(light upbeat music) >> Hey, everyone. Welcome back to theCUBE's day long coverage of Women in Data Science 2023. Live from Stanford University, I'm Lisa Martin. We've had some amazing conversations today with my wonderful co-host, as you've seen. Tracy Zhang joins me next for a very interesting and inspiring conversation. I know we've been bringing them to you, we're bringing you another one here. Dr. Irene Dankwa-Mullan joins us, the Chief Medical Officer at Marti Health, and a speaker at WIDS. Welcome, Irene, it's great to have you. >> Thank you. I'm delighted to be here. Thank you so much for this opportunity. >> So you have an MD and a Master of Public Health. Covid must have been an interesting time for you, with an MPH? >> Very much so. >> Yeah, talk a little bit about you, your background, and Marti Health? This is interesting. This is a brand new startup. This is a digital health equity startup. >> Yes, yes. So, I'll start with my story a little bit about myself. So I was actually born in Ghana. I finished high school there and came here for college. What would I say? After I finished my undergraduate, I went to medical school at Dartmouth and I always knew I wanted to go into public health as well as medicine. So my medical education was actually five years. I did the MPH and my medical degree, at the same time, I got my MPH from Yale School of Public Health. And after I finished, I trained in internal medicine, Johns Hopkins, and after that I went into public health. I am currently living in Maryland, so I'm in Bethesda, Maryland, and that's where I've been. And really enjoyed public health, community health, combining that aspect of sort of prevention and wellness and also working in making sure that we have community health clinics and safety net clinics. So a great experience there. I also had the privilege, after eight years in public health, I went to the National Institute of Health. >> Oh, wow. >> Where I basically worked in clinical research, basically on minority health and health disparities. So, I was in various leadership roles and helped to advance the science of health equity, working in collaboration with a lot of scientists and researchers at the NIH, really to advance the science. >> Where did your interest in health equity come from? Was there a defining moment when you were younger and you thought "There's a lot of inequities here, we have to do something about this." Where did that interest start? >> That's a great question. I think this influence was basically maybe from my upbringing as well as my family and also what I saw around me in Ghana, a lot of preventable diseases. I always say that my grandfather on my father's side was a great influence, inspired me and influenced my career because he was the only sibling, really, that went to school. And as a result, he was able to earn enough money and built, you know, a hospital. >> Oh wow. >> In their hometown. >> Oh my gosh! >> It started as a 20 bed hospital and now it's a 350 bed hospital. >> Oh, wow, that's amazing! >> In our hometown. And he knew that education was important and vital as well for wellbeing. And so he really inspired, you know, his work inspired me. And I remember in residency I went with a group of residents to this hospital in Ghana just to help over a summer break. So during a summer where we went and helped take care of the sick patients and actually learned, right? What it is like to care for so many patients and- >> Yeah. >> It was really a humbling experience. But that really inspired me. I think also being in this country. And when I came to the U.S. and really saw firsthand how patients are treated differently, based on their background or socioeconomic status. I did see firsthand, you know, that kind of unconscious bias. And, you know, drew me to the field of health disparities research and wanted to learn more and do more and contribute. >> Yeah. >> Yeah. So, I was curious. Just when did the data science aspect tap in? Like when did you decide that, okay, data science is going to be a problem solving tool to like all the problems you just said? >> Yeah, that's a good question. So while I was at the NIH, I spent eight years there, and precision medicine was launched at that time and there was a lot of heightened interest in big data and how big data could help really revolutionize medicine and healthcare. And I got the opportunity to go, you know, there was an opportunity where they were looking for physicians or deputy chief health officer at IBM. And so I went to IBM, Watson Health was being formed as a new business unit, and I was one of the first deputy chief health officers really to lead the data and the science evidence. And that's where I realized, you know, we could really, you know, the technology in healthcare, there's been a lot of data that I think we are not really using or optimizing to make sure that we're taking care of our patients. >> Yeah. >> And so that's how I got into data science and making sure that we are building technologies using the right data to advance health equity. >> Right, so talk a little bit about health equity? We mentioned you're with Marti Health. You've been there for a short time, but Marti Health is also quite new, just a few months old. Digital health equity, talk about what Marti's vision is, what its mission is to really help start dialing down a lot of the disparities that you talked about that you see every day? >> Yeah, so, I've been so privileged. I recently joined Marti Health as their Chief Medical Officer, Chief Health Officer. It's a startup that is actually trying to promote a value-based care, also promote patient-centered care for patients that are experiencing a social disadvantage as a result of their race, ethnicity. And were starting to look at and focused on patients that have sickle cell disease. >> Okay. >> Because we realize that that's a population, you know, we know sickle cell disease is a genetic disorder. It impacts a lot of patients that are from areas that are endemic malaria. >> Yeah. >> Yeah. >> And most of our patients here are African American, and when, you know, they suffer so much stigma and discrimination in the healthcare system and complications from their sickle cell disease. And so what we want to do that we feel like sickle cell is a litmus test for disparities. And we want to make sure that they get in patient-centered care. We want to make sure that we are leveraging data and the research that we've done in sickle cell disease, especially on the continent of Africa. >> Okay. >> And provide, promote better quality care for the patients. >> That's so inspiring. You know, we've heard so many great stories today. Were you able to watch the keynote this morning? >> Yes. >> I loved how it always inspires me. This conference is always, we were talking about this all day, how you walk in the Arrillaga Alumni Center here where this event is held every year, the vibe is powerful, it's positive, it's encouraging. >> Inspiring, yeah. >> Absolutely. >> Inspiring. >> Yeah, yeah. >> It's a movement, WIDS is a movement. They've created this community where you feel, I don't know, kind of superhuman. "Why can't I do this? Why not me?" We heard some great stories this morning about data science in terms of applications. You have a great application in terms of health equity. We heard about it in police violence. >> Yes. >> Which is an epidemic in this country for sure, as we know. This happens too often. How can we use data and data science as a facilitator of learning more about that, so that that can stop? I think that's so important for more people to understand all of the broad applications of data science, whether it's police violence or climate change or drug discovery or health inequities. >> Irene: Yeah. >> The potential, I think we're scratching the surface. But the potential is massive. >> Tracy: It is. >> And this is an event that really helps women and underrepresented minorities think, "Why not me? Why can't I get involved in that?" >> Yeah, and I always say we use data to make an make a lot of decisions. And especially in healthcare, we want to be careful about how we are using data because this is impacting the health and outcomes of our patients. And so science evidence is really critical, you know? We want to make sure that data is inclusive and we have quality data. >> Yes. >> And it's transparent. Our clinical trials, I always say are not always diverse and inclusive. And if that's going to form the evidence base or data points then we're doing more harm than good for our patients. And so data science, it's huge. I mean, we need a robust, responsible, trustworthy data science agenda. >> "Trust" you just brought up "trust." >> Yeah. >> I did. >> When we talk about data, we can't not talk about security and privacy and ethics but trust is table stakes. We have to be able to evaluate the data and trust in it. >> Exactly. >> And what it says and the story that can be told from it. So that trust factor is, I think, foundational to data science. >> We all see what happened with Covid, right? I mean, when the pandemic came out- >> Absolutely. >> Everyone wanted information. We wanted data, we wanted data we could trust. There was a lot of hesitancy even with the vaccine. >> Yeah. >> Right? And so public health, I mean, like you said, we had to do a lot of work making sure that the right information from the right data was being translated or conveyed to the communities. And so you are totally right. I mean, data and good information, relevant data is always key. >> Well- >> Is there any- Oh, sorry. >> Go ahead. >> Is there anything Marti Health is doing in like ensuring that you guys get the right data that you can put trust in it? >> Yes, absolutely. And so this is where we are, you know, part of it would be getting data, real world evidence data for patients who are being seen in the healthcare system with sickle cell disease, so that we can personalize the data to those patients and provide them with the right treatment, the right intervention that they need. And so part of it would be doing predictive modeling on some of the data, risk, stratifying risk, who in the sickle cell patient population is at risk of progressing. Or getting, you know, they all often get crisis, vaso-occlusive crisis because the cells, you know, the blood cell sickles and you want to avoid those chest crisis. And so part of what we'll be doing is, you know, using predictive modeling to target those at risk of the disease progressing, so that we can put in preventive measures. It's all about prevention. It's all about making sure that they're not being, you know, going to the hospital or the emergency room where sometimes they end up, you know, in pain and wanting pain medicine. And so. >> Do you see AI as being a critical piece in the transformation of healthcare, especially where inequities are concerned? >> Absolutely, and and when you say AI, I think it's responsible AI. >> Yes. >> And making sure that it's- >> Tracy: That's such a good point. >> Yeah. >> Very. >> With the right data, with relevant data, it's definitely key. I think there is so much data points that healthcare has, you know, in the healthcare space there's fiscal data, biological data, there's environmental data and we are not using it to the full capacity and full potential. >> Tracy: Yeah. >> And I think AI can do that if we do it carefully, and like I said, responsibly. >> That's a key word. You talked about trust, responsibility. Where data science, AI is concerned- >> Yeah. >> It has to be not an afterthought, it has to be intentional. >> Tracy: Exactly. >> And there needs to be a lot of education around it. Most people think, "Oh, AI is just for the technology," you know? >> Yeah, right. >> Goop. >> Yes. >> But I think we're all part, I mean everyone needs to make sure that we are collecting the right amount of data. I mean, I think we all play a part, right? >> We do. >> We do. >> In making sure that we have responsible AI, we have, you know, good data, quality data. And the data sciences is a multi-disciplinary field, I think. >> It is, which is one of the things that's exciting about it is it is multi-disciplinary. >> Tracy: Exactly. >> And so many of the people that we've talked to in data science have these very non-linear paths to get there, and so I think they bring such diversity of thought and backgrounds and experiences and thoughts and voices. That helps train the AI models with data that's more inclusive. >> Irene: Yes. >> Dropping down the volume on the bias that we know is there. To be successful, it has to. >> Definitely, I totally agree. >> What are some of the things, as we wrap up here, that you're looking forward to accomplishing as part of Marti Health? Like, maybe what's on the roadmap that you can share with us for Marti as it approaches the the second half of its first year? >> Yes, it's all about promoting health equity. It's all about, I mean, there's so much, well, I would start with, you know, part of the healthcare transformation is making sure that we are promoting care that's based on value and not volume, care that's based on good health outcomes, quality health outcomes, and not just on, you know, the quantity. And so Marti Health is trying to promote that value-based care. We are envisioning a world in which everyone can live their full life potential. Have the best health outcomes, and provide that patient-centered precision care. >> And we all want that. We all want that. We expect that precision and that personalized experience in our consumer lives, why not in healthcare? Well, thank you, Irene, for joining us on the program today. >> Thank you. >> Talking about what you're doing to really help drive the volume up on health equity, and raise awareness for the fact that there's a lot of inequities in there we have to fix. We have a long way to go. >> We have, yes. >> Lisa: But people like you are making an impact and we appreciate you joining theCUBE today and sharing what you're doing, thank you. >> Thank you. >> Thank you- >> Thank you for having me here. >> Oh, our pleasure. For our guest and Tracy Zhang, this is Lisa Martin from WIDS 2023, the eighth Annual Women in Data Science Conference brought to you by theCUBE. Stick around, our show wrap will be in just a minute. Thanks for watching. (light upbeat music)

>> Live from Stanford University, it's theCUBE. Covering Global Women in Data Science Conference. Brought to you by SiliconANGLE Media. >> Welcome back to theCUBE I'm Lisa Martin. We've been live all day at the fourth annual Women in Data Science Conference. I'm with John Furrier, John, this is not just WiDS fourth annual, it's theCUBE's fourth time covering this event. There were, as Margot Gerritsen, Co-Founder stopped by this afternoon and was chatting with me saying, there's over 20,000 people they expect today just to watch the WiDS livestream from Stanford. Another 100,000 engaging in over 150 regional WiDS events, and 50 countries, CUBE's been there since the beginning tell us a little bit about that. >> Well what's exciting about this event is that we've been there from the beginning, present at creation with these folks. Great community, Judy Logan, Karen Matthys, Margot. They're all been great, but the vision from day one has been put together smart people, okay, on a stage, in a room, and bring it, syndicate it out to anyone who's available, meet ups and groups around the world. And if you bet on good content and quality people the community with self-form. And with the Stanford brand behind it, it really was a formula for success from day one. And this is the new model, this is the new reality, where, if you have high quality people in context, the global opportunity around the content and community work well together, and I think they cracked the code. Something that we feel similar at theCUBE is high quality conversations, builds community so content drives community and keep that fly wheel going this is what Women in Data Science have figured out. And I'm sure they have the data behind it, they have the women who can analyze the data. But more importantly is a great community and it's just it's steamrolling forward ahead, it's just great to see. 50 countries, 125 cities, 150 events. And it's just getting started so, we're proud to be part of it, and be part of the creation but continue to broadcast and you know you're doing a great job, and I wish I was interviewing, some of the ladies myself but, >> I know you do >> I get jealous. >> you're always in the background, yes I know you do. You know you talk about fly wheel and Margot Gerritsen we had her on the WiDS broadcast last year, and she said, you know, it's such a short period of time its been three and a half years. That they have generated this incredible momentum and groundswell that every time, when you walk in the door, of the Stanford Arrillaga Alumni Center it's one of my favorite events as you know, you feel this support and this positivity and this movement as soon as you step foot in the door. But Margot said this actually really was an idea that she and her Co-Founders had a few years ago. As almost sort of an anti, a revenge conference. Because they go to so many events, as do we John, where there are so many male, non-female, keynote speakers. And you and theCUBE have long been supporters of women in technology, and the time is now, the momentum is self-generating, this fly wheel is going as you mentioned. >> Well I think one of the things that they did really well was they, not only the revenge on the concept of having women at the event, not being some sort of, you know part of an event, look we have brought women in tech on stage, you know this is all power women right? It's not built for the trend of having women conference there's actual horsepower here, and the payload of the content agenda is second to none. If you look at what they're talking about, it's hardcore computer science, its data analytics, it's all the top concepts that the pros are talking about and it just happens to be all women. Now, you combine that with what they did around openness they created a real open environment around opening up the content and not making it restrictive. So in a way that's, you know, counter intuitive to most events and finally, they created a video model where they livestream it, theCUBE is here, they open up the video format to everybody and they have great people. And I think the counter intuitive ones become the standard because not everyone is doing it. So that's how success is, it's usually the ones you don't see coming that are doing it and they think they did it. >> I agree, you know this is a technical conference and you talked about there's a lot of hardcore data science and technology being discussed today. Some of the interesting things, John, that I really heard thematically across all the guests that I was able to interview today is, is the importance, maybe equal weight, maybe more so some of the other skills, that, besides the hardcore data analysis, statistical analysis, computational engineering and mathematics. But it's skills such as communication, collaboration collaboration was key throughout the day, every person in academia and the industry that we talked to. Empathy, the need to have empathy as you're analyzing data with these diverse perspectives. And one of the things that kind of struck me as interesting, is that some of the training in those other skills, negotiation et cetera, is not really infused yet in a lot of the PhD Programs. When communication is one of the key things that makes WiDS so effective is the communication medium, but also the consistency. >> I think one of the things I'm seeing out of this trend is the humanization of data and if you look at I don't know maybe its because its a women's conference and they have more empathy than men as my wife always says to me. But in seriousness, the big trend right now in machine learning is, is it math or is it cognition? And so if you look at the debate that machine learning concepts, you have two schools of thought. You have the Berkeley School of thought where it's all math all math, and then you have, you know kind of another school of thought where learning machines and unsupervised machine learning kicks in. So, machines have to learn, so, in order to have a humanization side is important and people who use data the best will apply human skills to it. So it's not just machines that are driving it, it's the role of the humans and the machines. This is something we have been talking a lot in theCUBE about and, it's a whole new cutting edge area of science and social science and look at it, fake news and all these things in the mainstream press as you see it playing out everyday, without that contextual analysis and humanization the behavioral data gets lost sometimes. So, again this is all data, data science concepts but without a human application, it kind of falls down. >> And we talked about that today and one of the interesting elements of conversation was, you know with respect to data ethics, there's 2.5 trillion data sets generated everyday, everything that we do as people is traceable there's a lot of potential there. But one of the things that we talked about today was this idea of, almost like a Hippocratic Oath that MDs take, for data scientists to have that accountability, because the human component there is almost one that can't really be controlled yet. And it's gaining traction this idea of this oath for data science. >> Yeah and what's interesting about this conference is that they're doing two things at the same time. If you look at the data oath, if you will, sharing is a big part, if you look at cyber security, we are going to be at the RSA conference this week. You know, people who share data get the best insights because data, contextual data, is relevant. So, if you have data and I'm looking at data but your data could help me figure out my data, data blending together works well. So that's an important concept of data sharing and there's an oath involved, trust, obviously, privacy and monitoring and being a steward of the data. The second thing that's going on at this event is because it's a global event broadcast out of Stanford, they're activating over 50 countries, over 125 cities, they're creating a localization dynamic inside other cities so, they're sharing their data from this event which is the experts on stage, localizing it in these markets, which feeds into the community. So, the concept of sharing is really important to this conference and I think that's one of the highlights I see coming out of this is just that, well, the people are amazing but this concept of data sharing it's one of those big things. >> And something to that they're continuing to do is not just leverage the power of the WiDS brand that they're creating in this one time of year in the March of the year where they are generating so much interest. But Margot talked about this last year, and the idea of developing content to have this sustained inspiration and education and support. They just launched a podcast a few months ago, which is available on iTunes and GooglePlay. And also they had their second annual datathon this year which was looking at palm oil production, plantations rather, because of the huge biodiversity and social impact that these predictive analytics can have, it's such an interesting, diverse, set of complex challenges that they tackle and that they bring more awareness to everyday. >> And Padmasree Warrior talked about her keynote around, former Cisco CTO, and she just ran, car, she's working on a new start up. She was talking about the future of how the trends are, the old internet days, as the population of internet users grew it changed the architecture. Now mobile phones, that's changing the architecture. Now you have a global AI market, that's going to change the architecture of the solutions, and she mentioned at the end, an interesting tidbit, she mentioned Blockchain. And so I think that's something that's going to be kind of interesting in this world is, because there's, you know about data and data science, you have Blockchain it's the data store potentially out there. So, interesting to see as you start getting to these supply chains, managing these supply chains of decentralization, how that's going to impact the WiDS community, I'm curious to see how the team figures that out. >> Well I look forward to being here at the fifth annual next year, and watching and following the momentum that WiDS continues to generate throughout the rest of 2019. For John Furrier, I'm Lisa Martin, thanks so much for watching theCUBE's coverage, of the fourth annual Women in Data Science Conference Bye for now. (upbeat electronic music)

>> Live from Stanford University it's theCUBE. Covering Global Women in Data Science Conference. Brought to you by SiliconANGLE Media. >> Welcome back to theCUBE's live coverage of Women in Data Science fourth annual global conference. I'm Lisa Martin here at the Arrillaga Alumni Center at Stanford joined by, a WiDS speaker and Standford alum Madeleine Udell. You are now an assistant professor at Cornell University. Madeleine welcome to theCUBE. >> Thank you it's great to be here. >> So this is your first WiDS. >> This is my first WiDS. >> But you were at Stanford a few years ago when the WiDS movement began. So tell us a little bit about what you do at Cornell. The research that you do, the classes that you teach, and the people men and women that you work with. >> Sure so at Cornell I'm studying optimization and machine learning. I'm really interested in understanding low dimensional structure in large messy data sets. So we can figure out ways of looking at the data set that make them seem cleaner, and smaller, and easier to work with. I teach a bunch of classes related to these topics. PhD classes on optimization and on optimization for machine learning. But one that I'm really excited about is an undergrad class that I teach called, Learning With Big Messy Data. That introduces undergraduates to what messy data sets look like which they often don't see in their undergraduate curriculum. And ways to wrangle them into the kinds of forms that they could use with other tools that they have learned about as undergraduates. >> You say messy, big messy data. >> Yes. >> With a big smile on your face. >> Yes. >> So this is something that might be introduced to these students as they enter their PhD program. Define messy data and some applications of it. >> Often times people only learn about big messy data when they go to industry and that's actually how I understood what these kinds of data sets looked like. I took a break from my PhD while my advisor was on sabbatical and I scampered off to the Obama 2012 campaign, and on the campaign they had these horrible data sets. They had you know hundreds of millions or rows. One for every voter in the United States, and maybe tens of thousands of columns about things that we knew about those voters. And they were weird kinds of things, right? They were things like gender, which in this data set was boolean, State, which took one of fifty values, Approximate education level, Approximate income weather or not they had voted in each of the last elections and I looked at this and I was like I don't know what to do, right? these are not numbers, right? They are boolean, they're categorical they're ordinals and a bunch of the data was missing so there were many people for which we didn't know their level of education or we didn't know their approximation of income or we didn't know weather or not they had voted in the last elections. So with this kind of horrible data set how do you do like basic things, how do you cluster, how do you even visualize this kind of data set so I came back to my PhD thinking, I want to figure out how this works I want to figure out the right way of approaching this data set Cause a lot of people would just sort of hack it and I wanted to understand what's really going on here what's the right model to think about this stuff. >> So that really was quite influential in the rest of your PhD and what your doing now, cause you found this interesting but also tangible in a way, right? especially working with a political campaign >> That's right so, I mean I'm both interested in the application and I'm interested in the math so I like to be able to come back to Stanford at the time we're now at Cornell and really think about what the mathematical structure is of these data sets what are good models for what the underlying latent spaces look like, but then I also like to take it back to people in industry, take it back to political campaigns but you know here at WiDS I'm excited to tell people about the kinds of mathematics that can help you deal with this kind of data set more easily. >> Did you have a talk this afternoon called filling in missing-- >> Yup >> Data with low rank models >> that's right >> One of the things before we get into that, that id love to kind of unpack with you is looking at, taking the campaign Obama 2012 campaign messy data as an example of something that is interesting there's a lot of science and mathematics behind it but there's also other skill I'd like to get your perspective on and that's creativity that's empathy it's being able to clearly understand and communicate to your audience, Where do those other skills factor into what you do as a professor and also the curriculum you're teaching >> Sure, I think they are incredibly important if you want your technical work to have an impact you need to be able to communicate it to other people you need to make, number one make sure you are working on the right problems which means talking to people to figure out what the right problems are and this is one aspect that I consider really fundamental to my career is going around talking to people in industry about what problems they are facing that they don't know how to solve, right? Then you go back to your universities you squirrel away and try and figure it out, often sometimes I can't figure it out on my own so I need to put together a team, I need to pull in other people from other disciplines who have the skills I don't have in order to figure out the full solution to the problem, right? Not just to solve the part of the problem that I know how but to solve the full problem I can see and so that also requires a lot of empathy and communication to make the team actually produce something more than what the individual members could. Then the third step is to communicate that result back to the people who could actually use it and put it into practice, and for that you know that's part of the reason I'm here at WiDS is to try show people the useful things I think that I've come up with but I'm also really excited to talk to people here and understand what gnarly problems do they not know how to solve yet. >> There's a lot of gnarly problems out there, love that you brought that word up >> (laughter) >> But I'm just curious before we go further is understanding did you understand when you was studying mathematics, computational engineering data science did you understand at that point the other important skills. A collaboration of communication or did you discover that along the way and is that something that is taught today to those students these are the other things we want to develop in you >> Yeah I think we barely teach those skills, >> Really? I think at the earliest level there's a lot of focus on the technical skills and it's hard to see the other skills that are going to enable you to get from 90 to 100% but that 90 to 100% is the most important part. Right? If you can't communicate your results back then it doesn't do so much good to have produced the results in the first place, >> Right but really a lot of the education right now at most universities is focused on the technical core and you can see that in the way we evaluate student, right? We evaluate them on their homework which are supposed to be individual on their test performance, right? maybe their projects and the projects I think are much better at helping them develop these skills of communication and teamwork, but that's you know not included in most courses because frankly it's hard to do it's hard to teach students how to work on projects It's hard to get them topics, it's hard to evaluate their results on their projects it's hard to give them time to present it to a group, but I think these are critical skills, right? The project work is much more what works becomes after they finish their studies. >> As you've been in the STEM fields for quite a while and gone so far in your academic career, tell me about the changes that you've seen in the curriculum and do you think you're going to have a chance to influence some of those other skills communication when I was in grad school studying biology, communication a long time ago was actually part of it for a semester but I'm just wondering do you think that this is something that a movement like WiDS could help inspire. >> I think it's important to help people see what, the skills they are going to need to use down the line I think that sometimes, the thing is I think that the technical foundation is really important and I think that doubling down on that particularly when your young and can concentrate on the, on the nitty gritty details I actually think that's something that becomes harder as you get older And so focusing on that for people on their undergrad and early PDH I think that actually makes sense but you want them to see what the final result is, right? You want them to see like what is their career and how is that different from what they are doing right now So I think events like WiDS are really great for showcasing that but I would also like to sort of pull that forward, to pull that project work forward, to the extent possible with the skills that the students have at any point in their curriculum in the class that I teach in big messy date the cap stone of the course is, class project where the students tackle a big messy data set that they find on their own, they define the problems and the form of what they are supposed to produce is supposed to be a report to their manager, right? To say the project proposal says, "manager this is why I should be allowed to work on this "project for the next month because it's so important "it's really going to drive growth in our business it's going to "open up new markets" But they're supposed to describe it industry terms not just academic terms, right? Then they try and figure out actually how to solve the problem and at the end they're supposed to once again write a report that's describing how what they found will help and impact the business >> That element of persuasion is key-- >> That's right that's right >> So the last thing here as we wrap up this is the fourth annual women in data science conference that I mentioned in the opening. The impact and the expansion that they have been able to drive in such a short period of time is something that I always loved seeing every year there's is a hundred and fifty plus regional events going on they're expected to reach a hundred thousand people what excites you about the opportunity that you have to present here at Stanford later today? >> I think that it's amazing that there is so many people that are excited about WiDS, I mean I can't travel to a hundred and fifty locations certainly not this year, not in many many years so the ability to, to be in touch with so many people in so many different places is really exciting to me I hope that they will be in touch with me too that direction is a little be harder with current technology but I want to learn from them as well as teaching them. >> Well Madeleine thank you so much for sharing some of your time with me this morning on theCUBE we appreciate that, and wish you good luck on your WiDS presentation this afternoon >> It was really fun to talk with you, thank you for having me here >> Ah my pleasure >> We want to thank you, you're watching theCUBE live from the forth annual women in data science conference WiDS here at Stanford, I'm Lisa Martin stick around I'll be right back after a break with my next guest. 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