One of the thing’s I’ve observed over and over again from the best corporate innovation leaders is how data can be fuel for their innovation programs. On this episode of Agile Giants, I bring on a true academic thought leader on this issue – Rebecca Nugent. Rebecca is a chaired professor in statistics and data science at CMU. She’s built some really amazing programs both at the undergrad and graduate level around data science and really across the entire university. She also has partnered with me on some interesting executive education programs. As you listen to this week’s episode, let me challenge you to think about how data can be fuel for your innovation programs.
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Full Transcript
Sean Ammirati (00:08):
Welcome to Agile Giants, lessons from corporate innovators. I’m Sean Ammirati, your host, co-founder and director of the Carnegie Mellon Corporate Startup Lab and partner at the early stage venture capital fund, Birchmere Ventures. Each week I’m going to talk to guests who are experts at creating startups inside large corporations. I believe fundamentally a startup within a company. It’s the same as one inside the proverbial garage, a group of entrepreneurs trying to make the world a better place using new ideas and inventions. However, I also believe some of the techniques and processes are just inherently different. This podcast is going to explore those similarities and differences.
Sean Ammirati (00:55):
Welcome to another episode of Agile Giants. One of the things I heard from so many of you during the corporate venture capital conversation was how you appreciated the perspective of both academic thought leaders and individuals in industry applying CVC to the companies they worked with. As I thought about that and stepped back, what I want to do moving forward is really pull in academic leadership and thinking around many of these topics for corporate innovation and we’re going to start that today. A conversation I had with my colleague at Carnegie Mellon, Rebecca Nugent. We’re going to talk about how data can be really fuel for innovation. Rebecca is an expert on this as a chaired professor in statistics and data science at CMU. She’s built some really amazing programs both at the undergrad and graduate level around data science and really across the entire university and also partnered with me on some interesting executive education programs.
Sean Ammirati (01:53):
So as you’re listening to this week’s conversation with Rebecca, let me challenge you to think about how data can be fuel in your organization and let me know what you come up with. You can send me an email or just message me on social media, but I hope you enjoy this week’s conversation with professor Rebecca Nugent.
Sean Ammirati (02:14):
All right, well Rebecca, thanks so much for joining me today. I’m sure a lot of people are familiar with you. For those who may not be familiar with your work, maybe just quickly give an introduction on yourself for the audience.
Rebecca Nugent (02:24):
Yeah, sure. Thank you so much for having me today, Sean. My name is Rebecca Nugent. I’m the Feinberg professor of statistics and data science at Carnegie Mellon University. I’m the associate head of the department of statistics and data science and I’m also the co-director of the undergraduate program there.
Sean Ammirati (02:40):
Awesome. Data science is a term that almost everybody in this audience has probably spent more time thinking about in the last year than they probably did, however long their career extended before that. So when we say data science at Carnegie Mellon, what do we mean by that?
Rebecca Nugent (02:58):
That is a really good question and one that I get asked very frequently. So when we think of data science at Carnegie Mellon, we think about solving real problems with real data. We think of it as this process, thinking about the data life cycle. It starts with people and it ends with people. So if we’re given a research question, if we’re given an idea, an innovation that we want to try, we have to start with thinking about how to collect the data, where to store the data, how to access the data, provide privacy, security, all sorts of steps that are happening along the way before we even think about analyzing that data. How do we build infrastructure? How do we make sure that the data sets are well integrated, that we are able to access the information that we want? Moving into visualization. Heading into an analysis. An analysis could be robust statistical models. It could be machine learning techniques. It could be artificial intelligence, whatever’s appropriate for the problem at hand.
Rebecca Nugent (04:00):
Then after the data are analyzed, one of the most important steps in the process of data science is how do we take the results and interpret them and turn them into actionable insights that can be leveraged to solve whatever problem we’re addressing.
Rebecca Nugent (04:15):
I think when most people think about data science or if you just Googled data science, you would see lots and lots of different programming courses and different software packages and lots of very technical terms and technical skills that are used. But in our experience, where data science can make the biggest impact is trying to help people understand how to use data or how to understand their data to think about making decisions for their research or for their industry, for their business, whatever is most appropriate, even just for their lives. So the data really is the fuel as we’ve been talking about. The data is the fuel for making decisions and trying to interpret that data and use that data, that’s where we see the real impact of data science.
Sean Ammirati (05:03):
That’s awesome. This is probably easier for me to say than you to say, but CMU is sort of undisputed, the leader in data science in terms of those programs. We’re the number one program in the country for this investigation and doing all the stuff you’re talking about right?
Rebecca Nugent (05:20):
So of course I’m going to be biased, but so yeah Carnegie Mellon, due to our decentralized structure of Carnegie Mellon is made up of seven colleges and each of those different colleges has data science programs in it for example. We have nine different master’s programs that are associated with data science, including ones from the department of statistics and data science where I am. The PhD program is a top five ranked program, PhD in statistics and data science. The undergraduate program in statistics and data science has been ranked number one in the country by College Factual for the last four in a row. So we feel pretty good about what we’re trying to accomplish. We’ve got data science happening all over the campus.
Sean Ammirati (06:03):
So that was modest. But the short answer is yes, we’re number one. So moving right along, talk a little bit about how you got to CMU and to this place where you’re leading all these projects for the university.
Rebecca Nugent (06:16):
Yeah, sure. I did my undergraduate at Rice University and then I did my master’s at Stanford and my PhD at the University of Washington in Seattle. When I was finishing my PhD, I was looking around on the academic job market and there was a postdoc opportunity at Carnegie Mellon in the statistics and data science department. It was a postdoc for three years and it was a slightly unusual postdoc. Carnegie Mellon is known for being very creative in how they build positions to match people’s skillsets. At Carnegie Mellon it’s very common for them to look for talent and then try to find the right jobs that fit for them, versus having a job description and then sticking to that description. We’re very flexible. So this particular postdoc had some teaching and some research involved in it. Carnegie Mellon has always been one of the top statistics programs in the country and it seemed like a really interesting place that allowed me to develop in lots of different areas, leadership, pedagogy and research.
Rebecca Nugent (07:24):
I thought, well that’ll be a great place to go start. I’ll go spend a few years there and start my academic career. Then a few years into that postdoc, they asked me to stay. They asked me to stay on a teaching track position. A little bit of explanation here. Carnegie Mellon has three different kinds of faculty tracks. These are faculty lines. So there is a longterm investment. They’re not instructor positions or adjuncts. These are full faculty positions on the teaching track, the research track and the tenure track. For all three tracks you do research, teaching and service, just the percentages are slightly different. They just vary depending on the type of track. So when you’re on a faculty track line at Carnegie Mellon, regardless of what type of track, it is a full commitment and investment from the university.
Rebecca Nugent (08:10):
So at the time my department was excited to keep me on my research path and keep me on the teaching path that I was doing. Because one of the things that I had some early success in is building programs. So at the time say, let’s say about 10 years ago, the undergraduate programs in statistics were not very large. We had not had the boom that we see happening now at a national level. It was starting to trickle though, statistics and data were starting to be these buzz words. One of the things that we were lucky with in our department is to have the flexibility and the resources to be able to invest in building undergraduate courses to start building the program. We managed to catch that huge wave that came through for that demand for undergraduate education for statistics. Then data science came on a couple of years after that and of course we grabbed that because, I mean if I’m being honest, at Carnegie Mellon we were already doing data science before there was data science.
Rebecca Nugent (09:08):
What we’ve been able to do in 10 to 12 years is we’ve built five undergraduate degrees, different degrees in statistics and data science, including an undergraduate degree in statistics and machine learning. That is the only undergraduate degree in statistics and machine learning in the country due to the infrastructure that’s set up at Carnegie Mellon. So we have a statistics and data science department as well as a machine learning department. We built a new master’s program. We added a few PhD tracks. So we’ve really had a lot of success at building new degrees, particularly interdisciplinary degrees that are really excellent matches for today’s problems, for today’s challenging problems that stretch across disciplines.
Rebecca Nugent (09:53):
The quality of the students we were getting is just unbelievable. I mean these are just some of the smartest students that you’ll ever see, who are coming to these programs. Success begets success. We’re surrounded by really good faculty and really good support from Carnegie Mellon at an institutional level.
Rebecca Nugent (10:12):
Then we turned our eyes toward data science. With respect to data science, I’ve already talked a little bit about the different types of degrees that exist at Carnegie Mellon in data science. They’re diverse. Some of them are focused on public policy. Some of them are focused on security. Some are focused on statistical methodology. But they all have this flavor of real applications using real data. What we’ve really started to concentrate on in the last few years is this idea of data science, experiential learning.
Rebecca Nugent (10:42):
One example of that is a program that we started at the undergraduate level. We see that this is happening in multiple colleges at Carnegie Mellon, but the statistics and data science one is fairly large. We loosely call it Corp Cap. It stands for corporate capstone and what it is, is a data science experiential learning program where we match teams of faculty and students with real data science research problems that are happening in industry. These partners can be for profit, nonprofit, social services, government organizations, doesn’t really matter. We’ve worked with eight person startups all the way through global Fortune 100 companies. Everybody is using data. Everybody is trying to solve real problems with real data. We behave a little bit like the skunkworks of some of these companies. Oftentimes companies have ideas for what they might want to do with some of their data and they don’t have the bandwidth necessarily to tackle those challenges right now and they partner with Carnegie Mellon to kick some ideas around and try to do some experimentation and see what works and what doesn’t.
Rebecca Nugent (11:52):
We’ve also done consulting on the side, but the real key I think to this program and it’s very early and sustained success, is that it’s really the partnership. It’s the partnership between the students and faculty and our partners. The industry partners, and just using that phrase industry partner loosely, our industry partners are welcome to be as active in the project as they want. We meet with them weekly. We can meet with them every two weeks, three weeks, whatever’s appropriate for the project, but they’re really able to see progress. We get insights from them. We incorporate problem context. These aren’t just data scientists that are being turned loose on the data that you don’t talk to for four months and you have no idea what they’re doing. We really believe in collaboration. We really believe that if you don’t collaborate, you’re not actually doing data science. The more we can incorporate from our partners into solving the problem, the better the solution is going to be.
Rebecca Nugent (12:55):
That’s a program that we’re extremely proud of. It’s relatively new. It’s been going on for about maybe two to three years now. We’ve had so much fun. The students sign up and apply to be in the program four or five months in advance. We have wait lists every semester. It’s an incredibly exciting program to be proud of. I think it’s very representative of the kind of creative interdisciplinary program that Carnegie Mellon would create.
Sean Ammirati (13:25):
Yeah. We’ve had people who’ve done the CSL capstone on the podcast before, so some people who listen to a lot of these like who’ve listened to Gustava’s, for example, interview at MSA has heard about the capstone that we do at CSL. I should say I’ve had startups that I’ve invested in go through Rebecca’s capstone. So I’ve sort of experienced it as a board member and an investor in some of her capstones as well. I think difference in terms of undergrad or grad students, but otherwise and the problems are a little different in CSL creating companies versus creating data science projects. But the output of these is amazing.
Sean Ammirati (14:06):
One of our investments that we had taken advantage of this, and I’m comfortable sharing this because it’s my investment, is a company called Ikos, which is in the real estate space. Ikos helps landlords put tenants in rentals faster than any other approach. The data science team literally built a whole new offering that Ikos is taking advantage of now. By taking advantage of looking at the data Ikos was aggregating and figuring out product and additional value that could be delivered from that.
Sean Ammirati (14:36):
I know that you’ve had larger companies do similar things, where they’ve said, okay we’ve got this repository of data. It’s our incumbency advantage. Let’s figure out how to commercialize it, and your team figures out the offering, maybe not the business model, but at least the offering around it. If you can talk about, even if you can’t say the client name, just directionally the stuff you’ve done around that, I think it’d be really fascinating for this audience for the same kind of work you did with Ikos, but for some of these larger companies. Then maybe once you talk a little bit about that, we can talk about some lessons learned from it as well.
Rebecca Nugent (15:08):
Yeah, sure. I think one of the common themes I’ll say or the common types of products that get built using the data sets from our partners, they’re often based on the notion that we can’t really leverage value from our data until we can actually visualize it and understand it. So quite often what we see is, and this to small startups and this happens to two large companies that have been around for decades. This is a very, very common problem. The data sets, they exist. Maybe they exist in different formats. Maybe they exist in different hard drives and different infrastructures. Thinking about how to even pull those data sets together, that is a common challenge across the board. That is one of the things we’re often tasked with, is trying to figure out how can I even combine these data sets in order to be able to answer questions about them?
Rebecca Nugent (16:10):
Then being able to take the data sets and do visualization so I can really understand the insights. Sean brought up the Ikos example, and thinking about how to visualize the data and understand patterns of your consumers, understand patterns of your customers, understand patterns of your partners. Where are people going? What are they purchasing? What are they thinking about? That helps our partners develop insights that they might not have had before. So some of it is about, we have lots of data, we don’t know what to do with it. Help us think about how to manage and look at our data. So the types of products we’re creating there are more about integrating things that exist and helping people better manage it.
Rebecca Nugent (16:58):
Another theme that goes with that, as we mentioned, is then building possibly interactive visualizations and predictive analytics that don’t require people to be experts in data science in order to use them and to leverage them and gain insights. An example that I might throw out there is we worked with a global construction company in the last couple of years who was interested in trying to leverage some insights on large data sets they had been collecting over the years about incidents that happened at their construction sites. By incidents, it could just be characteristics. How many people are on the project? What types of work were they doing? Were there any accidents? Was it finished on time? Just basic information about the projects. It was stored in a complicated way, using different forms. This is global. There’s different formats depending on who is entering the data.
Rebecca Nugent (17:54):
What we were able to do with that project is build a prototype. This is in three, four months, is that we were able to build a prototype of an interactive interface that somebody who works for this construction company can plug in different values about a proposed project that’s coming downstream, maybe the size, the location, the type of work and other characteristics. What the interface does is it goes and pulls all of the data from similar projects from their warehouse, does analytics on them and returns interpretable visualizations and predictions for how that project is going to go, kind of the life cycle of that project. Where do we expect to see problems? Where do we expect to see different kinds of injuries that might show up so that the project managers can get ahead of that. They can get ahead of what types of problems or challenges they might see, be it weather, be it related to financial issues, et cetera.
Rebecca Nugent (18:52):
In that case, that was quite a bit of data science behind the scenes, but the end product was usable by people who did not have any data science background. I think that’s the key. We’re working with very smart people, but we shouldn’t require everyone to learn how to program Python and all kinds of languages in order to access the information in their data. I think that’s kind of an example of a really key insight for our data science products, I guess, that we’re creating, is that if people can’t use them, then there’s no point. So we have to be able to take these data science models, results, algorithms, visualizations, et cetera, and turn them into something that’s going to be usable by our partners and by pretty much any anyone.
Sean Ammirati (19:36):
Right, and I think the good news is people have gotten more comfortable with these types of interfaces. So you have consumers getting smarter and approaches getting more straight forward where you have them match. But what I find interesting, and I’ll put my VC hat on for a moment, about $30 billion a year flows into AI startups over the last three years, between $30 and $33 billion a year over the last three years. Not that those incremental couple of billion more over the last few years aren’t a lot, but it’s a large percentage of VC dollars. I sit down with startups, I’m overgeneralizing here, but I sit down with startups that are pitching me on like, hey I’ve got the next great AI machine learning start up, and we spend a bunch of time talking and I know a little bit about this cause I sold to machine learning startups, one to Morgan Stanley and one to LinkedIn before those were popular kinds of startups to build.
Sean Ammirati (20:35):
As we peeled the onion back, I realized like, oh my gosh, they’re calling this a machine learning or an AI startup and it’s basically like a pretty simple regression model but built on top of really interesting data sets. One of the things that I think is interesting for corporates is many of them have really interesting data sets already around, so they already have the, in this two step process, get the data, make sense of it like you talked about, and there’s work to make sense of it and then extract value from it. They already have the harder of those two things done because the startups have to go and do a lot of work to solve the cold start problem of getting that data that the companies already have this data as exhaust from other things that they’re doing. I’m wondering if… I know the answer. I know you encounter similar problems and your capstones. How do you walk companies through thinking about situations like that where it’s like, okay we’ve got this asset. We know it’s valuable, but we just don’t quite know how to do the right type of analysis. That second step to get the value out of it.
Rebecca Nugent (21:43):
Yeah, right sure. Probably I spend most of my time when I’m talking to potential partners or I’m scoping projects for current partners, et cetera. I spend most of my time taking people back to the beginning, back to the very, very beginning. I think that it’s really easy to get enamored with all of the buzzwords and the AI and the ML algorithms that seem to be so powerful and exciting and you’ll see they’re written up in papers and articles, et cetera. But what we’re finding more and more is that mostly stable, robust models, like you referenced regression, mostly stable, robust models do the trick when you have a really, really good understanding of what your data are, what the question is you’re actually trying to answer. When we’re walking through companies about how can you better leverage insights from the data that you have, or do you need to be go collecting different sets of data for example, we go all the way back to the very beginning. We actually just talked to them about what are you trying to solve? Don’t worry about the data right now. Just talk to us about the problems in front of you and what are the challenges.
Rebecca Nugent (23:00):
Sometimes it’ll be something as simple as, we’d like to improve customer satisfaction. That’s a very standard question. Obviously customer satisfaction is something that we want to optimize. We want that to go well. But then when we start talking to our partners and like you said, peeling back the onion, they start to realize how many different ways there are to think about satisfaction. What do you even want to measure? You can have an entire 90 minute conversation about how to measure satisfaction and still not even land on the definition. There are so many different ways to think about something as simple as customer satisfaction and it’s going to depend on what’s right for you and what’s right for your product and your company, et cetera.
Rebecca Nugent (23:44):
So of really early conversations about what’s actually driving success for you as a company? What does innovation look like for you as a company? Then trying to understand if the data sets that they are already collecting, if that’s a good match or if we’re able to measure that. You could spend an amazing amount of time trying to just get the question and the understanding of the data right, and then run simpler models at the end and do better than if you rushed through those early phases of that data life cycle and then tried to run something really complicated. More and more we see that the complicated models, if people can’t interpret them and use them, then it was kind of for naught.
Rebecca Nugent (24:31):
Fully understanding the problem before we throw black box algorithms at it, is really the key to success. I think most of our partners who’ve taken that step back and really thought about the problems before they were throwing it into let’s say those black box algorithms, have been very happy with what they’ve gotten on the other side because they better understood what they were trying to accomplish.
Sean Ammirati (24:55):
Perfect. One more question then we’ll move to the couple of wrap up questions. One last question is, I know you also… We’ve collaborated together on some of this, do quite a bit of executive education as well. Where companies say, okay we’ve got a workforce that we need to retool to understand data science, machine learning, that kind of stuff. So we’ve collaborated together on one for Optum, that’s AI for business leaders for example, and built a custom program together for that and there’s hopefully more opportunities to do things like that. If you were giving somebody advice in terms of how they should think about retooling their workforce for this new data heavy world that we live in today, any quick punchy advice that you would offer them to think about as they move forward with retooling their teams?
Rebecca Nugent (25:42):
A couple of things that I might add. The first would be, and this is my opinion, I’m an advocate for this. I think that you want your data scientists to be embedded throughout your enterprise. You might have a data science group still but if the data science group is not consistently collaborating and talking to people throughout your enterprise, you’re not going to be able to maximize their value.
Rebecca Nugent (26:04):
But to do that, and this leads me to my second thing, to do that then you need everyone to be able to talk about data. People spend a lot of money training data scientists on technical skills, which of course are very important. But I think the bigger bang for the buck is being able to increase the data acumen or data-driven decision-making, being able to interpret data, interpret results from models, et cetera, maybe thinking about data literacy. If you can raise the data acumen and the data literacy for your entire enterprise, then that I think is going to be far more impactful.
Rebecca Nugent (26:43):
So the types of executive education programs that Sean’s referencing, these aren’t programs where we’re necessarily spending time teaching people how to code, although we can do that. It’s a lot of teaching people what kinds of questions to ask about data, how to communicate with data across people from different divisions in your enterprise or with different backgrounds, how to collaborate, work together, how to think about tackling those data problems. I think that if everyone within the enterprise can increase, like I said, their data acumen, or their ability to make decisions using data and to ask the right questions, that’s going to be the big win. That’s the big win across the board.
Rebecca Nugent (27:24):
Places like Carnegie Mellon and other universities are churning out hundreds of students who can do all of the technical stuff. Again, if they can’t get their technical work involved in the problem, involved in the application problem, involved in the context, talking with other people who are working on this problem from the business direction. Like I said, data science begins with people and ends with people. So if we can’t take all of this technical work and turn it into something people can use, then we haven’t really accomplished much to be honest.
Sean Ammirati (27:57):
Yeah. I find a lot of companies, they make 80% of the required investment in this and realize 20% of the value because they don’t tool up the rest of the organization that have that acumen.
Rebecca Nugent (28:09):
Exactly, exactly.
Sean Ammirati (28:10):
It’s not a massive investment. The program was custom designed for Optum, but it’s three days in person and six sessions that ended up being about 18 hours of work between asynchronous and synchronous work, between the three. That’s not a massive investment relative to the investment companies are making to hire all of the students coming out of places like Carnegie Mellon and build out data science groups and things like that. I do think making that last incremental investment to build out the acumen of your team so that they can leverage the work being done is, is really, really important. It’s been fun to work with you on that.
Sean Ammirati (28:48):
As we turn to kind of wrapping this up, I did want to end with two questions that I always ask. One is career advice. For your context, I thought it’d be fun, the advice you offer your students. You have lots and lots of students that come through your programs and are embarking at the start of their career. How do you encourage them to think about their careers as they’re starting off in the world today with the skills they’ve built at a place like Carnegie Mellon?
Rebecca Nugent (29:13):
That’s a great question. We have about 7,000 undergraduates at Carnegie Mellon and about 550 or 600 of them are statistics and data science majors. So we certainly are churning out our share. One of the things that we really try to impress upon them is when they’re leaving. When they’re with us, we really try to impress upon them that it’s not just about learning all of the models and learning the code, that it’s about solving real problems. In our curriculum, freshman, on day four of the first week of class, are already analyzing real data with zero coding experience, zero coding knowledge. They’re already analyzing real data in our system, our platform called the integrated statistics learning environment or ISLE. The point being is that they’re working with real data, real problems from the get go, for four years.
Rebecca Nugent (30:10):
When they’re leaving us, we try to offer them advice that these statistics and data science opportunities don’t just exist for big companies that they see all the time in the press or that they hear about all the time. That there are incredibly interesting data centric problems and data centric opportunities in every industry, everything. If they want to work on data science for social good, if they want to move in the public policy space, if they want to go to the tech space, if they want to go into medical pharma pedagogy teaching, you name it, they are well trained to address problems in those fields. We try to get students to look beyond what the name of the company is and try to get under the hood and understand the really, really cool challenges that those companies are facing using data.
Rebecca Nugent (31:06):
Most of our students are very strongly attracted to really making an impact with whatever they’re going to do. We’re seeing a shift away from… They don’t necessarily want to just work for three or four companies. They want to know that their work is making an impact. That’s another reason why they really enjoy participating in our core cap data science experiential learning program is because they get to see this incredibly diverse array of opportunities that they could go do afterward.
Rebecca Nugent (31:36):
So I guess it’s look under the hood. Think a little bit more broadly about where you might find a challenge and know that your skill set is transferable. If something works for you for a few years and then maybe there’s a life change that precipitates, like a move to another place or maybe another opportunity, you are well positioned to go take these statistics and data science skills and transfer them to another opportunity, as long as you’re good at collaborating, you’re good at thinking through problems and so on.
Rebecca Nugent (32:06):
We also tell them to smile a lot and to have some fun because we’re sort of all about the whole person at Carnegie Mellon. We want to make sure that they don’t just see themselves as statisticians and data scientists, but they make sure that they’re contributing to their community, that they’re having a well balanced life and that they’re feeling like they’re being productive and happy.
Sean Ammirati (32:28):
That’s awesome. Okay so last question is an easy one. If people are trying to stay in touch with the work you’re doing, where’s the best places to find you online?
Rebecca Nugent (32:38):
Yeah, sure. One project that I think people might be interested in that you can find online is the integrated statistics learning environment or ISLE that I just referenced in the previous question’s answer. This is a data analytics platform that we use for things like executive education. We use for freshmen through graduate level work at Carnegie Mellon University, but it’s ways to interact with data and learn about data without requiring high levels of programming skill. For example, in executive education and for freshmen courses, there’s no coding whatsoever. So people can learn about data science. We do research on how people do data or how they work with data through the ISLE platform, including how people collaborate, how to optimize data science collaborative teams. That’s something that we’re really excited about. Our new platform allows people to work on data science problems or data analysis together, but from different locations of course. They’re all working in one platform together and we can track action logs and users and decisions and we can help people develop the optimal data science team.
Rebecca Nugent (33:48):
If people want to learn more about that project, you can find it at stat.cmu.edu/isle. That’s I-S-L-E. So stat, S-T-A-T.cmu.edu/I-S-L-E. If people want to find me, I can be reached… Again, my name is Rebecca Nugent, and I can be reached at rnugent@andrew.cmu.edu. R-N-U-G-E-N-T@andrew.cmu.edu. You can also find me online stat.cmu.edu/~rnugent. Or to be honest, you could probably Google Rebecca Nugent Carnegie Mellon’s statistics and find it just as easily.
Sean Ammirati (34:34):
So I’ll make sure to include a note to your faculty page and the ISLE in the show notes. Rebecca, this was fantastic. I really appreciate you making the time today and hope you guys continue to stay healthy and safe there.
Rebecca Nugent (34:46):
Oh, thanks so much. You too Sean. Thank you so much for having me on and I hope you guys are doing well as well.
Sean Ammirati (34:51):
Awesome.
Sean Ammirati (34:57):
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