially over pre-pandemic levels. Definitely the biggest challenge that still seems to be one of the biggest that remains is attrition. I would love to get your advice on what would you tell your younger self or the previous prior generation in terms of having the confidence and the courage to pursue engineering, pursue data science, pursue a technical role, and also stay in that role so you can be one of those females on stage that we saw today? >> Yeah, that's the goal right there one day. I think it's really about finding other people to lift and mentor and support you. And I talked to a bunch of people today who just found this conference through Googling it, and the fact that organizations like this exist really do help, because those are the people who are going to understand the struggles you're going through as a woman in this industry, which can get tough, but it gets easier when you have a community to share that with and to support you. And I do want to definitely give a plug to the WIDS@Dataiku team. >> Talk to us about that. >> Yeah, I was so fortunate to be a WIDS ambassador last year and again this year with Dataiku and I was here last year as well with Dataiku, but we have grown the WIDS effort so much over the last few years. So the first year we had two events in New York and also in London. Our Dataiku's global. So this year we additionally have one in the west coast out here in SF and another one in Singapore which is incredible to involve that team. But what I love is that everyone is really passionate about just getting more women involved in this industry. But then also what I find fortunate too at Dataiku is that we have a strong female, just a lot of women. >> Good. >> Yeah. >> A lot of women working as data scientists, solutions engineer and sales and all across the company who even if they aren't doing data work in a day-to-day, they are super-involved and excited to get more women in the technical field. And so. that's like our Empower group internally that hosts events and I feel like it's a really nice safe space for all of us to speak about challenges that we encounter and feel like we're not alone in that we have a support system to make it better. So I think from a nutrition standpoint every organization should have a female ERG to just support one another. >> Absolutely. There's so much value in a network in the community. I was talking to somebody who I'm blanking on this may have been in Barcelona last week, talking about a stat that showed that a really high percentage, 78% of people couldn't identify a female role model in technology. Of course, Sheryl Sandberg's been one of our role models and I thought a lot of people know Sheryl who's leaving or has left. And then a whole, YouTube influencers that have no idea that the CEO of YouTube for years has been a woman, who has- >> And she came last year to speak at WIDS. >> Did she? >> Yeah. >> Oh, I missed that. It must have been, we were probably filming. But we need more, we need to be, and it sounds like Dataiku was doing a great job of this. Tracy, we've talked about this earlier today. We need to see what we can be. And it sounds like Dataiku was pioneering that with that ERG program that you talked about. And I completely agree with you. That should be a standard program everywhere and women should feel empowered to raise their hand ask a question, or really embrace, "I'm interested in engineering, I'm interested in data science." Then maybe there's not a lot of women in classes. That's okay. Be the pioneer, be that next Sheryl Sandberg or the CTO of ChatGPT, Mira Murati, who's a female. We need more people that we can see and lean into that and embrace it. I think you're going to be one of them. >> I think so too. Just so that young girls like me like other who's so in school, can see, can look up to you and be like, "She's my role model and I want to be like her. And I know that there's someone to listen to me and to support me if I have any questions in this field." So yeah. >> Yeah, I mean that's how I feel about literally everyone that I'm surrounded by here. I find that you find role models and people to look up to in every conversation whenever I'm speaking with another woman in tech, because there's a journey that has had happen for you to get to that place. So it's incredible, this community. >> It is incredible. WIDS is a movement we're so proud of at theCUBE to have been a part of it since the very beginning, since 2015, I've been covering it since 2017. It's always one of my favorite events. It's so inspiring and it just goes to show the power that data can have, the influence, but also just that we're at the beginning of uncovering so much. Jacqueline's been such a pleasure having you on theCUBE. Thank you. >> Thank you. >> For sharing your story, sharing with us what Dataiku was doing and keep going. More power to you girl. We're going to see you up on that stage one of these years. >> Thank you so much. Thank you guys. >> Our pleasure. >> Our pleasure. >> For our guests and Tracy Zhang, this is Lisa Martin, you're watching theCUBE live at WIDS '23. #EmbraceEquity is this year's International Women's Day theme. Stick around, our next guest joins us in just a minute. (upbeat music)

(ambient music) >> Good morning, everyone. Lisa Martin with theCUBE, live at the eighth Annual Women in Data Science Conference. This is one of my absolute favorite events of the year. We engage with tons of great inspirational speakers, men and women, and what's happening with WiDS is a global movement. I've got two fabulous co-hosts with me today that you're going to be hearing and meeting. Please welcome Tracy Zhang and Hannah Freitag, who are both from the sata journalism program, master's program, at Stanford. So great to have you guys. >> So excited to be here. >> So data journalism's so interesting. Tracy, tell us a little bit about you, what you're interested in, and then Hannah we'll have you do the same thing. >> Yeah >> Yeah, definitely. I definitely think data journalism is very interesting, and in fact, I think, what is data journalism? Is definitely one of the big questions that we ask during the span of one year, which is the length of our program. And yeah, like you said, I'm in this data journalism master program, and I think coming in I just wanted to pivot from my undergrad studies, which is more like a traditional journalism, into data. We're finding stories through data, so that's why I'm also very excited about meeting these speakers for today because they're all, they have different backgrounds, but they all ended up in data science. So I think they'll be very inspirational and I can't wait to talk to them. >> Data in stories, I love that. Hannah, tell us a little bit about you. >> Yeah, so before coming to Stanford, I was a research assistant at Humboldt University in Berlin, so I was in political science research. And I love to work with data sets and data, but I figured that, for me, I don't want this story to end up in a research paper, which is only very limited in terms of the audience. And I figured, okay, data journalism is the perfect way to tell stories and use data to illustrate anecdotes, but to make it comprehensive and accessible for a broader audience. So then I found this program at Stanford and I was like, okay, that's the perfect transition from political science to journalism, and to use data to tell data-driven stories. So I'm excited to be in this program, I'm excited for the conference today and to hear from these amazing women who work in data science. >> You both brought up great points, and we were chatting earlier that there's a lot of diversity in background. >> Tracy: Definitely. >> Not everyone was in STEM as a young kid or studied computer science. Maybe some are engineering, maybe some are are philosophy or economic, it's so interesting. And what I find year after year at WiDS is it brings in so much thought diversity. And that's what being data-driven really demands. It demands that unbiased approach, that diverse, a spectrum of diverse perspectives, and we definitely get that at WiDS. There's about 350 people in person here, but as I mentioned in the opening, hundreds of thousands will engage throughout the year, tens of thousands probably today at local events going on across the globe. And it just underscores the importance of every organization, whether it's a bank or a grocer, has to be data-driven. We have that expectation as consumers in our consumer lives, and even in our business lives, that I'm going to engage with a business, whatever it is, and they're going to know about me, they're going to deliver me a personalized experience that's relevant to me and my history. And all that is powered by data science, which is I think it's fascinating. >> Yeah, and the great way is if you combine data with people. Because after all, large data sets, they oftentimes consist of stories or data that affects people. And to find these stories or advanced research in whatever fields, maybe in the financial business, or in health, as you mentioned, the variety of fields, it's very powerful, powerful tool to use. >> It's a very power, oh, go ahead Tracy. >> No, definitely. I just wanted to build off of that. It's important to put a face on data. So a dataset without a name is just some numbers, but if there's a story, then I think it means something too. And I think Margot was talking about how data science is about knowing or understanding the past, I think that's very interesting. That's a method for us to know who we are. >> Definitely. There's so many opportunities. I wanted to share some of the statistics from AnitaB.org that I was just looking at from 2022. We always talk at events like WiDS, and some of the other women in tech things, theCUBE is very much pro-women in tech, and has been for a very long, since the beginning of theCUBE. But we've seen the numbers of women technologists historically well below 25%, and we see attrition rates are high. And so we often talk about, well, what can we do? And part of that is raising the awareness. And that's one of the great things about WiDS, especially WiDS happening on International Women's Day, today, March 8th, and around event- >> Tracy: A big holiday. >> Exactly. But one of the nice things I was looking at, the AnitaB.org research, is that representation of tech women is on the rise, still below pre-pandemic levels, but it's actually nearly 27% of women in technical roles. And that's an increase, slow increase, but the needle is moving. We're seeing much more gender diversity across a lot of career levels, which is exciting. But some of the challenges remain. I mean, the representation of women technologists is growing, except at the intern level. And I thought that was really poignant. We need to be opening up that pipeline and going younger. And you'll hear a lot of those conversations today about, what are we doing to reach girls in grade school, 10 year olds, 12 year olds, those in high school? How do we help foster them through their undergrad studies- >> And excite them about science and all these fields, for sure. >> What do you think, Hannah, on that note, and I'll ask you the same question, what do you think can be done? The theme of this year's International Women's Day is Embrace Equity. What do you think can be done on that intern problem to help really dial up the volume on getting those younger kids interested, one, earlier, and two, helping them stay interested? >> Yeah. Yeah, that's a great question. I think it's important to start early, as you said, in school. Back in the day when I went to high school, we had this one day per year where we could explore as girls, explore a STEM job and go into the job for one day and see how it's like to work in a, I dunno, in IT or in data science, so that's a great first step. But as you mentioned, it's important to keep girls and women excited about this field and make them actually pursue this path. So I think conferences or networking is very powerful. Also these days with social media and technology, we have more ability and greater ways to connect. And I think we should even empower ourselves even more to pursue this path if we're interested in data science, and not be like, okay, maybe it's not for me, or maybe as a woman I have less chances. So I think it's very important to connect with other women, and this is what WiDS is great about. >> WiDS is so fantastic for that network effect, as you talked about. It's always such, as I was telling you about before we went live, I've covered five or six WiDS for theCUBE, and it's always such a day of positivity, it's a day of of inclusivity, which is exactly what Embrace Equity is really kind of about. Tracy, talk a little bit about some of the things that you see that will help with that hashtag Embrace Equity kind of pulling it, not just to tech. Because we're talking and we saw Meta was a keynote who's going to come to talk with Hannah and me in a little bit, we see Total Energies on the program today, we see Microsoft, Intuit, Boeing Air Company. What are some of the things you think that can be done to help inspire, say, little Tracy back in the day to become interested in STEM or in technology or in data? What do you think companies can and should be doing to dial up the volume for those youngsters? >> Yeah, 'cause I think somebody was talking about, one of the keynote speakers was talking about how there is a notion that girls just can't be data scientists. girls just can't do science. And I think representation definitely matters. If three year old me see on TV that all the scientists are women, I think I would definitely have the notion that, oh, this might be a career choice for me and I can definitely also be a scientist if I want. So yeah, I think representation definitely matters and that's why conference like this will just show us how these women are great in their fields. They're great data scientists that are bringing great insight to the company and even to the social good as well. So yeah, I think that's very important just to make women feel seen in this data science field and to listen to the great woman who's doing amazing work. >> Absolutely. There's a saying, you can't be what you can't see. >> Exactly. >> And I like to say, I like to flip it on its head, 'cause we can talk about some of the negatives, but there's a lot of positives and I want to share some of those in a minute, is that we need to be, that visibility that you talked about, the awareness that you talked about, it needs to be there but it needs to be sustained and maintained. And an organization like WiDS and some of the other women in tech events that happen around the valley here and globally, are all aimed at raising the profile of these women so that the younger, really, all generations can see what they can be. We all, the funny thing is, we all have this expectation whether we're transacting on Uber ride or we are on Netflix or we're buying something on Amazon, we can get it like that. They're going to know who I am, they're going to know what I want, they're going to want to know what I just bought or what I just watched. Don't serve me up something that I've already done that. >> Hannah: Yeah. >> Tracy: Yeah. >> So that expectation that everyone has is all about data, though we don't necessarily think about it like that. >> Hannah: Exactly. >> Tracy: Exactly. >> But it's all about the data that, the past data, the data science, as well as the realtime data because we want to have these experiences that are fresh, in the moment, and super relevant. So whether women recognize it or not, they're data driven too. Whether or not you're in data science, we're all driven by data and we have these expectations that every business is going to meet it. >> Exactly. >> Yeah. And circling back to young women, I think it's crucial and important to have role models. As you said, if you see someone and you're younger and you're like, oh I want to be like her. I want to follow this path, and have inspiration and a role model, someone you look up to and be like, okay, this is possible if I study the math part or do the physics, and you kind of have a goal and a vision in mind, I think that's really important to drive you. >> Having those mentors and sponsors, something that's interesting is, I always, everyone knows what a mentor is, somebody that you look up to, that can guide you, that you admire. I didn't learn what a sponsor was until a Women in Tech event a few years ago that we did on theCUBE. And I was kind of, my eyes were open but I didn't understand the difference between a mentor and a sponsor. And then it got me thinking, who are my sponsors? And I started going through LinkedIn, oh, he's a sponsor, she's a sponsor, people that help really propel you forward, your recommenders, your champions, and it's so important at every level to build that network. And we have, to your point, Hannah, there's so much potential here for data drivenness across the globe, and there's so much potential for women. One of the things I also learned recently , and I wanted to share this with you 'cause I'm not sure if you know this, ChatGPT, exploding, I was on it yesterday looking at- >> Everyone talking about it. >> What's hot in data science? And it was kind of like, and I actually asked it, what was hot in data science in 2023? And it told me that it didn't know anything prior to 2021. >> Tracy: Yes. >> Hannah: Yeah. >> So I said, Oh, I'm so sorry. But everyone's talking about ChatGPT, it is the most advanced AI chatbot ever released to the masses, it's on fire. They're likening it to the launch of the iPhone, 100 million-plus users. But did you know that the CTO of ChatGPT is a woman? >> Tracy: I did not know, but I learned that. >> I learned that a couple days ago, Mira Murati, and of course- >> I love it. >> She's been, I saw this great profile piece on her on Fast Company, but of course everything that we're hearing about with respect to ChatGPT, a lot on the CEO. But I thought we need to help dial up the profile of the CTO because she's only 35, yet she is at the helm of one of the most groundbreaking things in our lifetime we'll probably ever see. Isn't that cool? >> That is, yeah, I completely had no idea. >> I didn't either. I saw it on LinkedIn over the weekend and I thought, I have to talk about that because it's so important when we talk about some of the trends, other trends from AnitaB.org, I talked about some of those positive trends. Overall hiring has rebounded in '22 compared to pre-pandemic levels. And we see also 51% more women being hired in '22 than '21. So the data, it's all about data, is showing us things are progressing quite slowly. But one of the biggest challenges that's still persistent is attrition. So we were talking about, Hannah, what would your advice be? How would you help a woman stay in tech? We saw that attrition last year in '22 according to AnitaB.org, more than doubled. So we're seeing women getting into the field and dropping out for various reasons. And so that's still an extent concern that we have. What do you think would motivate you to stick around if you were in a technical role? Same question for you in a minute. >> Right, you were talking about how we see an increase especially in the intern level for women. And I think if, I don't know, this is a great for a start point for pushing the momentum to start growth, pushing the needle rightwards. But I think if we can see more increase in the upper level, the women representation in the upper level too, maybe that's definitely a big goal and something we should work towards to. >> Lisa: Absolutely. >> But if there's more representation up in the CTO position, like in the managing level, I think that will definitely be a great factor to keep women in data science. >> I was looking at some trends, sorry, Hannah, forgetting what this source was, so forgive me, that was showing that there was a trend in the last few years, I think it was Fast Company, of more women in executive positions, specifically chief operating officer positions. What that hasn't translated to, what they thought it might translate to, is more women going from COO to CEO and we're not seeing that. We think of, if you ask, name a female executive that you'd recognize, everyone would probably say Sheryl Sandberg. But I was shocked to learn the other day at a Women in Tech event I was doing, that there was a survey done by this organization that showed that 78% of people couldn't identify. So to your point, we need more of them in that visible role, in the executive suite. >> Tracy: Exactly. >> And there's data that show that companies that have women, companies across industries that have women in leadership positions, executive positions I should say, are actually more profitable. So it's kind of like, duh, the data is there, it's telling you this. >> Hannah: Exactly. >> Right? >> And I think also a very important point is work culture and the work environment. And as a woman, maybe if you enter and you work two or three years, and then you have to oftentimes choose, okay, do I want family or do I want my job? And I think that's one of the major tasks that companies face to make it possible for women to combine being a mother and being a great data scientist or an executive or CEO. And I think there's still a lot to be done in this regard to make it possible for women to not have to choose for one thing or the other. And I think that's also a reason why we might see more women at the entry level, but not long-term. Because they are punished if they take a couple years off if they want to have kids. >> I think that's a question we need to ask to men too. >> Absolutely. >> How to balance work and life. 'Cause we never ask that. We just ask the woman. >> No, they just get it done, probably because there's a woman on the other end whose making it happen. >> Exactly. So yeah, another thing to think about, another thing to work towards too. >> Yeah, it's a good point you're raising that we have this conversation together and not exclusively only women, but we all have to come together and talk about how we can design companies in a way that it works for everyone. >> Yeah, and no slight to men at all. A lot of my mentors and sponsors are men. They're just people that I greatly admire who saw raw potential in me 15, 18 years ago, and just added a little water to this little weed and it started to grow. In fact, theCUBE- >> Tracy: And look at you now. >> Look at me now. And theCUBE, the guys Dave Vellante and John Furrier are two of those people that are sponsors of mine. But it needs to be diverse. It needs to be diverse and gender, it needs to include non-binary people, anybody, shouldn't matter. We should be able to collectively work together to solve big problems. Like the propaganda problem that was being discussed in the keynote this morning with respect to China, or climate change. Climate change is a huge challenge. Here, we are in California, we're getting an atmospheric river tomorrow. And Californians and rain, we're not so friendly. But we know that there's massive changes going on in the climate. Data science can help really unlock a lot of the challenges and solve some of the problems and help us understand better. So there's so much real-world implication potential that being data-driven can really lead to. And I love the fact that you guys are studying data journalism. You'll have to help me understand that even more. But we're going to going to have great conversations today, I'm so excited to be co-hosting with both of you. You're going to be inspired, you're going to learn, they're going to learn from us as well. So let's just kind of think of this as a community of men, women, everything in between to really help inspire the current generations, the future generations. And to your point, let's help women feel confident to be able to stay and raise their hand for fast-tracking their careers. >> Exactly. >> What are you guys, last minute, what are you looking forward to most for today? >> Just meeting these great women, I can't wait. >> Yeah, learning from each other. Having this conversation about how we can make data science even more equitable and hear from the great ideas that all these women have. >> Excellent, girls, we're going to have a great day. We're so glad that you're here with us on theCUBE, live at Stanford University, Women in Data Science, the eighth annual conference. I'm Lisa Martin, my two co-hosts for the day, Tracy Zhang, Hannah Freitag, you're going to be seeing a lot of us, we appreciate. Stick around, our first guest joins Hannah and me in just a minute. (ambient music)

(intense music) >> Hi and welcome to theCUBE, I'm Lisa Martin coming to you from our Palo Alto studio. Very excited to be joined by a CUBE alumni, the CEO and president of Anita Borg, Brenda Darden Wilkerson. Welcome back to theCUBE, Brenda. >> Thanks so much for having me. >> It's great to have you here. You have been at Anita Borg for about six months. You've got a great background in the tech industry and in education. Give us some perspective of what's happening, what's new with Anita Borg? >> Well, we are very excited to be in this space at this point in history. It's very exciting. Women are alive to the possibilities of what they can contribute in tech. We can thank so many important women who are contributing to the conversation, and it is our job to make sure that they have a voice. And so we are working really hard to make sure that the perceptions that would create barriers for women contributing to tech, having a career path, taking those really important positions of power in tech, that we obliterate them and that the flood gates are open for all who want to participate. >> I love that on the website I saw the what we do, one of the things that shattering perceptions. And I thought that word shattering, that description, was really, really important. >> It is very important because you would think in 2018 that these issues that our founder, Anita Borg, talked about years and years ago, I mean she was a visionary, when she said 50/50 by 2020. And actually we are coming back from the cliff that we fell off of in terms of being our percentages in tech. We're at about 22% now, and a lot of that has to do with those perceptions. What are the images that young women see? Or people in power in tech. What are those images that continue to contribute to those barriers, and that's first and foremost the thing that we're working to change. >> When you were on theCUBE at Grace Hopper 2017, just six months or so ago, one of the things that you said that I really love was people can be what they can see. So having awareness and showing females in technology and leadership positions, showing people this power of representation is critical. >> Very much, very much so. And really all we're talking about is telling the truth. You know? It's not as though the women haven't always been there. It's not as though they aren't making huge contributions, it's just making sure that when they do the work they get the credit for it and that people get to see it. I've seen it be very important in my previous work in driving computer science. All of the stakeholders needed to understand that underrepresented people of all kinds could do tech, and they were very much impacted by the images that they saw. And so it's our job to make sure that all of those stories get told. >> So you spent 15 years in education, and you had many years before that in tech. You made a massive impact with the Computer Science For All initiative that you founded back in 2013 in Chicago. Tell us a little bit about that because it's really exploded and I'm sure really kind of exceeded your expectations. Tell us about that initiative and where it is currently today. >> I'm very excited about the initiative. I mean, really it was born out of some of my own experiences. I was a person who, in my background, I wasn't exposed to computer science until I found it accidentally in college. I mean, obviously that accident changed my whole trajectory, right? So when I found out that that was still happening to women and underrepresented students when I got into education, that was sort of the genesis of wanting to do something about it. That was when we launched Computer Science For All. And yes, now it is a national initiative. In Chicago we have a graduation requirement. Students all have to graduate with at least one year of computer science, and we're seeing that transformation. I've got students who we started with in the beginning who are graduating this year from universities with computer science, data science, information science degrees, and they are doing amazing things. They're starting companies, they're developing products, all because they had that exposure. And so it's exciting now to be on the other side, really kind of coming home full circle, back to advocate for women in tech, as I started out. To make sure that those hundreds of thousands, millions of students have access to the opportunities that we need them to have access to. >> Right, that access is such a critical thing. And you kind of think in some respects, as we were talking about earlier, you've made a massive impact in Chicago, New York City, the Obama administration got behind this. Well, you started out with a goal of reaching four hundred thousand kids in Chicago, there's now over 1.5 million. But it starts with that awareness, that this shouldn't be an elective. But kids need access to understand I can be what I can see. If I can't see it, I don't know that it's an opportunity. >> And if I don't know, if I can't touch it and know that I have access to being the creator of technology, changing the world as we know technology alone can do, then we're going to miss out on the contributions that only they can make, and so that's what makes this so exciting. When we started out, I'm thinking of the kindergartners that started that first year. They're in fourth grade now, right? What is the world going to look like when they graduate from high school? It's just going to be amazing, I can't wait. >> Yeah, we were just covering Women In Data Science a couple of weeks ago, I was mentioning to you before, and I love that event because you walk into where the main event at Stanford is held, and you just instantly feel positivity, excitement of this movement. And there's so much opportunity within data science alone, and one of the things I wanted to talk with you about is we heard a lot of people that were guests that day talk about the creative element. And we often think of the hard skills that computer scientists and data science need to have, but you found CS accidentally as you said, and one of the things that I've heard you say is the opportunity to be creative. Tell us a little bit more about what, how people, young girls can get creative and expressive creativity through computer science. >> Well, that's very important. We found that we could attract more girls into computer science when we told them that they could use these skills and this knowledge to solve problems that they cared about. You know, initially because it was such a, thought to be such a male-oriented subject, it was all about computer games and the kill games, and the girls were like, I'm not interested in that, but I want to do things that are impactful to the world, to change my society, to change my community, and you can do that with technology, and you can create something out of your own ideas from scratch, from concept, and I can see the lights go on for them, wait, I can create an app that helps my friends through a particularly difficult time with bullies. Yes, you can do that. And so, that is the exciting explosion that's about to happen. People who are really using these skills to solve problems for the human good, that's what we're going to see an increase of, because that's what many times the women bring. So Grace Hopper 2018 is coming up, what in September? >> It's September 26th through 28th this year, and it's in Houston, we're returning to Houston. We're actually even going to use the Toyota Center for our keynotes. >> And you're expecting 20 thousand people this year? >> That's right, we had 18 thousand last year, we're inviting 20 thousand this year. We're going to have over 17 tracks. Last year we had 405 concurrent sessions. The whole point is to give women an idea of how they can transform their lives, coming into technology at whatever stage they found themselves in, whether they are just seekers and are interested in learning about technology, or if they are middle career and going to that next stage or the executive level, we have something for all of them. >> So, and you give out awards, the Abie awards, at Grace Hopper. Give us a little bit of an idea of the types of categories in which women are awarded. >> So we award the top innovator, we award top educator, so wherever women find themselves, we want to bring attention to the fact that we need participation not in just what we think as the high-tech sector, but all along the pathway. People who are bringing attention to issues using technology in their community. We award all of those, people who participate in creating more of a well-rounded experience for all of us to understand what technology can do for our lives. >> And it's really everywhere, right? And that's one of the thing that I think is personally really intriguing about technology is every company now has to be a tech company. >> That's right, every company is a tech company, right? And so that's another thing that we want to make sure that people are not just thinking, oh if I'm going to get in tech I can just work for these five or 10 high-tech companies. Tech is everywhere, it's across the country, it's around the world, it's right where women are living and having their existence. And we need their contribution in those places. >> Yeah, another thing about WIDS and women, we were talking about data science that I found interesting, was some of these female leaders talking about the hard skills, the data analysis, the interpretation, but also needing to have more diversity in the analysis to remove, we all kind of come with biases, but to start having more female perspectives to really kind of open up the analyses and remove some of the biases, which was kind of something, to be honest, I've been in tech for a long time, I hadn't really thought about before. >> Yeah, and it's really shocking just how impactful some of those biases are in the data on people's everyday lives. We've heard things everywhere from as serious as different sentencing levels for people based upon the algorithms that are there, to how much things can cost more for important things like insurance, based upon the data that's there. I think the New York Times did a piece a couple weeks ago about face recognition software and those images that are in those databases. And so it's so important that we have diverse faces at the table, as a black woman, my face is likely to be misunderstood 37% of the time. Right? So to be able to have the diverse background there that will check for those images to make sure that they are more representative of the whole population is just going to make all of our lives better. >> So at Grace Hopper your audience is made up of girls maybe interested in STEM, women that you said are in many stages of their careers, on the corporate side, one of the things I read recently is that article that you wrote in Mashable called Voices of Women in Tech collaboration with Anita Borg where you talked about corporate activism, and there's some pretty significant benefits that companies can achieve by speaking out. Tell us a little bit more about that. >> Well, you know, we have a much more engaged and active population, especially the millennials, and they care what their companies care about and how they contribute or don't contribute to the causes that they care about. And so one of the most expensive things that a company will ever experience is their ability to retain great talent. And what we've seen is that millennials will decide to stay or leave based upon some of the things that companies contribute to or don't contribute to, so being able to pay attention and to get into the game of other things that are outside just the product that they produce, actually contributed to company's bottom line. >> That's pretty interesting. >> It's very interesting and very important, and knowing that is something that they can immediately put in place that impacts the success of their company. >> Absolutely, and some of the things, too, that I've heard on various CUBE shows that we've done is the millennials perspective on the gender gap. And often they'll go, I don't know why you guys are still talking about this. And we think, we don't either, but we are, and it's refreshing to hear that this next generation thinks that that is just something that is just kind of ridiculous that we're still talking about, and also how important seeing a leader, a CEO being involved in something important, is to retention, so I think that's a great message that Anita Borg can help get out there and show businesses this huge impact and benefit to you and fostering your own talent. >> Yeah, you know, and it's encouraging, as you say, the millennials are jumping in, and many more people are jumping in and giving this perspective to companies, which is actually assisting them, right? So now they don't have to feel like, okay, this is just my idea, I'm going to take a risk and jump out. They've got people who are loyal to their organization saying, I believe in this and I believe in you, let's do this together, and so definitely our job is to make sure that companies have access to all the information they need to make these, what shouldn't be hard decisions, but we're there to help them. So the 50/50 idea, you have said that, and you mentioned that earlier, that you want to see 50/50 representation of females in the next 10 years. Tell us a little bit more about kind of what's coming out the rest of 2018 from Anita Borg, and how you guys are working to help make that, help get those numbers up from where they are currently. >> So it's all about awareness, and there's a lot of awareness out there, but what we want to do is increase it. You mentioned the idea of people can't be what they can't see. Images are so powerful, and so we want to work with media outlets, we want to work with entertainment companies with writers, with producers, and say help us create the images that can turn around and tell the truth, really. I mean we're not creating a fiction. Let's just tell the truth and allow people to understand that yes, this is how this works, and let's couple that with the data that shows that the bottom lines of companies that have more diverse workforces, that have more diverse boards, are muchly improved over those that are nondiverse. And so, we are creating that awareness, we are helping our companies find out what we call, not only best practices, but many times it's better practices. We're still working towards that best practice of here's how you can make incremental steps forward. Excuse me, you mentioned 10 years, I'm a little more urgent than that. I feel like the things that we get done are the things that we're most urgent about. One of the issues about why we're still dealing with these things, it's just been sort of like let's work on it in the sweet by and by. I want to say, let's work on it in the next two years, in the next three years. Let's make some goals, let's put some metrics behind them, and those were some of the things that we help companies do. >> I love that urgency. I think it's essential, but the awareness and kind of this idea that you have of, let's just tell the truth. There's really nothing more powerful than that. But also, the imagery and the representation is critical for that. If you look back at all of your success and think back to younger Brenda, what advice would you give somebody that looks at you and goes, wow. Where do I start? What's that recommendation for shattering someone's own maybe perception of themselves in getting into technology? >> Right, I mean we have to start with the conversation that we have with ourselves, but you know, we're in this world now where there's so many great images. Find those images. You know, you can find successful women. There are so many of them. Talk to them, reach out to those of us, because we want you to succeed. We want you to participate and come on board. And so, we have a world with social media that allows people to have access to each other that we didn't have before. The most important thing is don't take no for an answer. Not only because it's just not true, but because we need you, and it is an amazing time right now where you have all these women who are standing up saying that they want change, and we're here to support them, and we're here to support you. >> Speaking of this kind of movement going on globally about we want change, with the Me Too movement, a bit of a different genesis, however, the awareness is starting to be there. You talked about needing the entertainment industry to get on board and really start ensuring that we're sharing the truth here. What opportunities do you see to deliver through Anita Borg that maybe you can leverage that's coming from the Me Too movement and all of Hollywood that's really starting to stand up and be very vocal about this? >> Well, you know, it's interesting because people ask me that question a lot, and from my own perspective, there's this awakening because those same sorts of things are happening in tech as well, we know. We've seen the stories. It's not as though we're looking aghast at what's happening to women across the way. So these things have been happening, and what is happening is people are starting to look internally to say, how can I strengthen myself and stand up like these women did? And so at AnitaB.org we are creating those opportunities for women to network, for them to get mentored. We have communities around the world where women can get together and understand what the pathway was of other women. It would have been really helpful for me to have sort of some of those breadcrumbs out in front of me, some of those examples and other people to talk to, but we provide that as part of what we do in our organization. We provide training opportunities, other experiences where people can see all across the tech ecosystem where they can come in. It's not just one way in, it's not just one pathway, and so that's going to be a really important thing to make sure that women know they have choices. >> And I think it's so important in general, but you mentioned some of the attendees at Grace Hopper are maybe women who are in transition who are maybe, had a career in something different for a while, and are now getting into tech. I'd love to maybe understand that a little bit more, maybe some of the demographics there, but how do you see, what are some of the inspirational stories maybe that you can see where a woman who was maybe mid-career or somewhere around there, just went, you know what, I am interested in this, maybe didn't have the confidence when she was younger. Any stories there that kind of jump out at you that are great examples of it's never too late? >> Absolutely, in fact that was some of my first inspiration in getting involved with taking my background in tech and sort of lighting the path for people to get in who had traditionally been shut out. My first educational experience was at a community college level. And many of those people were people who, like myself, had not been given that introductory experience of computer science in K-12 space. Maybe they went to work and did some other things, maybe they got talked out of it. You know, it's not for you. And they came back later saying, you know, maybe I could learn, maybe I could try, and so really opening up that pathway to them. I've seen people who have gone from either having no education or maybe having even a PhD in linguistics figuring out once again that creativity. How can I take that and apply that technologically to creating solutions that only I would care about or know about? And so we've seen people come from all different walks of life, different career paths, and start small. Some of them are self trained, some of them are bootcamp trained, some of them go back and get an additional education. What we do at AnitaB.org is, not only help them understand those multiple pathways, but we work with partner companies to say, you know, there are other ways for people to come in. We've got these, what, 500, 600 thousand empty positions. Why don't you take a look at some of the people who are in your industry already? If you're a bank and you've got a woman who's been working for you for 20 years, she knows your business inside out, she is loyal, she can learn the tech. So we're seeing those types of transitions take place as well. >> Fantastic, well Grace Hopper in Houston in September. Is there also Grace Hopper, there are forums in other countries? >> Yes, so we also have Grace Hopper India that takes place in November, right after the one we do here in the United States. We've also started to have one day Grace Hopper events, we call them Hopper by Once, and we are planning those out around the world. And so we're increasing, we're trying to increase people's opportunity to come and experience all of the wonderful things that are available at Grace Hopper. We hear so many wonderful things about how it's transformative in their lives to see that many women in one place, to have access to training and mentoring and networking opportunities, and we're just excited for what's to come. >> Well we're excited to see what happens in the next few months, and Brenda thanks so much for stopping by, sharing what's new with AnitaB.org, your vision for that, and the transformation that you're already helping to facilitate. >> Thank you for having me. >> Absolutely our pleasure. We want to thank you for watching theCUBE, I'm Lisa Martin, from our Palo Alto studios, thanks for watching. We'll see you next time. (intense music)

(upbeat techno music) >> And welcome back everybody, Jeff Frick here with theCUBE. We're having a CUBE Conversation in our Palo Alto Studios. We're excited to have our next guest, who is an ABIE award winner from the Grace Hopper Celebration. Would've been competing in early October, we tried to get her on then, schedules didn't mesh so it took us a few months, but we're really excited to have our next guest. She's Mercedes Soria, she is a VP of Software Engineering for Knightscope. Mercedes, welcome. >> Thank you, thank you, I am so happy to be here. >> Absolutely, so, congratulations again on your award of leadership and part of the winnings of that is you got to keynote in front of 18,000 people. So A, What was your impression of Grace Hopper and B, how did you like keynoting in front of 18,000 folks? >> Yes, how was Grace Hopper, it was a huge community of women in technology. I was so excited to be there, everybody was just looking up to women, everybody was trying to help each other. How do you go forward in your career, and I was really focused on STEM careers, which is one of my passions. So I was so glad to be there. And how it was to keynote in front of 18,000 people, so I hadn't done that before, so I can check it off my bucket list, that was one thing. And it was amazing, there were so many women who just clapped and they just kept supporting it and I had to stop several times while I was giving the speech, so it was once in a lifetime opportunity that I'm very grateful for. >> It's an amazing accomplishment, again, congratulations, and it's amazing show, if you haven't been to Grace Hopper, you have to sign up, how fast you say it sold out? >> Mercedes: 25 minutes. >> 25 minutes, oh. Go to anitaborg. or anitab.org now, that's right, they changed the URL, yeah, I'll have to check it out. So let's jump in about Knightscope. So for the people who aren't familiar, go the website, knightscope.com, a bunch of really cool fun stuff, tell us about what Knightscope's all about. >> So Knightscope is a company that is trying to cut the crime cost to the US in half. So most people don't know that the US spends about one trillion dollars a year just to deal with crime in the US, so our goal at Knightscope is to cut that in half with the use of new technologies like artificial intelligence, machine learning, and robotics. A group is software plus hardware plus humans, so we take the good things that humans do, which is make strategic decisions, the good things that machines do, which is do the monotonous work and store data for a very long time, and we combine those to try to help with crime. >> Right, so that's a nice explanation. The short answer is, if you go to the website, it's all rolled up into these cool robots that look like C-3PO, and I'm wondering if there's a little man inside there, but we'll get into that later. But I think it's a really interesting concept because you are bringing together many of the hot topics in technology right now, so one of'em just with robotics. You got these robots of various shapes and sizes, but as you said, really, it's the synergy of the robots with the people that give kind of a one plus one makes three effect. How is it, where are those points of intersection, and how does the robot help the human do a better job, and how does the human help the robot do a better job? >> So the robot helps the human because, in this case, security guards have to walk around the same places all day long, right, they have their route, they do that all day long and they get very, very bored, and they get to the point where they don't care anymore and they just scan a badge and then that is the job, right? So that's what the robots do, which is, they don't mind going around the same area all day long, recording data, recording video. That's where the synergy is. Now what the robots, at this point, can do is make a decision in terms of, okay, I have this five things, should I make an alarm to my supervisor and say a guard needs to come. The robot only provides information, so all of that information that we provide is so the human can make a decision on what to do next. >> And does it feed into, I mean obviously these are big security systems that already exist inside these big buildings and these big facilities. Does your robot tie back into those facilities, is it a different layer on top of it, how does it work with the existing security infrastructure that's already in place? >> So the existing security infrastructure is a bit separate at this time. There is a project that we're working on in terms to integrate because there's so many security systems out there, for a start up like us, we need to be very smart in terms of where we spend our resources. So we got to do studies and figure out which were the better senders, the better companies that we need to partner with to do that. But at this point, it's a separate tool, so you open it and all the gear you need is a current browser, you can open it from anywhere in the world, and your security people can look at all the data the machine has collected. >> Right, so the other interesting piece that you're tying together via these machines is really this combination of AI and ML, artificial intelligence, machine learning, but also your background is in user interface, so it can't just be happening in the background because these machines need to do their job, executing through and with people, on the UI side and the security guards and the security infrastructure behind them. So as you've introduced more AI and machine learning into the software components that you can drive the UI, how is that changing the world, how is the UI world changing because now you've got so much more data and so much more kind of compute behind that before it even gets to the actual user that's interfacing with it? >> Yeah so the UI's a little more rich these days, it used to be a webpage and HTML and JavaScript page, and that's all it did, right, but now we have a lot more information that we can provide. For example, we have machine learning algorithms that detect if there's people in an image, so I don't only tell you this is my video, but I also give you a picture of the person that I just saw, and then I tell you, hey, this is what I saw. It makes your experience a lot more incursive. >> Right, and another potential integration point, right obviously with photos in the security system for IDs and passes and all those things. >> Yeah, even face detection at some point as well is very important for us. >> Now you have four different models, why do you have so many models, what's the use cases that would drive you to have four different models? Hard to support four models instead of one as a startup. >> Yeah so our customers have very different needs. Crime doesn't happen just in a shopping mall, crime happens at PG&E offices, it happens at the mall, it happens at different locations, it could be outside, it could be inside, it could be in a hospital, it can be in a parking lot, so what we tried to do was to cover all of those potential places where crime will be. So with that we have four products; we have the K5, which is our first product. It goes into ADA compliant environments like hospitals and data centers, it's a big robot and mainly used for things like a parking lot to detect license plates, to make sure that it's monitoring all the outside. Our second product is the K3 which is a smaller machine, and what it does is mainly goes inside, it can go through a door and it can do things like monitoring who's at the office at night, raising an alert if there was a fire, stuff that happens inside. We have the K7 which goes to outside places where you have things like speed bumps, you have different kind of terrain, gravel or other type. And then the K1 which is our static model that what we're working on that for the future is to have concealed weapon detection at that point, which is something that is very useful for places that have, like for hospitals, when somebody comes in, they want to be able to know if these people are armed. >> Right, I'm just curious if you can share where customers have seen the most impact, the most benefit by using one of your robots. What specific behaviors have just been a game changer when they put in the Knightscope robot? >> Yeah, so I can't tell you the actual customer, that is something >> No, no, that's okay. >> That we can't say, but I would tell you one example. We have, for example, a hospital and this place is open 24/7, obviously the emergency room, and when they will have, it's down in LA, so they will have at least one break-in every week at the parking lot. So we put our machines there and the past seven months that they have been there, they got zero, they got no break-ins. And the nurses now feel safer going to their cars, people feel safer going there at night, so that is one example. We also had an example of a shopping mall where there was a guy who was basically exposing himself and nobody could catch him because he would drive, as soon as he saw a security guard, he would drive out. So we were able to catch that person as well. There are some people to steal merchandise, so they came, they stole something, they left, and the very next day, they come back and they try to sell this back to the mall people, so by seeing who these people are then determining that they came back to the mall, we were able to apprehend them as criminals. >> Right, on the first example, on the parking lot example, does the robot have active deterrents that it can do, can it sound alarms, light lights, to make people feel safer in a parking lot, that's very different than just monitoring things? >> Yeah so what the robot does is, it has a sound that it's all day it's playing that sound, there's a lot of lights, the lights change color based on what's happening around the robot. Another thing that we have that helps a lot of people feel safe, we have a push-to-talk functionality, so if you were feeling something was wrong at night, you can push that button and you can directly talk to the people at the security operation center. They can walk you through what to do, they can follow you while you go to your car, there's different functionality that we have that helps people feel that they're safe outside. >> Right, and on the shoplifting one, it's interesting 'cause lots of stores have cameras, right, that's not a new thing. So what did your system do differently that the regular camera that they had in there before probably would've filmed the person but didn't necessarily wasn't firing off the alert, recognizing they were back again, did somebody go in and manually type in this particular person's a shoplifter. How did you guys take it to a much different level than just kind of a static security cam? >> So the main thing that you should keep in mind for static cameras is there's always black spots, blind spots, there's no way that they can see everything, and mainly you have cameras inside of the shops, you don't have them outside, so what we did is, we not only saw that we not only got the video of the person inside of the shop, but we saw them when they came outside, we saw them when they were moving, all of this is recorded in video and that we can then match them and see the people who were. Another thing that we do that cameras don't do is we can detect your mobile devices, anything that has that's looking for a network, we can identify that device, and that is always for you and that is always for that device, so we can match those devices when they come in. >> You shouldn't have waited this long but one of the most interesting things about the company and what you guys do, and it's highlighted by what you just said, is the way you go to market. People are not buying these robots, right, you offer the robots as a service, so really interesting model and really brings up interesting things like you said where you can do all kinds of software upgrades, you can do hardware upgrades, you can do all types of changes to the actual unit that the customer just benefits, it's a classic SAS model. So how did you get to that stage and how do people like having, now, kind of a simple monthly payment with all the upgrades and constant, I would imagine, a lot of upgrades coming pretty consistently? Pretty interesting way to go to market, how's that received in the market? >> It's very well, people really accepted, especially when it's new technology. We decided from the beginning that we wanted to be, to own the whole technology stack, and even the robot itself because we knew there would be a lot of upgrades, we knew there would be changes and we wanted to serve our customers in the very best way that was possible. So to help people adopt new technology, we help them with how do they perceive it on a daily basis. If you come to somebody and says they want you to buy a hundred thousand dollar robot, uh, you don't know what that's going to be, but if you said, I charge you ten dollars an hour and give you a robot, that not only changes software every other week, it changes hardware every six months, and you have whatever robot will fit your needs the best. People are really accepting of that model, to the point that all the companies are jumping into the same thing. >> It's really interesting because then it begs where you guys will develop as a company, you know, are you are robotics company, are you a software company, are you a software monitoring company, do you become really a security AI company that pulls from lots of different data and lots of different sources? It really opens up a broad range of opportunities for you guys in which you want to go or where you find your most expertise or where the market takes you. Pretty exciting way to go to market. >> Yeah so what we decided to was we wanted to be the Apple of security guards, so what Apple does is they have their software, their hardware, they own all of it, and therefore they have a very loyal following. We want to be that for security guards, so we own the whole environment, we make changes when we wanted to, and then we go to market that way. >> Okay, that's a great story and again it's knightscope.com, they're fun pictures for one, but it's a great story. But before I let you go, Telly would not be happy if I didn't take a few minutes to talk about your journey. How did you get here, VP of Software Engineering? You know, software's eating the world, it's a great place to be, you've got a solutions based system, but really it's a bunch of metal wrapped up with software inside. So how did you get here, and I wonder if you can share a little bit of your journey to become VP of Software Engineering? >> Yeah so I'm an immigrant, I'm not from the US. I was born South America, and when you're in South America and somebody tells you, hey there's an opportunity for you to go study in the US, you take that opportunity. So I came to the US to study for college, I had a Bachelors in Computer Science and then a Masters in Computer Science. >> Where did you go to school? >> I went to Middle Tennessee State University, and like I said, when somebody tells you, you're going to the US, you don't ask questions, you just go. >> So who made you that offer, how did that come about? >> My university in Ecuador, where I was from, they had an agreement with the university in Tennesee. So they would send students back and forth in an exchange program. >> So you're a good student, they identified you as having great potential and you got picked for that program? >> So 5,000 people apply for 20 spots when I applied. >> Wow. >> So 20 of us came, and out of the 20, the only two people who are staying in the US, my sister and I, we're twins, I have a twin sister. >> 'Cause you ask your sister for support, maybe? Twin sister. >> If I really, it probably had a lot to do with it. And then with technology, I found my way into Knightscope, and Knightscope is a really good company for women in technology specifically, and that is some of the work that I pushed myself to do. Our women in technology numbers are about 25% to 28% of the company which is a huge number for Silicon Valley. So we hire women, we try to mentor them, I myself take time to spend time with them, and then help them get a career that they're excited about. >> And when did you discover your affinity for computer science? It's always a great debate as to when is the best time, or when is the optimal time, or the most popular time for young girls and eventually young women to get involved in STEM? What was your experience? >> So I live with my uncle in Ecuador and my mother, so I always knew I wanted to do something structured, and at the beginning, he was an architect, so I thought I would be an architect, but then I started reading some science fiction books and the closest thing for me to science fiction, making that a reality, was a career in computer science and technology. So that's how I started, and that has led me to, now, Knightscope, and we're doing the most advanced technology that is out there, we're out there with artificial intelligence, we have machine learning, all of the technologies that are out there, robotics, we are using them to put them to use for the greater good. Our job is to keep America safe, and we all are working towards that goal. >> But I think you just want to make something fun that looked like C-3PO. >> It's more like R2-D2 actually, and if you want to see more, go to knightscope.com. >> Okay, and final question. So you're advice, more general advice, to older girls or young women, in terms of what they should do if they want to get into this or why they should consider a career in STEM if they haven't already. >> A career in STEM is very, very rewarding. You're going to be doing sometimes things that nobody else has done ever before. You're out there in front of everything that's happening with technology, and it's actually exciting. When you find other women that do what you want to do, look at people's backgrounds, look at what they've done, look what they're trying to accomplish, and then, make sure that you get into their lives and they'll help you through it. There's a lot of women who would be happy to help out and one of those is me, I'd be glad to help people out. >> Well, Mercedes, thank you so much, again, for spending some time. Congratulations on the award and comin' in and tellin' us your story and educating us more on Knightscope. >> Thank you, and if anybody wants to know, knightscope.com, they can find all about our technology. >> Alright, she's Mercedes, I'm Jeff Frick, we've been having a CUBE conversation in Palo Alto, thanks for watching, we'll catch you next time. (light techno music)

>> Announcer: Live from Orlando, Florida, it's theCUBE. Covering Grace Hopper Celebration of Women in Computing. Brought to you by Silicon Angle Media. >> Welcome back to theCUBE's coverage of the Grace Hopper Celebration in Orlando, Florida. I'm your host, Rebecca Knight along with my co-host, Jeff Frick. We are here with Brenda Darden Wilkerson. She is the new president and CEO of the Anita Borg institute. Thank you so much for joining us. >> I'm so excited to be here. >> This is a new position for you. >> Absolutely. >> But you've obviously been involved with the Anita Borg Institute for your career. At least been aware of it. So tell us a little bit about what this appointment means to you. >> Oh, it's so exciting. It's like coming full circle back to a tech career that I started. Back to understanding the needs of women having been there. Gone through the various stages of my career and then going into education. Helping encourage women into a career in tech. And now being able to advocate for them to be able to contribute at whatever stage they're in. Whether they are just entering or whether they're one of the women who have been in tech for a long time and are getting promoted into C-suite. Or whether or not they went through traditional education pathway to get in or if they learned on their own. So it's very exciting. >> And it cannot be as hard as the challenge that you just accomplished. I'm so impressed. Getting computer science as a requirement in the Chicago School District. >> Yes, yes. >> I mean that must've been quite a battle. I can only imagine. >> It was. It was, but you know when you want something, and you believe in it, it is amazing how you find other people who believe like you do. And you form a collaborative partnership that's really about caring about people. >> Jeff: Right. >> Many of us had been in tech and we had had the challenges and myself, personally, I came about computer science accidentally. I went to college thinking I was going to go into medicine. So I was pre-med. So I only learned about computer science accidentally. And of course obviously it changed my trajectory. It's been my career path and I was fine with that. Until years later when we were working on making computer science core, I was doing some lobbying on Capital Hill on a panel with a bunch of people. One happened to be a 19 year old girl who had a story similar to mine. And I thought how could this still be happening? >> Jeff: Right, right. >> How can people not have this choice and have this exposure early in life so that they know how to choose to contribute to the thing that's changing the way we live every single day. >> So do you see it changing? I mean we talked about this so many times on theCUBE. You know, that the core curriculum is the core curriculum. It's been there forever. >> Yes, yes, yes. >> And then the funny joke, right? Go back 100 years, nothing looks familiar except if you go to the school. I mean they're still reading the same Mark Twain book, right? >> Brenda: Right, right. >> Do you see it changing 'cause computing is such a big part of everyday life now. And it should be core everywhere. I mean the fact that you got that through, do you see it changing in a broader perspective from, kind of, your point of view? >> I do, I do. Education changes slowly, unfortunately. But actually when you look at, we launched computer science for all in 2013. And now it is an initiative that is national. The Obama White House embraced it and we were so proud. And it made the knowledge of going after computer science as something that all educators should really be thinking about as early as kindergarten for our students. It is making a difference in the lives of women. I've seen girls who many times would have been talked out of getting into a technical field by high school. For the few that could trickle in and get into those one or two classes that used to be available. I'm seeing girls learn that they could be innovators as early as five, six, or seven years old. Okay, so I'm just waiting to see the world that they're going to create for us when all of them. Because now, in Chicago, they're required to have computer science to graduate. So that's everyone so that's the key. It's computer science for all. And it is making a change. Not just for the kids, but the educators. I'm seeing women educators go, I could do this? I could get in and teach computer science? I could create something? That's exciting. >> So the Anita Borg Institute does so much good work around these issues. From getting computers into the hands of kindergartners to helping women on the verge of C-suite jobs in some of the biggest tech companies in the world. Where do you want to focus? As the new president, what are some of your special pet projects that you want to look at in the upcoming years? >> So I really want to think about how we dig into intersectionality. I want to first and foremost make vivid for more women of different backgrounds, who may have traditionally been left out of the equation, that there is an opportunity here for you if you want it. Okay, so that's about listening to them. That's about building additional alliances. That's about figuring out how to partner with organizations that we're all going in the same direction, right? So that more people that bring their unique lenses and experiences can help us create solutions, products, services that serve better just because they're there. So that's the first and most important thing. But then of course to, in order to do that, we have to figure out how to accelerate the work that anitab.org does in helping companies to figure out how to solve any problems that they may be having about diversifying their work force. So that's the other half of the equation. >> Do you see that the message is resonating? And this, I mean, you've been in the tech industry for, you're a veteran of the tech industry. Let's just say it, let's just put it at that. Let's just put it at that. But do you, I mean, just in terms of what we've been saying here too is that it's a lot of the same stuff. A lot of the same biases. And then there's things like to Google Manifesto which was this year, you know? And you just think, are we really still talking about this? I mean, where are you on the spectrum of completely discouraged to hopeful and inspired? >> Oh, I'm hopeful. I mean, look around you. (laughing) Look around you at all these women who are also hopeful. I am hopeful for them. We are hopeful together. And I think many times some of the remarks or things that happen out there are just an indication that maybe we're getting closer to moving that needle, you know? Sometimes that's when you hear from people is when changes are being made. So I'm not discouraged at all. I'm very excited to be on this team. It's a very powerful team. And to create the coalitions that our women are counting on us to do. >> It's pretty interesting with a lot of the negative stuff that happens in the news. And it actually has a really bright silver lining. And that it kind of coalesces people in ways that wouldn't necessarily happen. >> That's right, that's right. >> I thought your comment kind of about overt, or no, I guess the last guest. Overt, kind of, discrimination versus, kind of, less overt. It's harder to fight the less overt. So when somebody shines a big bright light on it, it actually, in a way, is a blessing because then it surfaces this thing. >> The stuff that's kind of, you know, it's lukewarm. It's easy for people to explain away. Even if it's really obvious to most people. But when it is as overt as it's been, it's out there now. It's like now we have something that we all have to deal with. It's not, you know, we can't be lukewarm and mealy mouth about it. Let's go to work and address this because it's so obvious. So in that way it's a silver lining. >> Jeff: Right, right. >> But the culture war that we're dealing with this. With what Melinda Gates was describing as the brogrammers. The hoodie guys, the sea of white dudes. >> Yes. >> Where we think all the great ideas are coming from. >> Brenda: Yeah. >> What is you feeling on how do we combat that? >> So, you know, here's an interesting perspective. I'm going to put a call on the entertainment industry. >> Rebecca: Okay. >> To put more images out there that are representative of what's really happening, right? So, you know, I have a sister that's a lawyer. And she's older than I am. And there was a time when you just didn't see very many images of women lawyers or women doctors. But if you watch television, you watch the movies, there are plenty of those now and the numbers. People can be what they can see. But if the images out there are all about the sea of white men. Then we will fight that struggle because people are impacted by what they see. >> Rebecca: The power of representation. >> The power, absolutely. And so I'm calling on people who have the power to change the images to do so. And to show the truth of what's really going on. >> Okay, so Hollywood, are you listening? (laughing) Do you have any final advice for the young women who are here. And maybe it's their first Grace Hopper Conference. >> Yeah, yeah. >> What do you think they should do to get the most out of their experience here in Orlando this week? >> Well, first of all, I'm so glad that you're here and I want you to be encouraged that there is a sisterhood. There is a community that cares about you that has seen some of the same things, some of the challenges. And maybe you don't even know about yet. But together, we can make a better world. We can be the change agents that we already are but on a such bigger scale. So, you know, go for it. Don't ever let fear stop you. And you will make a success out of whatever you're going after. >> Those are words to live by. >> Yeah, we need to get a bigger boat though. You got 18,000 people. >> I know. >> That's right. >> You can't get that on you IM placard. >> That's right, that's right. That's a new solution for tomorrow. (laughing) >> Great, well, Brenda, thanks so much. We're so excited for you and to be here at Grace Hopper again. >> Thank you so much. I appreciate being here. >> Great event, great event. >> Okay, thank you. >> I'm Rebecca Knight for Jeff Frick. We will have more from Grace Hopper in a little bit.