 Live from Stanford University in Palo Alto, California. It's theCUBE. Covering Women in Data Science Conference 2018. Brought to you by Stanford. Welcome back to theCUBE's continuing coverage of the Women in Data Science event withs. 2018, we are live at Stanford University. You can hear some great buzz around us. A lot of these exciting ladies in data science are here around us. I'm pleased to be joined by my next guest, Bhavani Charasingham, who is one of the speakers this afternoon, as well as a distinguished professor of computer science and the executive director of Cybersecurity Institute at the University of Texas at Dallas. Bhavani, thank you so much for joining us. Thank you very much for having me in your program. You have an incredible career. But before we get into that, I'd love to understand your thoughts on WIDS. In its third year alone, they're expecting to reach over 100,000 people today, both here at Stanford, as well as more than 150 regional events in over 50 countries. When you were early in your career, you didn't have a mentor. What does an event like WIDS mean to you? What are some of the things that excite you about giving your time to this exciting event? This is such an amazing event. And just in three years, it has just grown. And I'm just so motivated myself. And it's just, in a words, cannot express to see so many women working in data science or wanting to work in data science. And not just in US and in Stanford, it's around the world. I was reading some information about WIDS and I'm finding that there are WIDS ambassadors in Africa, South America, Asia, Australia, Europe, of course, US, Central America, all over the world. And data science is exploding so rapidly because data is everywhere, right? And so you really need to collect the data, stow the data, analyze the data, disseminate the data. And for that, you need data scientists. And what I'm so encouraged is that when I started getting into this field back in 1985, and that was 32 plus years ago in the fall, I worked 50% in cybersecurity, what used to be called computer security, and 50% in data science, what used to be called data management at that time. And there were so few women, and we did not have, as I said, women role models. And so I had to sort of work really hard, the commercial industry and then the MITRE Corporation and the US government, but slowly I started building a network. And my strongest supporters have been women. And so that was sort of in the early 90s when I really got started to build this network. And today I have a strong support group of women and we support each other. And we also mentor so many of the junior women and so that they don't sort of go through, have to sort of learn the hard way like I have. And so I'm very encouraged to see the enthusiasm, the motivation, both the part of the mentors as well as the mentees. So that's very encouraging. But we really have to do so much more. We do, you're right. It's really kind of the tip of the iceberg. But I think the scale at which Wiz has grown so quickly shines a massive spotlight on there's clearly such a demand for it. I'd love to get a feel now for the female undergrads in the courses that you teach at UT Dallas. What are some of the things that you're seeing in terms of their beliefs in themselves, their interest in data science, computer science, cyber security? Tell me about that dynamic. Right, so I have been teaching for 13 plus years full time now after a career in industry and federal research lab and government. And I find that we have women, but still not enough. But just over the last 13 years, I'm seeing so much more women getting so involved and wanting to further their careers, coming and talking to me. When I first joined in 2004 fall, there weren't many women. But now with programs like Wiz, and I also belong to another conference and actually I chaired that in 2016 called Wiz's Women in Cybersecurity. So through these programs, we've been able to recruit more women. But I would still have to say that most of the women, especially in our graduate programs are from South Asia and East Asia. You know, we hardly find women from the US, right? US-born women pursuing careers in areas like cyber security and to some extent, I would also say data science. And so we really need to do a lot more. And events like Wiz and Wizis, and we have also started a Grace lecture series. A Grace Hopper. We call it Grace lecture at our university. Of course there's Grace Hopper. We go to Grace Hopper as well. So through these events, I think that women are getting more encouraged and taking leadership roles. So that's very encouraging, but I still think that we are really behind, right? When you compare men and women. Yes, and if you look at the statistics, the statistics, yeah. So you have a speaking session this afternoon. Share with our audience some of the things that you're going to be sharing with the audience and some of the things that you think you'll be able to impart in terms of wisdom on the women here today. Okay, so what I'm going to do is that, first start off with some general background, how I got here. So I've already mentioned some of it to you. Because it's not just going to be a US event. It's going to be an Forbes reports that around 100,000 people are going to watch this event from all over the world. So I'm going to sort of first speak to this global audience as to how I got here to motivate these women from India, from Nigeria, from New Zealand, right? So, and then I'm going to talk about the work I've done. So over the last 32 years I've said about 50% of my time has been in cybersecurity, 50% in data science. Roughly, sometimes it's more in cyber, sometimes more in data. So my work has been integrating the two areas, okay? So my talk, first I'm going to wear my data science hat. And as a data scientist, I'm developing data science techniques, which is integration of statistical reasoning, machine learning, and data management. So applying data science techniques for cybersecurity applications. What are these applications? Intrusion detection, inside a threat detection, email spam filtering, website fingerprinting, malware analysis. So that's going to be my first part of the talk, a couple of charts. But then I'm going to wear my data, sorry, cybersecurity hat. What does that mean? These data science techniques could be hacked. That's happening now. There are some attacks that have been published where the data science, the models, are being thwarted by the attackers. So you can do all the wonderful data science in the world, but if your models are thwarted and they go and do something completely different, it's going to be of no use. So I'm going to wear my cybersecurity hat and I'm going to talk about how we are taking the attackers into consideration in designing our data science models. It's not easy. It's extremely challenging. We are getting some encouraging results, but it doesn't mean that we have solved the problem. Maybe we will never solve the problem, but we want to get close to it. So this area called adversarial machine learning, it started probably around five years ago. In fact, our team has been doing some really good work for the Army, Army Research Office on adversarial machine learning. And when we started, I believe it was in 2012, almost six years ago, there weren't many people doing this work, but now there are more and more. So practically every cybersecurity conference has got tracks in data science, machine learning. And so their point of view, I mean, their focus is not sort of designing machine learning techniques. That's the area of data scientists. Their focus is going to be coming up with appropriate models that are going to take the attackers into consideration. Because remember, the attackers are always trying to thwart your learning process. We were just at Fortinet Accelerate last week, the queue was, and cybersecurity and data science are such interesting and pervasive topics, right? Cybersecurity things, when aquifax happened, it suddenly translates to everyone, male, female, et cetera. And the same thing with data science in terms of the social impact. I'd love your thoughts on how cybersecurity and data science, how you can educate the next generation and maybe even reinvigorate the women that are currently in STEM fields to go look at how much more open and many more opportunities there are for women to make massive impact socially. Yeah, there are, I would say at this time, unlimited opportunities in both areas. Now in data science, it's really exploding because every company wants to do data science because data gives them the edge. But what's the point in having raw data when you cannot analyze, right? That's why data science is just exploding. And in fact, most of our graduate students, especially international students, want to focus in data science. So that's one thing. Cybersecurity is also exploding because every technology that's being developed, anything that has a microprocessor could be hacked. So we can do all the great data science in the world but an attacker can talk everything, right? And so cybersecurity is really crucial because you have to try and stop the attacker or at least detect what the attacker is doing. So every step that you move forward, you are going to be attacked. That doesn't mean you want to give up technology. One could say, okay, let's just forget about Facebook and Google and Amazon and the whole lot and let's just focus on cybersecurity or this, but we cannot. I mean, we have to make progress in technology. Whenever we make progress with technology, driverless cars or pacemakers, these technologies could be attacked, right? And with cybersecurity, there is such a shortage with the US government. And so we have substantial funding from the National Science Foundation to educate US citizen students in cybersecurity and especially recruit more women in cybersecurity. So that's why we are also focusing, we are a permanent co-chair for the Women in Cybersecurity event. What are some of the things along that front and I love that you think are key to successfully recruiting US females into cybersecurity. What do you think speaks to them? I think what speaks to them and we have been successful in recent years, this program started in 2010 for us, so it's about eight years. The first phase, we did not have women. So 2000 to 2014 because we were trying to get this education program going and giving all these scholarships. And then we got our second round of funding but our program director said, look, you guys have done a phenomenal job in hiring students, educating them and placing them with US government, but you all have not recruited female students. So what we did then is to get some of our senior lecturers, a superb lady called Dr. Jenelle Straj. She can really speak to these women. So we started the grace lecture and so with those events, and we started the Women in Cybersecurity Center as part of my cybersecurity institute. Through these events, we were able to recruit more women. We as women are still underrepresented in our cybersecurity program, but still instead of zero women, I believe we have now about five women and that's a five out of, by the time we would have finished our second phase, we would have total graduated about 50 plus students, 52 to 55 students, out of which I would say about eight would be female. So from zero to go to eight is a good thing, but it's not great. You see, so we want out of 50, we should get at least 25, but at least it's a start for us. Absolutely. But data science, we don't have as much of a problem because we have lots of international students. Remember, you don't need US citizenship to get jobs at Facebook or, but you need US citizenship to get jobs at NSA and CIA. So we get many international students and we have more women. And I would say we have, I don't have the exact numbers, but in my classes I would say about, I would say about 30% maybe, just under 30% female, which is encouraging, but still. It's not good. 30% now, you're right, it's encouraging. What was that 13 years ago when you started? When it started, before data science, everything, it was more men, not very few women. I would say maybe about 10%. So we've been getting to 30% now is a pretty big accomplishment. Exactly, in data science, but we need to get that cybersecurity numbers up. So last question for you as we have about a minute left, what are some of the things that excite you about having the opportunity to not just mentor your students, but to reach such a massive audience that you're going to be able to reach through with? It's, as I said, words cannot express my honor and how pleased and touched, these are the words, touched I am, to be able to talk to so many women. And I want to say why, because I'm a Tamil of Sri Lanka, no region, and so I had to make a journey, I got married, I'm going to talk about it at 20 in 1975, and my husband was finishing, just finishing my undergraduate in mathematics and physics. My husband was finishing his PhD at University of Cambridge, England. And so soon after marriage at 20, I moved to England, did my master's in PhD. So I joined University of Bristol and then we came here in 1980. And my husband got a position at New Mexico Petroleum Recovery Center. And so New Mexico Tech offered me a tenure track position, but my son was a baby. And so I turned it down. Once you do that, it's sort of hard to, so I took visiting faculty positions for three years in New Mexico, then in Minneapolis. Then I was a senior software developer at Control Data Corporation. It was one of the big companies. Then I had a lucky break in 1985. So I wanted to get back into research because I liked development, but I wanted to get back into research. So 85 I became, I was becoming in the fall, a US citizen, Honeywell got a contract to design and develop a research contract from United States Air Force, one of the early secure database systems. And Honeywell had to interview me and they had to like me, hire me. All three things came together. Wow. That was a lucky break. And since then my career has been just so thankful. So grateful. And you've turned that lucky break by a lot of hard work. A lot of hard work. Into what you're doing now. We thank you so much for stopping me. Thank you so much for having me. And sharing your story. And we're excited to hear some of the things that you're going to speak about later on. So have a wonderful rest of the conference. Thank you very much. We want to thank you for watching theCUBE. Again, we are live at Stanford University at the third annual Women in Data Science Conference. Hashtag wins 2018. I'm Lisa Martin. After the short break I'll be back with my next gas stick around.