ANGELA BUSHESKA: Yeah, absolutely. Thank you so much for having me. Super excited to be here and super excited to have spent three months—time flies!— as a researcher along with the project. So my name is Angela. I am originally from North Macedonia, a very small country next to Greece in the Balkans. And I spent my first, like, 16 years thinking that I would become a mathematician, really focused on math Olympiads. And then I moved to the capital city of my country, which is Skopje, North Macedonia, and that year, in 2019, we had the greatest pollution. We were, like, on the top of every single list in terms of pollution, but honestly, I didn’t care. I was like, I’m going to do, like, math Olympiads, win every medal. I moved in with my aunt, who was living very close to the city center, and she had a lot of cardiovascular difficulties. And then this air pollution clogged her blood, and she passed away. And that was the moment when I saw like, I love math, but this air pollution took my aunt, so I have to do something. So I made the hard decision that junior year going into senior year to cut my participation in math Olympiads after seven great years there and focus on the climate. I really just started to understand where is the pollution coming from in my area. I started to realize that fast fashion had a huge influence, and then that’s why I started, like, getting into this field, getting into research, started a climate tech nonprofit called EnRoute, where our mission is to debunk the fast fashion supply chain and let people but also corporations know what is behind their clothes. So we, kind of, help both sides. We help people with their shopping choices, but we help a lot of corporations and policy to understand either what’s behind their supply chain and change it or how to better communicate their supply chain because there’s a lot of greenwashing that is happening in this space. So it really started as a rag-tag group of teenagers, and this is my fifth year working at it. So I really had a passion for both understanding the research side of it and the nonprofit, like, getting people to learn more about it. So I think that marked really my presence. And then coming into the US in 2021, I started to study both electrical engineering and computer science just because I love this intersection of building and doing stuff in practice and a way to apply my math knowledge into real life.

RANGANATHAN: Wow, so early in life you have such amazing goals. That’s impressive. So, Angela, we connected after Madeleine Daepp, a researcher who was involved in the intern selection process, introduced your work and your passion for sustainability to me. I saw your passion for this area and, you know, your enthusiasm in the first conversation—we were already working on ideas, right! I knew I had to work with you. But for you, there are many other internship opportunities. I would love to know what excited you the most about Microsoft Research.

BUSHESKA: Absolutely. Yeah. So as I said, like, I started getting into this area of sustainability by myself. I really didn’t have a structured way to learn. So when I heard that there is an opportunity from people who have, like, previously worked in this area, are doctors in this area, to learn along them, that was definitely one. But also throughout my life, I had like entrepreneurship stints. I was selling lemonade when I was [LAUGHS] 6 years old; started a nonprofit. And this was a great opportunity for me to start a zero-to-one project while being an intern, which is, I would say, a once-in-a-lifetime chance. We had a chance to define the problem, see solution, try things. I was not part of just one project or building one feature. I had a chance to drive it along you and the team. So that was, I think, the main reason why.

RANGANATHAN: I’m glad we were able to attract your attention here. [LAUGHS] Before we get into specifics on, you know, exactly what we did this summer, I’d like for us to talk a bit about your internship setup, right. I think it allowed for some really unique experiences, and, you know, I would love to have you share your internship highlights. I think there is one in particular that I’m thinking about, which I really hope you’ll share.

BUSHESKA: Absolutely. I would say this was an internship like no other experience I’ve had in the past. So just for context, I was interning in New York while part of the team being in Brazil, part of the team—you and the other researchers—being in Redmond, a couple of other researchers, like, being technically in New York. So I had a chance to interact with all of these different time zones. Also for an additional context, I was a part of undergrad research internship group where they brought us to Seattle from New York—and also there are other interns from Boston—to spend like a week and learn more about Microsoft, Microsoft Research, the leadership. So that was an incredible opportunity. Also, I had a great opportunity prior to that to come to the team meeting to see farms in real life, interact with farmers, interact with the team in person, which was also another great opportunity. And I’ll come to the one that you’re referring [to] here [LAUGHS]. So the second time when I was, like, visiting Seattle, I remember a week prior, it was Friday, we were finishing up some stuff. You actually pointed out that I should definitely message some of the leadership people to interact with them and hear their insights. And it was EST time zone, somewhere around 8 when I left the office, and I shoot an email to the chief sustainability officer, Melanie Nakagawa. I was like, “Hey, my name is Angela. I’m a research intern. I have worked in, like, fast fashion in the past. Now I’m working with Vaishnavi and the team on this food project. I’d really love to meet you next week. [LAUGHS] I’m coming for like three days.” I sent that email. I was like, there’s no way I’m getting response from this. And then on Sunday, I traveled to Seattle. And I remember during the first day, assistant of the chief sustainability officer says, like, there is this Bloomberg Green Festival that is happening in Seattle on Thursday, and we would love for you to come and meet Melanie. It was, like, I don’t know. It was like real life or not. I really couldn’t … it took a lot of time to process. And after that, one thing additional that was a barrier was how to get into this festival because this festival had a ticket of like $300, $400. And I think that by that time everything was sold out. So I was like, I would love to, but I cannot just get into the Bloomberg Green Festival without a badge. So then I realized there is this sustainability community at Microsoft that is like 8,000 employees or something, and they got badges previously for the festival, and everyone who is working there can just get the basic badge just to get into. So I emailed a couple of people from there; they set [me up] with a badge. Huge props also to the undergraduate research team who extended my stay for a day because I was supposed to leave for New York the other day. So a couple of miracles happened there. I had a chance to go to the Bloomberg Green Festival, had a chance to meet with Melanie and her assistant, Tyler, who was working also in this area of sustainability in her presence on, like, these events. It really goes to say how an undergrad intern can, like, meet with leadership and learn from the leadership to the place and the opportunities we had during this internship. Thank you so much Melanie and Tyler for taking out the time at the Bloomberg Green Festival. I know it was a super packed day for both of you, but thank you so much for taking the time.

RANGANATHAN: Thank you for sharing that. I want to point out something. It’s amazing how the leadership makes time and they really value every person’s work, and that’s an internship which is accounted for now, right, and you know your work is meaningful to the company. I think Microsoft is so big that people don’t realize there’s this community of 8,000 people in sustainability often, so it’s amazing. It blows my mind every single time, and kudos to you for following up on my late-night comment. [LAUGHS] Um, Angela, so this internship falls under larger efforts within Microsoft Research to establish a sustainable agriculture and food supply chain, a project that we know as FoodVibes. In your work this summer, you focus specifically on enabling technology to meet the new European Union’s regulation around deforestation-free products. Could you tell us, in your words, about these regulations and how you think the work can aid in their implementation?

BUSHESKA: Absolutely, yeah. So the European Union’s regulation on deforestation-free products, also known as EUDR, is a legislation that will prevent food linked to deforestation to enter European Union borders[1]. But how will the border officials know if something is coming from a deforested area or not? On another side, it’s also the farmers from this area who don’t know what exactly is a deforested area, especially the ones who moved after maybe an area has been deforested. So there are a lot of questions around there, and I think that getting into and understanding the problem was a huge part to continue after that in building the solution.

RANGANATHAN: Yeah, and I believe that having a large team here really helped because we all often had discussions around many of these areas and we really valued your inputs and how you participated in these discussions, right. So through your internship, we’ve seen that there are a variety of tools, you know, and vegetation metrics at our disposal to help determine what exists on a plot of land because that’s what we want to identify. There’s, like, satellite imagery. We get the normalized difference vegetation index, and then there’s the USDA’s cropland data layer, which exists purely for the US. But, you know, we are getting an incomplete picture here, at least as it pertains to this particular use case. How do you think TerraTrace brings these pieces together, and what are some of the specific challenges or motivations of the core of your work?

BUSHESKA: Absolutely. Yeah, I think, like, a couple of the first weeks, we tried these great machine learning models, and one thing we realized is that when we have, like, orchards, and when we had pine trees, and then when we had, like, regular forest, all of them are classified the same, which was a really great light saying, like, yeah, these are great. These are models that are trained by millions of data. They still don’t work as we want to. And also, I think that in an era where everyone is, like, training big great models, we kind of decided to take another route and say like, models are great. What if we go step by step and maybe we don’t need a full model round. Maybe we can go step by step to a certain point to try to understand deeply. And I think I’m really grateful that we took this direction because we had a chance to understand at a granular level what is actually happening. Here we had, like, we were lucky at the beginning to work with farms in Washington. We had, like, an actual farmer who was in the team who we have, like, pictures and we had all the data. So that was a great ground truth for us to understand, OK, this is how the vegetation looks on a farm. This is how the vegetation looks on a forest across years. So we had these snapshots and then from then on, we just moved along to say, well, if we know about this, what’s happening in Washington, how we can scale now to other places in the US. We looked deeply into California just because it has a lot of, like, agricultural diversity. And I think that this step-by-step level brought us to create TerraTrace. TerraTrace is a platform where one can enter, like, the coordinates of a farm and see what has been happening on a given piece of land across time. Now, TerraTrace combines a couple of different things. It combines mathematical results, combines LLMs, and combines just, like, the basic information of risk. And I think this is good just because it’s not a heavy computational platform that we have. And if we need to use it on, like, a couple of years from now on a farm, we technically can. If we need to use it, like, in a legal office, we can. Just because it doesn’t depend a lot on data and it’s, like, easily portable, which is another plus, and differentiator compared to what exists outside.

RANGANATHAN: Do you want to share a little bit about the signature curves that you identified?

BUSHESKA: Absolutely. So when I just, like, realized … there is a metric called NDVI (Normalized Difference Vegetation Index), which is basically measuring vegetation. It is a time series of vegetation. What we did with NDVI is we tried to, like, measure it across time on different places. And I think in coffee, it had the greatest impact. Just because there was a coffee in Vietnam, coffee in Honduras, completely two different places. And then when we analyzed and saw the shapes, we were like, wow, they are completely two different places; however, they still follow the same trend and that was a great calling to say, like, this is a greater metric. Maybe if, like, vision computer models failed, this is something that is, like, very rudimentary. You wouldn’t expect that just a very simple index would calculate that, but we saw it working for a lot of different places, and I’m particularly grateful for the places that were not together at the same place.

RANGANATHAN: Yeah, and I think, like, looking at this, like, temporal, over-time picture is the unique aspect of what you built and that was what unlocked it for us, right. So you hinted at this. In developing the TerraTrace platform, you ended up combining large language models, or LLMs, and statistics. What did you like about this approach? What did each bring to the table? How did it contribute to your ultimate goal? Could you share a bit about?

BUSHESKA: As I said, like, at the beginning, I’m a huge math person, and I truly believe that math has more power than we credit today for. So one thing that I started building, when I started building, I was just, how can we get as much information as possible without LLMs and then give it to the LLMs? That was kind of the approach that I started. So we have this, as you mentioned previously, crop data layer that was the base truth. So I could always go back and compare to see if it has, like, if we are doing the same thing. Then we had the signature curves that we could compare to base signature curves to understand, OK, it’s coffee or it’s a farm. And now when we were having a farm, we had a problem because corn looked the same as wheat. Multiple crops look the same. So I needed something more to differentiate what exactly is there. So by math, I managed to understand like the growth rate, the fallout rate, what is the percentage when the curve is up, how much percentage is down so every insight that we could get by just basic statistics. And after that, all this combined knowledge was given to the LLM to, kind of, just confirm that the math is right and give us, OK, greater insight into, well, your math is good, and after that, based on your math and everything the LLM knows, well, there is this probability that it’s corn and after that we came to the crop data layer to say, yep, it’s corn. Referring to corn just because this was our example demo. Also, LLMs are changing very rapidly today, so like GPT-4, GPT-4 Turbo, expecting a GPT-5, so constant development needs to be done, and I think that if something is, like, changed and doesn’t work in the platform, one thing that will always work is the statistics. So having something to always refer to was a very interesting process.

RANGANATHAN: Yeah, I really appreciate, you know, how we had the focus on, hey, let’s do what we can with math first because, after all, this is a sustainability project, right?

BUSHESKA: Exactly.

RANGANATHAN: So LLMs were great at summarizing this, taking all the data and, like, giving you the outputs. I think that was a very interesting approach, too. So there are several existing pieces of work we’ve seen in this area. We’ve relied on a lot of literature in this specific application, right, that ended up havingauthors who were affiliated with Microsoft. Could you share a little bit about those works that we drew on and how we leveraged the Microsoft connections?

BUSHESKA: Absolutely. One thing that was something I was not expecting was reading the papers and then hopping on a call with the authors. So one thing that I would credit is first we understood … there was this, like, GeoLLM group. They actually had a presentation in our weekly meeting for the group. So it was, like, me just staying in the presentation and trying to learn. And the other day, they send out like, this is a research paper; take a look at it. And after, like, we realized that the majority of the authors were in the same building as you actually, we had a great chance to, like, meet, like, with them and understand how …

RANGANATHAN: This is SatCLIP, right?

BUSHESKA: … SatCLIP (opens in new tab) and GeoLLM (opens in new tab), both of them. Trying to understand, like, how they have built it, what they have built, why, how they are planning to continue, how I am able to use it. And, actually, because when you’re reading a paper and you’re reading GitHub repo, it’s one thing. [LAUGHTER] When you’re speaking with the author and seeing, like, this is why we’ve built it, here’s the limitations, be careful how you’re using it, it’s completely another thing. So I am really grateful for this situation. But also, not only, like, inside of Microsoft, we also had a chance to work with a lot of other folks. For example, we read a lot of papers from research groups in universities. And because our interns came from these universities, it was, like, great to understand from them also what they have built.

RANGANATHAN: Nice, and the other interns whom you worked with, which we’ll come to. So for this specific work, we were motivated by the use case. What were some of your findings, and also how are you envisioning this work as a foundation for other supply-chain or even, you know, broader sustainability scenarios and even beyond those fields that you can think of?

BUSHESKA: Yeah, so one thing that we, as a conclusion, came out from here is that signature curves are not just by look. When we tested that with a lot of different other signature curves, we realized, OK, this is true because in most of the cases, they worked. Obviously we had some failures, like citrus signature curves didn’t work because of other reasons that happened on the farm. But for most of them, we got same results on what’s happened with the signature curves with what happens actually on the farm, which is a great way to further this exploration here. Another thing that we realize from this project is now the group previously has worked on trying to understand supply chain, like having the tracking part and having this code to track along the journey, but we were missing the part on what happened on the land. And now that we have this additional way to understand what happened on the land, I think it is a full system of starting this is what happened on the land, this is what happened across the road, and this is where it is now, so that not only EUDR, but even, like, customers in the future will know where their food is coming from, which is that total part of sustainability that we want to get to. Because I think that at this point, we know the challenges, we know the climate change challenges that we are having, and the more information we have, the better decisions we and policymakers would be able to make.

RANGANATHAN: And I think we realized this and you can chime in, Angela, but I feel that the traceability is a vehicle for data. What you build is a means to take all that data and make it meaningful to people, right. Like what does NDVI mean? I have no idea as a layperson, so you can actually get that information out. There were also a few other use cases that you gleaned out of your data input, right. Like wildfire was one of them. Would you like to share a little bit about that?

BUSHESKA: Yes, so we were looking specifically about California, specifically about 2020, which was a season of very, very bad wildfires. And we could see by measuring this vegetation how, like, wildfires affected these regions. Like, you could see how vegetation was dropping very quickly from a great number of one to just, like, flat zero just because everything was there burned. So seeing firsthand on these curves and after that linking up to a lot of other background wildfire data was a good check-in. Like, you could see that it’s not a person deforestation; it’s a wildfire deforestation. And also it is really helpful for risk estimation as we go further along. We could see, like, this past year climate change effects are there. It’s not just like this fancy term in the future. It is there. So I think that having a chance to see in the past and having these models, it’s also giving us a preview for the future. Because ultimately we want this greater food yield so that more and more people can enjoy healthy food. So having a chance to predict the risk would help us to save more food and, yeah, care better about our planet and our people.

RANGANATHAN: I think there’s a lot of potential. The more information you have, the more applications in sustainability you can build. This is merely, like, a footstool to launch it off, right. You’ve said one of the things you’ll miss most from your internship is the density of smart people in the building and in the company. Over the course of your internship, what did you learn? How did your actual experience compare to, like, expectations you had for it?

BUSHESKA: I think it exceeded every single expectation that I had because I would say when you mentioned the density of smart people, it was really incredible to have—and I think that this … maybe my very interesting connection of being in New York while working with people from Seattle and Brazil allowed me to have a broad range of people that I had, like, had a chance to meet. So, like, the New York office, it is very specifically focused on economics and social aspects that I’m not an expert on, but having a lunch with these people every single day, I had a chance to learn a lot about how, like, human data is provisioned. They were building a lot of things for, like, Microsoft’s new projects and new products that are, like, the Copilot and the computers, so I had a chance to hear about those perspectives. Being in Seattle and meeting with interns and researchers in Seattle, I had a chance to learn not only about the farms perspective but also how other projects came to life. With our meetings, I realized how FarmVibes and FoodVibes were once just, like, a small idea and now are these huge projects. So having a chance to understand the history of all the things around me was a great way to see how I am able to build something for the future. And also maybe another thing is the collaboration that happens. As I said, I was, like, surrounded by a lot of socioeconomic people—that I am not really an expert in—but they were really great with providing me advice from an area I would never think of. So when I was speaking, like, from farms to someone who is in economics, they would raise another point on how this can hurt or help economy or to look at it from another side, which is all great perspectives to have when you’re creating a research from scratch. That’s why I really, like, referred to entrepreneurship previously because I feel like even though was an intern, I really had a chance to shape the project in a way that I would get all these insights from people and then obviously decide how to go further.

RANGANATHAN: Yeah, and I want to add summer with interns is the highest energy level at MSR (Microsoft Research) and the best time of my year at Microsoft. So having successfully completed your internship, what advice do you have for our audience, you know, when it comes to applying for an internship like this in industry and then getting the most out of that experience?

BUSHESKA: I would say one of the things that really maybe helped me through the process is, like, working obviously, in an area that I was really passionate about. But another thing, and especially this is coming specifically for the marginalized communities in tech—although I would say MSR is doing a great job in having this balance. But I’m coming from a liberal arts college where we have, like, two girls that are studying electrical engineering—so having this disbalance can in some ways, like, I don’t know, put you, “I’m not good enough” and all this imposter syndrome. So maybe one thing is definitely, like, reach out to people. And something that I really learned heavily from you is reach out. There is nothing you can lose. Most of the time, it is just … getting a no is maybe the hardest answer that you will get. So having a chance to ask is always a good idea. Obviously, don’t go to, like, every single executive to ask questions, even though it paid out—it paid out [LAUGHTER]—and just, like, ask for help from the people around you. Ask to learn. I realized, like, researchers really want to speak about what they have worked on previously. So whenever you get a chance to ask them about their past project, they’re super eager to tell you about like their grad days and how they have become what they are now. So that was also great. And in terms of applying, I think I would … when I applied for this, I was like, there’s no chance I will be able to get in. But for me, as an introvert turned into an extrovert—fun fact, I was a big introvert in the past who turned into an extrovert just because of my work—it’s like if you don’t apply, there’s a 100 percent chance that you’re not going to get in. So, like, when I interviewed, after my first interview, I was like, yeah, there is, like, no chance. Because, like, I presented the projects, but I was like, there are so many other students who I know that are working in these areas and then it happened and then I’m here recording this podcast. I remember it was Thanksgiving break when I applied for this internship. If I was like, no—if I self-rejected myself previously, yeah, this would never have happened. So I would say for all interns out there, just, like, don’t self-reject yourself. Let them reject you. [LAUGHTER] It’s my really advice.

RANGANATHAN: And, yeah, have more confidence. I’m glad you applied and you joined us because this was a highlight of the summer for us, this was, right. So your work with Microsoft Research is one of your several projects. You’ve been involved with many other things, right. What else do you have going on? How does it contribute to the impact you’re looking to have at this point in your life?

BUSHESKA: Yeah. So one thing that I realized in the past four years is that I started … so my, like, journey was very, I would say, interesting and not predictable just because when I, like, graduated from high school, I got trapped by all the COVID pandemic. I couldn’t fly into the US just because of all the visa regulations. It was like, OK, a gap year at home, which if you asked any of my friends before, I was never the person who would take a gap year. So staying that year really, like, without a school or official job was the chance for me to just look at and start my nonprofit, EnRoute. From then on, I worked with policy. We worked at the UN. We even went to Hollywood in March to speak about, like, fast fashion. So I realized there is no one way to make an impact. So I was really glad to join research, to join policy, and speak wherever I can. I think now I’m getting back into my senior year of college. Time flies. And something that I am, like, looking definitely into is how to continue my research in this area, where I most probably would love to be through grad school. Having a chance also to push further this project just because I think that speaking out and sharing research is one of the, like, greatest things we can do, like referring to all the other times when I was speaking on conferences. It will be magical for someone to respond with positive and negative feedback and maybe even can create a collaboration for us to move forward. So that is definitely something that I’m looking forward to. So definitely this area is something that I got in by mistake. I got in really just by … because I had that great time in my senior year to spend more time research, but it definitely something that marked my life at this point. And I think that, like, whatever route I pursue will be to fight climate change in any way or another.

RANGANATHAN: I want to point out we are also planning to open source this and you hope to work with people, you know, to get it in the hands of people who need it and could benefit from it most, right.

BUSHESKA: Absolutely.

RANGANATHAN: So that’s a great direction. I think every year our undergraduate researchers set a new bar in my life expectations. And I thank you for that. This year is another bump up. So, yeah, you are a senior, Angela. What’s next for you, and what’s next for your research and work?

BUSHESKA: Yeah, so this will be a fall really spent with a lot of applications because of, like, graduate school. Definitely something that I’m looking forward, especially graduate school in this area. So we kind of spent a lot of time living in an era of GPT wrappers, and I think when there is a product out there that is really based on research, it can make a tremendous impact. As I said, I am really a person who loves to experiment and do things. So something out there is definitely either, like, having a research that is spun out of this or having perhaps a startup down the line that is based in this area and helping millions of people around the world is definitely something I’m looking forward. Because I saw the impact when you’re helping other people, and I think that is the best thing that one person can do. So I was really excited to do it with all of you this summer and obviously continuing on to open source this project obviously for feedback but also for real use. And if we can help with something, that will be amazing. Hopefully getting these out to conferences around the world. And, yeah, see where it can take us.

RANGANATHAN: Yeah, I have one last question for you. You had some interesting intern mentors, myself included, and then Bruno Silva, who sits in Brazil, Peder Olsen, and Solon Barocas. Do you have anything to say to all of them? [LAUGHS]

BUSHESKA: Yeah, I would say a big thank-you for keeping up with my questions. As all the listeners can probably see, I’m a person who really speaks a lot, even more than needed sometimes. So I would say I had a great balance because I was in a time zone that was between Brazil and Redmond. So I would spend a lot of my mornings, especially in the beginning—huge shoutout to Bruno for dealing with all of my technical questions [LAUGHS]. It was my first time setting up a lot of research clusters. We should not forget also the people at Microsoft who are dealing with all of the GPUs and stuff because I think that managing all of these researchers for all the people out there is a hard job. So having a chance to communicate and also understand. Even though I study electrical engineering, I never had a chance to deeply understand how they’re used in real life. Speaking also with people from all different walks of life because Peder, my other mentor, was someone who has been really experienced in this area, so he would reference projects from I don’t know how many years, and it was, like, so fun to see the history, to see his previous experiences come to life. You don’t every single day get to have like three or four mentors from all different areas and aspects. So I’m really grateful that I had a chance to talk and also get called when things were not right so we can make them better and more correct for the people that will use this project.

RANGANATHAN: I’ll tell them you said thank you. So, Angela, I think we are coming to a close here. Thank you so much for taking the time to share your experience with us. It’s been a pleasure to work with you, and I really look forward to seeing what the future holds for you and what amazing things you’re going to achieve. So thank you. Closing out.

[MUSIC] 

BUSHESKA: Thank you so much. Really appreciate to have this opportunity. And for everyone that is, like, working in this area, for every future intern that is doubting themselves, feel free to find me out there and, like, ask. Always happy to help people, always happy to chat sustainability with people. So thank you so much.

[MUSIC FADES]

MADELEINE DAEPP: Welcome to Intern Insights, a Microsoft Research Podcast featuring the brilliant students who are contributing to the research and advances at Microsoft as part of the renowned internship program at Microsoft Research. I’m Dr. Madeleine Daepp, a Senior Researcher at Microsoft Research, and today I’m talking with two of my interns, Jennifer Scurrell and Alejandro Cuevas Villalba, about their work and their experience this summer at Microsoft Research. Welcome, Jennifer and Alejandro! 

JENNIFER SCURRELL: Thanks.  

ALEJANDRO CUEVAS: Hi. Glad to be here.  

DAEPP: So, of course, I know both of you, but many of the listeners do not. So can you tell us a little bit about yourselves? Let’s start with you, Jennifer. 

SCURRELL: Yeah, sure. So I’m from Switzerland. I live in Zurich. I’m a PhD candidate at the Center for Security Studies at ETH Zurich. And I’m actually a political scientist by training. So I did my bachelor’s and my master’s at the University of Zurich in political science, and now I’m actually entering my last year of PhD-ing at ETH. And what I look at in my dissertation is how AI-enhanced bots influence political opinion formation online. 

DAEPP: Awesome. Well, it’s been so great to have you, especially with that background, and we’ll talk a little bit about how relevant that is to some of the work all of us are doing this summer. Alejandro, how about you? 

CUEVAS: Hi. Thank you, Madeleine. On my end, my name is Alejandro or Alejandro for our Latino listeners! I come from an infamous area of the world called the Triple Border, specifically Ciudad del Este, Paraguay. I grew up there all my life and came to the States then to study. I’m currently at Carnegie Mellon University studying societal computing. I’m going into my fifth year, and I would say I’m an interdisciplinary researcher by force—or by chance! It’s something that I happened to end up after all the work that I did in undergrad and then subsequently in PhD. 

DAEPP: Can you tell me a little bit about what societal computing is? 

CUEVAS: Yeah. Broadly, the way that I would define it and the way that I approach it is essentially how computer science technologies shape or impact society. And that is very broad but indeed is a very broad field. 

DAEPP: Right. And it’s extremely relevant right now as we’re seeing society being reshaped by some of these new technologies. So I think a sort of next question that I have for both of you is, you know, there are so many things you could have done this summer, and I’m really curious how you ended up at Microsoft Research. So maybe, Alejandro, why don’t you start this time? 

CUEVAS: Yeah, broadly, the story that I’ve told a few times already to you all, but I’m happy to share over and over again, is that I interned here in 2016 as a software engineer, and I happened to attend a research talk by MSR (Microsoft Research) at the time, and I was a sophomore in an undergrad at the time, and I thought I didn’t really know much about research, honestly. I didn’t know what a PhD really entailed. And so it was my first time sort of seeing this whole nother line of work that I could be doing. And even though, you know, I liked coding in C# at the time, I think the … seeing how researchers in CS were working on mosquitos for whatever reason at Microsoft of all places was just eye opening. And so after that, I started doing research at school, and two of the mentors that I had in undergrad, they had met at MSR in an internship a little while ago, and they both spoke very highly of it. They both had a great time. And it always seemed like whenever I went to conferences or saw papers that I liked, I kept stumbling upon researchers that I admired and that, you know, we’re doing super impactful work, and it almost seemed like a rite of passage that all these people went through. And, you know, I said, I want that. I want to experience that, as well. So I waited a little bit for the pandemic to dwindle down and not have the remote work again because I knew I wanted to be here in person. And then once that happened, I tried my best to, to, to get here. And just yesterday, I shared with my colleague Eva Brown, who is another intern in our group, and I was telling Eva and Jennifer, who’s here in our talk, as well, that I had to cold … you know, I went ahead and cold emailed Glen Weyl, who’s the director of the Plural Technology Collaboratory, and I said, hey, I’m super interested in this job position. I really want to be there. I have these ideas, and I hope you will consider my application. I really wanted to be here this summer. 

DAEPP: That’s so fascinating, and I can really empathize with that feeling of sort of learning about Microsoft Research and then starting to see it everywhere. Like you see on conferences. There’s often, you know, a coauthor from Microsoft Research. You look at people’s backstories. And you start to realize sort of how many people across computer science, but also computational social science, also other fields, really sort of have some time that they spend in this sort of magical place. So, Jennifer, what about you? 

SCURRELL: Yes, so I can only second that. So because I’m just entering my last PhD year, I have to decide at some point where I want to go. Do I want to stay in academia, or do I want to go to industry? And I said to myself, OK, to really find out what would suit me best, I really … I need to do an internship in, in the industry and especially, as well, because what we just discussed, right? You look at papers and see all these impactful papers with a lot of MSR researchers as authors. And so one day, my PhD colleague in my lab actually sent me the link to the internship advertisement. I was like, OK, this is just perfect. First of all, it’s Microsoft Research, which is one of the leading research facilities in tech industry. And second of all, the description was just … it was just matching. And I tried my best, and, yeah, now I’m here and I’m so happy that it all worked out! 

DAEPP: That’s so funny. So just to give you both a little bit of context about some of those internship postings, you know, on our end, we sort of had this set of projects and areas that we were so excited about in the fall of 2022, and then of course in the spring of 2023, the world starts to change here at Microsoft Research, right. Like I start using ChatGPT every day and telling everybody in my family about it. In February, we get our first glimpse of GPT-4, right, through Bing Chat. And then I was sort of walking around and every day somebody is finding some new result about GPT-4 and theory of mind, or GPT-4 and discovery of causal graphs, or, you know, even that it can code 3D video games. And so we started to think, oh, there’s really an opportunity here, and we need to think about what our research is, what’s sort of most impactful. And so especially for a computational social scientist like me, it was sort of like there’s a new microscope, right. There’s this brand-new tool. What can we do? And so, for Alejandro, I think you applied to this sort of like web3 cryptocurrency project with Glen Weyl, and then you came in and I told you I wanted you to work with large language models. Tell us a little bit about how you brought a growth mindset to that context. 

CUEVAS: Yeah, it’s really … it’s really funny that you’re mentioning that. I remember going through the interviews at the time and I kept wondering, why are they asking me so much about large language models? Because from my conception, I still thought that I was coming to work on web3 and DAOs—decentralized autonomous organizations—and I had my whole pitch around web3. But then all the interview questions were around large language models, but luckily, I mean, I spend a good amount of time observing internet phenomena, and it was hard to miss the LLM sort of rise, and I was happy that I could answer all those questions. But indeed, I came here, and it was a big emphasis on figuring out a project that was quite different from what I had originally envisioned. But I didn’t really mind. I mean, I wanted to do this internship either way, and throughout my research career, I had the privilege of always being able to tackle different types of problems. I think, just in undergrad, I worked all the way from usable security on one side and all the way to systems security on the other side, and so jumping headfirst into a new problem was just an average PhD day. You know, it’s just another project that needs to be thought through and scoped out and carried out. And I don’t think it ever detracted me too much that it was something that I was not super familiar. Of course, it’s always difficult to venture into a domain that you have a lot to catch up. But it’s also a fun and rewarding experience. It’s a body of literature that I got the opportunity to explore around really talented individuals. And a lot of the literature search that I would have done on my own was facilitated by all these people that were already thinking about these things. So it was a very fast-paced opportunity to acquire a whole new skill set that I think would be relevant to the rest of my work. And so when you mention growth mindset, I think it was just really an opportunity to fully come in and absorb an area that I know it’s going to be impactful—I know it’s going to have benefits on other areas of my work—and, you know, let’s go at it! 

DAEPP: Can I just jump in there? I think something that’s so interesting in what you just said was, you know, sometimes when you’re doing your PhD, you’re really sort of doing a literature review, right. You’re really sort of out there searching for article after article, reading textbook chapters, trying to get up to speed. Maybe you get to take a class. But here what I saw you do was you sort of went door knocking, right. You said, oh, you know, here’s the world’s expert in this area. Or, hey, I need to do something conversationally interactive—who do I go talk to? So can you talk a little bit more about sort of what it looked like to, to ask other researchers, ask other PhD students, ask other interns for help? 

CUEVAS: Yeah. Absolutely. I think one of my favorite things, which I will definitely miss outside, is in meetings, there’s this culture of sharing links and references as the meeting is going on, and all of a sudden, you end the meeting and you have this body of work in your chat log that you can go and, you know, sift through. And people did that so much at the beginning and throughout the internship that we would be talking about some topic and there will be three references on the chat, you know, regarding that topic, three papers to read. And I think that was really, you know, it was eye opening in the sense that I didn’t have to, you know, go to Google Scholar and figure out where to find papers. I … there was a network of people around me already that had all this expertise. And so I think it played a phenomenal role from the get-go of facilitating those connections. I think within my first week or two, you had already introduced me to two researchers that were working on areas that I thought were interesting and also that were relevant to the work that I wanted to do. And so having sort of that, from the beginning, really made it easy to then feel comfortable and, you know, empowered to just reach out. You know, that’s something that, from the get-go, people would tell me, you know, just reach out. They’re around the corner. Just go and have a meeting with them. And so, I figured, OK, you know, let’s do it. Why not? 

DAEPP: Jennifer, that’s something that you did a lot of as well. You reached out, you sort of asked people for coffee chats. Can you talk a little bit about that experience on your end? 

SCURRELL: Exactly. I just wanted to say, can I jump on this? So it was amazing. The first week, we had our initial meeting, and you said, hey, this is … this summer is an opportunity—reach out to people. Network. I was like, OK, uh, sounds easier than it might be, question mark? But it turned out to be so easy! It was really, you know, you just drop an email, you drop a, a message in Teams: hey, I’m interested in your work. I want to learn more. Can we go for a coffee? And next day, I was actually really with this, you know, super-famous researcher drinking coffee in the Building 99 atrium! And I learned so much in 20 minutes I could not read, I don’t know, 10 papers about the issue I wanted to discuss with this person. It’s really amazing how especially also open people are to, yeah, just meet and chat. And that was a really great experience, and I really enjoyed it a lot. 

DAEPP: Alejandro, you also had a researcher that you wanted to get coffee with. Tell us a little bit about seeing that researcher in the halls versus how you finally decided to ask them to meet? 

CUEVAS: Oh, that was so nerve wracking. This was Cormac Herley, who was just a few doors down from my office, and I would see him every day almost at the kitchen when I would go get coffee and I would look at him; he would look at me. We would do, you know, like a passive nod at the beginning. And eventually—I think only eight weeks in—I finally sort of decided to say, “Hi, hello, my name is Alejandro,” and I completely butchered actually that introduction because I was so nervous. I think my Apple Watch, you know, gave me the “high heart rate” notification. And I think I even said, “Hi, I’m, I’m Cormac!” or something like that. And he was so confused! And I just said, you know, like, I really like this work, and this work, and this work. And he was like, why are you citing this work from 2009? And I was like, it’s just so good, you know? And we happened to sit down right then and there, and I think we chatted for about an hour. Again, just being able to see somebody that you, you admire their work and you, sort of, admire their ideas and then sit down with them, have coffee, interact, chat, and see sort of how they’re thinking about future research problems … I think that was a very exciting thing, and I think it’s one of the things that was in my bucket list, and then I definitely crossed off from the summer! 

DAEPP: I think this really speaks to the power also of the in-person internship. This is my first year as an in-person mentor, and I’ve had wonderful experiences with remote intern groups, but this has been really special because I think we’ve been able to, you know, have these sort of in-person meetings and also a very intense bug bash day where we were all sort of in the same room trying to resolve a problem that was very time sensitive. And so one of the things that I think makes this group a little special—although I think it’s actually true of many, many intern groups—is how incredibly interdisciplinary you all are. Like I think of you, Alejandro, really as a computational social scientist, somebody with sort of deep computer science skills. Jennifer of course you have this training in political science. Our other intern, Eva Maxfield Brown, is in an informatics PhD program, and I am a PhD urban planner. So we have really different skill sets and perspectives that we’re bringing in. And so I was wondering maybe, Jennifer, for you, if you could talk a little bit about, sort of, that teamwork experience and how we started to build these collaborations across disciplines. 

SCURRELL: Sure. So I’m a political scientist, and traditionally our dissertations look sometimes very different. So I do, for example, I write a monograph. So that means not really collaborating with other people on papers. I’m, with my dissertation, I’m kind of sometimes very alone. And so, one of the goals this summer was really to explore teamwork. And I will say it was so much fun, especially as you said, Madeleine, in this really, really interdisciplinary team. And maybe an anecdote … because interdisciplinary work sometimes could be difficult, right, because we’re coming from different disciplines, we do not talk the same languages. And so in the first few weeks, I had so many meetings with our colleague, fellow intern, Eva, just to discuss projects and to understand, while talking about these projects and ideas and so on, what we’re actually really meaning when we are talking about these issues. So she’s really coming from this computer-sciencey background. I’m coming from the social sciences. And we had many, many sessions before we actually started to understand each other, what we’re actually talking about! 

DAEPP: Are there particular words that stand out to you as you were using them differently, like particular barriers to understanding? 

SCURRELL: Maybe it was more way of thinking … yeah. 

DAEPP: I think there’s definitely a strong, sort of, you know, I, I think some folks come from this very sort of prototyping-development orientation and some folks come from this very strong research orientation. And so that cross-pollination between all of you has been quite magical because I think we’re seeing the builders do some really beautifully designed research and the researchers really think about values in building. 

SCURRELL: I think it’s, it’s exactly that; that maybe it’s more the approach like the more data science-y, inductive approach versus the theoretical approach like deductive approach, and, yeah, I really have to say it was so interesting because everybody always listened to each other. I really have to say that. That was one of the things I really appreciated so much. You had so many brainstorming sessions and everybody was listening to each other. And at some point, we also started to understand each other and that was just, yeah, amazing! 

DAEPP: So I, I do want to just sort of take you up just a notch and ask you a little bit about the pace that you’ve been working at. Alejandro, you set your goal of building an entire large language model-based prototype, deploying it on Azure, getting it out as a user study with over 450 Microsoft employees, and then now you’re writing up, you know, analyzing and writing up the results. So that’s a lot in three months! And, Jennifer, you are, you know, doing interviews with really sort of elite political stakeholders about artificial intelligence and its impacts on political opinion formation. And so you are running from interview to interview. You know, again, you are doing an entire study about a major timely topic in the span of three months while also making sure that Alejandro has good survey skills and that Eva has a clear theoretical approach, and so I wanted to ask, I think maybe Alejandro first, how does this pace compare? How do you like this pace of work? 

CUEVAS: I think in general, the way that I approach work at least is through sprints, and I think that this summer was definitely a moment to sprint. I thought, you know, based on everything that I’ve said so far, it was a summer that I came super-excited whatever the work was going to be and whatever skill sets I needed to bring to the table. I think it was a summer that I wanted to sprint. And it started off, you know, already sprinting. I had the first week of work, I actually had a deadline that I submitted a paper for just, just before we started. And … or … 

DAEPP: So that was an external paper? That was not here … 

CUEVAS: Right. It was a paper that I … it was for my PhD. It just happened to be the deadline right in the middle of like onboarding, you know, the first week, and I remember, you know, coming already off that and sort of hit the ground running. I was already in that, in that state. And I wanted to do that as a challenge for myself. I wanted to take this opportunity to grow and, you know, learn all these skills that, that were in front of me. And I think the pace was very fast. I think I played a big role dictating that pace. But at the same time, the people around me were able to also match that pace. And I think in particular, a lot of the work that we did together in July felt, you know, truly collaborative and truly exciting because we were able to not only keep each other accountable, but it’s the same sort of feeling that you have when you’re running, you know, next to somebody and that you don’t want to give up because the other person is also, you know, running really hard and you’re keeping each other sort of like motivated. And I think that’s something that I got from the team as a whole. You know, there were people around me that were really excited to do and accomplish, you know, great projects during the summer. You know, looking back, it was an ambitious undertaking, and I think it was … I’m glad that it worked out and that it went well and that we had the results that we had. But I want to also lift up the fact that, you know, through the process, you played a big role in sort of like encouraging that development. You know, a lot of things that you would tell me in July were, you know, you’re working hard; you’ll be judged according to your hard work, not your results. And I think having that peace of mind was also just like, you know, it was a, a good thing to have in the back of my mind. Not only that, the reassurance of it’s not results driven, but the fact itself that you had visibility on the hard work that I was putting. And that made it more encouraging because I knew that you acknowledged and you recognized that, and that played a big role in me wanting to keep putting that effort in. 

DAEPP: I was a little worried about how hard you were working! I was like, please have boundaries, you know—go out, play in Seattle! [LAUGHS] All of you, especially, you know, the two of you and Eva Maxfield Brown, but also all of the interns that we get, are so talented and so ambitious and so driven that the summer can be just really exciting. It’s just so magical to be around that energy. Thank you for bringing that. Jennifer, what about you? What was your experience of pace? 

SCURRELL: Yeah, OK. I really have to say … so I never worked in industry before, so this pace was really something different than I was [LAUGHS] used to because in my PhD, as I said, I do a monograph. I’m, I’m responsible for my own project and my own time, so I can really take time to, to think about things almost as long as I want, right. I mean, OK, my supervisor has some say there, but … and I was aware that coming here, doing this internship, there will be a different pace, but I never expected it to be so fast. But I must say I really liked it, and I was never scared or stressed or anything because it’s exactly what also you said, Alejandro. I knew people have my back, and it was always a team effort, and everybody was always so passionate and motivating and it really … I felt so at ease even though there were so many stressful situations, but it was always so much fun because we went through it together. 

DAEPP: That’s so nice to hear. So, you know, you sort of compared it to your PhD, but Alejandro, you also have a startup. Can you tell us a little bit about that and also how that informs some of the insights you brought to us about pace and intensity and stress and emotional management from that world? 

CUEVAS: So Redoux is a fragrance company, and it’s something a little bit different from what I do as a PhD—at first glance, I would say. I’ve been able to in this past four and five years to find a lot of overlap within the, the work that I do at Redoux as well as in my PhD work. We talk a lot about interdisciplinarity in the research that we do within our academic world. But at times, I like to extend interdisciplinarity as well to other aspects just that are beyond academia, as well. Those could include your lived experiences. Those inform the type of work that you do, as well. Those definitely inform mine, and the work that I do at Redoux informs the way that I think about problems, as well. It informs the way that I think about or approach creativity. It helps me learn about storytelling, as well. It helps me learn about what another side of the world cares about or a whole different segment of people or set of problems. What I derive from, from these experiences is that there’s a hard problem, there’s a project, there’s scope, and there is an effort to do your best in whatever realm that looks like. And when I say, you know, there’s that part of how to think about things, but even here at Microsoft, there was a talk about using machine learning to discover new scents and new fragrances, and that is work that is directly related to the work that I care about in the Redoux world. So I think at the end of the day, I keep finding these overlaps between these two worlds, and I take the opportunity to merge them and inform the way that I approach problems as much as I can. And, yeah, it’s been, it’s been rewarding. It’s been tough to handle, but it’s also been, I think, a very formative part of how, I think, I communicate and how I think about broadly creativity and, and my work. 

DAEPP: Yeah. I do want to lift up … I love this sort of insight about how I think all, all of you as interns really bring sort of these multifaceted identities into, you know, partly into the work, partly into the team, but also into your social lives. So, Jennifer, for you, tell us a little bit about how you’ve been spending your weekends. 

SCURRELL: So, yeah, funny story here. Like, I mean, we are like 10 weeks in the internship now, almost at the end, and only I think three weeks ago or so I was so proudly saying, “Hey, Madeleine, I spent last weekend … I discovered Seattle!” And you were like, wait, it’s already six weeks into your internship. What did you do all the other weekends? I was like, I went hiking! So most of the times … it was really, really amazing to grab, for example, the other interns and go drive two hours, for example, to Mount Rainier and have a wonderful hike there. Or I also went, for example, to Mount Saint Helens. They are these huge lava caves. And, yeah, I went caving and, yeah, hit my head, but everything went fine. [LAUGHS] I’m still here. And no, it’s really amazing what you can do here, especially in nature. But also on campus of course what I really appreciated always were the ice cream socials, for example. And I will really dearly miss them!  

DAEPP: There were a lot of ice cream socials for the internship community this summer, and they were wonderful. 

CUEVAS: Don’t forget about Puzzle Day, too, Jennifer. 

SCURRELL: Oh! Oh yeah, Puzzle Day! I mean, we became fifth, right, out of 65, I think, groups? Yeah, that was really cool. 

DAEPP: Wait, tell me about Puzzle Day. I wasn’t there. 

CUEVAS: So Puzzle Day was this event organized by Microsoft where you put a team of very, very talented and very smart individuals together and threw really hard puzzles at them. And we managed to assemble a very good team from fellow MSR interns, and we bunkered down and went at really tough puzzles for about five hours. And as you mentioned, we came out fifth out of plenty of teams. I think we could have officially if … you know, we should have come out first or second.  

SCURRELL: Yeah … [LAUGHTER]  

CUEVAS: But that’s a story for another day! But that was one of the fun things, I think, at least Jennifer and I and a few other people got to do as a nice bonding experience. I think apart from that, something that I had the chance to do a lot during the summer was have friends that came and visited me and that we got to explore Seattle with people outside of town. And that was just fun in general, having somebody from, you know, another side of the country come here and discover together a new neighborhood or a new restaurant or a new store, a new museum. I think one of my favorite ones there was when Amelia, my partner, came out here, we discovered a hidden beach in the northwest side of Bellevue. It was a hidden beach access that is not marked. And I think Seattle has this little sort of mysteries or like little hidden spots around town that we got the chance to discover. And I think that was really one of my favorite things during the summer. 

SCURRELL: And can I add one more thing? Not to forget about July 4th, our fellow intern Eva invited us to spend Fourth of July with her and her friends. So we also met some locals, and we had a barbecue and we saw the fireworks and that was really, really also a very nice experience. 

DAEPP: I think there’s nothing that makes me happier than when I see all of you the next morning and you say, oh, we all had dinner with all of the interns last night. Because I know for me, with my graduate school sort of research experiences, those connections to other interns are the things that have lasted for, you know, that now I look for those people at conferences. I cite their papers. Those are such, such wonderful connections. And so it’s been really cool to see all of you starting to build that sort of relationship, and I’m hopeful that that will, you know, be sort of a real source of value going forward. So I am curious for both of you, based on your experience, would you recommend this internship to other folks considering Microsoft Research? 

SCURRELL: I mean, yes, of course! No, definitely. It’s just so amazing with like who you can work with, what resources you have, the whole organization, all the events you can, you can attend. And it’s really … besides learning technical skills, but also I really learned a lot about myself. And I really also take a lot back to Switzerland not only for my PhD but also for my general life. I really learned a lot here. 

DAEPP: Can you say what you mean by that? 

SCURRELL: It’s just this open-mindedness. I really must say … so in the coffee talks, but also in … we have had so many brainstorming sessions with so many different groups and this, this respect for each other that you’re listening, you’re truly interested in discussing things with your colleagues, and you’re getting so much valuable input. We’re all in the boat together.  

DAEPP: I’ve certainly also experienced this, a strong culture of respect for one another, like especially across disciplines.  

SCURRELL: Yes.  

DAEPP: But then it’s also … we had this session, this weekly session, called “Show Us Your New Clunky Prototypes” by the human-computer interaction team, where people would show very early-stage demos, and it wasn’t considered “demo-able” unless it broke, right. And, Alejandro, you had a, had a quite clunky prototype in exactly that sense. But that to me is exactly sort of this example of how, how much people gave actionable, useful feedback to very clunky, very early-stage ideas, really sort of thinking about what, what is this trying to be? What can this be? And emphasizing what was good about it. Is, is that your experience? 

SCURRELL: Yeah. I also … I must second that it’s really this not having to worry to fail or putting something very rough out there. People still, you know, take you serious [LAUGHS] and want you to succeed. And first, I was really nervous at the beginning of my internship because, you know, all the very famous and like super-interesting and super-successful researchers you’re around, but I soon really relaxed because I understood that people really are interested in working together successfully and supporting each other. And that was really, really nice to experience. 

DAEPP: Alejandro, what about you? You had looked forward to being at Microsoft Research really for years before you came here. Did it live up to your expectations? 

CUEVAS: Yeah, I think I would recommend it to anybody who wants to work with a cohort of really, really talented individuals, anybody who wants to create some long-lasting friendships and professional connections, or anybody who wants to be part of a pretty solid network of alumni interns, which I think is pretty remarkable. Or anybody who wants to come and work in a fast-paced environment with a ton of really talented individuals across numerous, numerous disciplines. And if anything from that list, I think, appeals to anybody, I think Microsoft Research is a very good place to come and spend your summer at. 

DAEPP: So last question for both of you. It’s a big question. What impact do you want your work to have going forward? I mean, you are both really exciting young scholars. I’m so grateful to be a part of your journey. Can you tell me just, just think a little bit forward. What impact do you want to have with your work, and what are you hoping this next stage of your career looks like? 

CUEVAS: Something that I’ve been thinking a lot and wanting to venture as I head into some of my last years of PhD is the dissemination of my work, but not necessarily in the traditional academic sense where we find other academics who find our work interesting, but in the sense that I think I want to work on problems that interest and appeal to the broader public and something that is communicated to and accessible to people who are outside of academia. I want to position the work that I’m doing particularly, you know, around cryptocurrencies and around what I like to say like the back alleys of the internet … I want to have that work accessible and to create an appeal to a broader public. I think something that, when I was younger, I wish I knew was … or I wish I had more exposure to what research was and what a PhD was, particularly coming from Paraguay, right, a place that, I shared with you, when I was in high school, had one patent a year. A place where I went to my first science fair when I was a senior in high school. And I want my work to be not only accessible from a place of interest but accessible in a way that it can tell a story that people find so compelling that they feel inspired to work in something similar or inspired to, to go out and investigate phenomena that they think is fascinating. And I want to be able to transmit this excitement and transmit sort of this joy, I guess, of working on something that interests you with the broader public and hopefully appeal and again inspire somebody who may never have thought that this is a career that they could consider or a path that is potentially open to them. 

DAEPP: Yeah, I really hope that you are the first of many brilliant Paraguayan computer scientists who come here for the internship! 

CUEVAS: I hope so, too. 

DAEPP: Jennifer, what about you? 

SCURRELL: So for me actually, it would be important that my work—because I’m a political scientist, social scientist, I’m working specifically on sociotechnical problems—that my research also really, you know, gives something back to society and that I also really have, or that my work or our work, really has impact on a higher level to solve the big problems we have in this world, specifically, for example, with my work regarding how AI impacts political opinion formation; regarding, for example, next elections that I can give some input how we could do better with AI, with technology, to solve these societal problems. And especially what I also want to say, as a first-generation academic, I hope to inspire people that are like me to, yeah, reach high! 

DAEPP: Well, thank you so much to both of you. I could not be more grateful for such a wonderful team and collaboration. And I really do think you’re going to make major impacts on important sociotechnical problems, on showing new methods with large language models, and I’m excited to advocate for your career going forward.  

[OUTRO MUSIC] 

Thank you for a wonderful summer.  

CUEVAS: Thank you so much. 

SCURRELL: Thank you.