(upbeat music) >> Hey everyone. Welcome back to theCUBE's live coverage of Women in Data Science, WiDS 2022, coming to live from Stanford University. I'm Lisa Martin. My next guest is here. Nandi Leslie, Doctor Nandi Leslie, Senior Engineering Fellow at Raytheon Technologies. Nandi, it's great to have you on the program. >> Oh it's my pleasure, thank you. >> This is your first WiDS you were saying before we went live. >> That's right. >> What's your take so far? >> I'm absolutely loving it. I love the comradery and the community of women in data science. You know, what more can you say? It's amazing. >> It is. It's amazing what they built since 2015, that this is now reaching 100,000 people 200 online event. It's a hybrid event. Of course, here we are in person, and the online event going on, but it's always an inspiring, energy-filled experience in my experience of WiDS. >> I'm thoroughly impressed at what the organizers have been able to accomplish. And it's amazing, that you know, you've been involved from the beginning. >> Yeah, yeah. Talk to me, so you're Senior Engineering Fellow at Raytheon. Talk to me a little bit about your role there and what you're doing. >> Well, my role is really to think about our customer's most challenging problems, primarily at the intersection of data science, and you know, the intersectional fields of applied mathematics, machine learning, cybersecurity. And then we have a plethora of government clients and commercial clients. And so what their needs are beyond those sub-fields as well, I address. >> And your background is mathematics. >> Yes. >> Have you always been a math fan? >> I have, I actually have loved math for many, many years. My dad is a mathematician, and he introduced me to, you know mathematical research and the sciences at a very early age. And so, yeah, I went on, I studied in a math degree at Howard undergrad, and then I went on to do my PhD at Princeton in applied math. And later did a postdoc in the math department at University of Maryland. >> And how long have you been with Raytheon? >> I've been with Raytheon about six years. Yeah, and before Raytheon, I worked at a small to midsize defense company, defense contracting company in the DC area, systems planning and analysis. And then prior to that, I taught in a math department where I also did my postdoc, at University of Maryland College Park. >> You have a really interesting background. I was doing some reading on you, and you have worked with the Navy. You've worked with very interesting organizations. Talk to the audience a little bit about your diverse background. >> Awesome yeah, I've worked with the Navy on submarine force security, and submarine tracking, and localization, sensor performance. Also with the Army and the Army Research Laboratory during research at the intersection of machine learning and cyber security. Also looking at game theoretic and graph theoretic approaches to understand network resilience and robustness. I've also supported Department of Homeland Security, and other government agencies, other governments, NATO. Yeah, so I've really been excited by the diverse problems that our various customers have you know, brought to us. >> Well, you get such great experience when you are able to work in different industries and different fields. And that really just really probably helps you have such a much diverse kind of diversity of thought with what you're doing even now with Raytheon. >> Yeah, it definitely does help me build like a portfolio of topics that I can address. And then when new problems emerge, then I can pull from a toolbox of capabilities. And, you know, the solutions that have previously been developed to address those wide array of problems, but then also innovate new solutions based on those experiences. So I've been really blessed to have those experiences. >> Talk to me about one of the things I heard this morning in the session I was able to attend before we came to set was about mentors and sponsors. And, you know, I actually didn't know the difference between that until a few years ago. But it's so important. Talk to me about some of the mentors you've had along the way that really helped you find your voice in research and development. >> Definitely, I mean, beyond just the mentorship of my my family and my parents, I've had amazing opportunities to meet with wonderful people, who've helped me navigate my career. One in particular, I can think of as and I'll name a number of folks, but Dr. Carlos Castillo-Chavez was one of my earlier mentors. I was an undergrad at Howard University. He encouraged me to apply to his summer research program in mathematical and theoretical biology, which was then at Cornell. And, you know, he just really developed an enthusiasm with me for applied mathematics. And for how it can be, mathematics that is, can be applied to epidemiological and theoretical immunological problems. And then I had an amazing mentor in my PhD advisor, Dr. Simon Levin at Princeton, who just continued to inspire me, in how to leverage mathematical approaches and computational thinking for ecological conservation problems. And then since then, I've had amazing mentors, you know through just a variety of people that I've met, through customers, who've inspired me to write these papers that you mentioned in the beginning. >> Yeah, you've written 55 different publications so far. 55 and counting I'm sure, right? >> Well, I hope so. I hope to continue to contribute to the conversation and the community, you know, within research, and specifically research that is computationally driven. That really is applicable to problems that we face, whether it's cyber security, or machine learning problems, or others in data science. >> What are some of the things, you're giving a a tech vision talk this afternoon. Talk to me a little bit about that, and maybe the top three takeaways you want the audience to leave with. >> Yeah, so my talk is entitled "Unsupervised Learning for Network Security, or Network Intrusion Detection" I believe. And essentially three key areas I want to convey are the following. That unsupervised learning, that is the mathematical and statistical approach, which tries to derive patterns from unlabeled data is a powerful one. And one can still innovate new algorithms in this area. Secondly, that network security, and specifically, anomaly detection, and anomaly-based methods can be really useful to discerning and ensuring, that there is information confidentiality, availability, and integrity in our data >> A CIA triad. >> There you go, you know. And so in addition to that, you know there is this wealth of data that's out there. It's coming at us quickly. You know, there are millions of packets to represent communications. And that data has, it's mixed, in terms of there's categorical or qualitative data, text data, along with numerical data. And it is streaming, right. And so we need methods that are efficient, and that are capable of being deployed real time, in order to detect these anomalies, which we hope are representative of malicious activities, and so that we can therefore alert on them and thwart them. >> It's so interesting that, you know, the amount of data that's being generated and collected is growing exponentially. There's also, you know, some concerning challenges, not just with respect to data that's reinforcing social biases, but also with cyber warfare. I mean, that's a huge challenge right now. We've seen from a cybersecurity perspective in the last couple of years during the pandemic, a massive explosion in anomalies, and in social engineering. And companies in every industry have to be super vigilant, and help the people understand how to interact with it, right. There's a human component. >> Oh, for sure. There's a huge human component. You know, there are these phishing attacks that are really a huge source of the vulnerability that corporations, governments, and universities face. And so to be able to close that gap and the understanding that each individual plays in the vulnerability of a network is key. And then also seeing the link between the network activities or the cyber realm, and physical systems, right. And so, you know, especially in cyber warfare as a remote cyber attack, unauthorized network activities can have real implications for physical systems. They can, you know, stop a vehicle from running properly in an autonomous vehicle. They can impact a SCADA system that's, you know there to provide HVAC for example. And much more grievous implications. And so, you know, definitely there's the human component. >> Yes, and humans being so vulnerable to those social engineering that goes on in those phishing attacks. And we've seen them get more and more personal, which is challenging. You talking about, you know, sensitive data, personally identifiable data, using that against someone in cyber warfare is a huge challenge. >> Oh yeah, certainly. And it's one that computational thinking and mathematics can be leveraged to better understand and to predict those patterns. And that's a very rich area for innovation. >> What would you say is the power of computational thinking in the industry? >> In industry at-large? >> At large. >> Yes, I think that it is such a benefit to, you know, a burgeoning scientist, if they want to get into industry. There's so many opportunities, because computational thinking is needed. We need to be more objective, and it provides that objectivity, and it's so needed right now. Especially with the emergence of data, and you know, across industries. So there are so many opportunities for data scientists, whether it's in aerospace and defense, like Raytheon or in the health industry. And we saw with the pandemic, the utility of mathematical modeling. There are just so many opportunities. >> Yeah, there's a lot of opportunities, and that's one of the themes I think, of WiDS, is just the opportunities, not just in data science, and for women. And there's obviously even high school girls that are here, which is so nice to see those young, fresh faces, but opportunities to build your own network and your own personal board of directors, your mentors, your sponsors. There's tremendous opportunity in data science, and it's really all encompassing, at least from my seat. >> Oh yeah, no I completely agree with that. >> What are some of the things that you've heard at this WiDS event that inspire you going, we're going in the right direction. If we think about International Women's Day tomorrow, "Breaking the Bias" is the theme, do you think we're on our way to breaking that bias? >> Definitely, you know, there was a panel today talking about the bias in data, and in a variety of fields, and how we are, you know discovering that bias, and creating solutions to address it. So there was that panel. There was another talk by a speaker from Pinterest, who had presented some solutions that her, and her team had derived to address bias there, in you know, image recognition and search. And so I think that we've realized this bias, and, you know, in AI ethics, not only in these topics that I've mentioned, but also in the implications for like getting a loan, so economic implications, as well. And so we're realizing those issues and bias now in AI, and we're addressing them. So I definitely am optimistic. I feel encouraged by the talks today at WiDS that you know, not only are we recognizing the issues, but we're creating solutions >> Right taking steps to remediate those, so that ultimately going forward. You know, we know it's not possible to have unbiased data. That's not humanly possible, or probably mathematically possible. But the steps that they're taking, they're going in the right direction. And a lot of it starts with awareness. >> Exactly. >> Of understanding there is bias in this data, regardless. All the people that are interacting with it, and touching it, and transforming it, and cleaning it, for example, that's all influencing the veracity of it. >> Oh, for sure. Exactly, you know, and I think that there are for sure solutions are being discussed here, papers written by some of the speakers here, that are driving the solutions to the mitigation of this bias and data problem. So I agree a hundred percent with you, that awareness is you know, half the battle, if not more. And then, you know, that drives creation of solutions >> And that's what we need the creation of solutions. Nandi, thank you so much for joining me today. It was a pleasure talking with you about what you're doing with Raytheon, what you've done and your path with mathematics, and what excites you about data science going forward. We appreciate your insights. >> Thank you so much. It was my pleasure. >> Good, for Nandi Leslie, I'm Lisa Martin. You're watching theCUBE's coverage of Women in Data Science 2022. Stick around, I'll be right back with my next guest. (upbeat flowing music)