Posts in category: Microsoft AI

Transcript 

RANVEER CHANDRA: If you’re a professional, one of the things I would say is try to go after your passion. If you give your work a bigger meaning than just making money, you’ll go beyond the 9-to-5 or 9-to-6 schedule. You’ll give it a lot more than just thinking about it as work. 

JOHANNES GEHRKE: Microsoft Research works at the cutting edge. But how much do we know about the people behind the science and technology that we create? This is What’s Your Story, and I’m Johannes Gehrke, Lab Director of Microsoft Research Redmond. I’m excited by the people I work with, and I’m curious about how they became the talented and passionate people they are today. So I sat down with some of them. Now, I’m sharing their stories with you. In this podcast series, you’ll hear from them about how they grew up, the critical choices that shaped their lives, and their advice to others looking to carve a similar path. 

In this episode, I’m talking with Ranveer Chandra. Ranveer is the Managing Director of Research for Industry and head of Networking Research in Redmond, and he’s been with the company for almost 20 years. His work in systems and networking is helping to bring more internet connectivity to more people and is yielding tools designed to help farmers increase food production more affordably and sustainably.  

Here’s my conversation with Ranveer, beginning with his childhood in India and his experience applying to and studying at one of the prestigious Indian Institutes of Technology.

RANVEER CHANDRA: So I grew up in India. I grew up in a city called Jamshedpur in India. It’s a steel city. It’s the only city in India without a mayor, so the Tatas run …   

JOHANNES GEHRKE: What does steel city mean? 

CHANDRA: It’s the first steel plant in India … 

GEHRKE: OK, uh-huh … 

CHANDRA: … and a lot of the steel comes from there. The Tatas, which are the big industrialists in India, they run the city. So it’s … I grew up with 24 water, 24 electricity, trees on both sides. It looks like a mini Seattle or a mini Palo Alto in India. It’s a beautiful city. And I did my schooling there in, uh, in one of the schools in that city called Jamshedpur. I did my undergrad in IITs, one of the IITs in India, and then I came to the US, uh, to do my PhD at Cornell. So my childhood, we are three brothers and a sister. All three brothers went to … all four of us studied engineering. Uh, the three brothers all went to IITs, different IITs, and we studied hard, played hard. We did spend a lot of time in villages, though. Every summer and winter vacation, we would go to my grandparents’ place, which was in another state in India called Bihar, which is one of the poorest states. But my grandparents, they were farmers, and they had a lot of farmlands in those villages, so I did spend summer and winter vacations in those villages. 

GEHRKE: And how did you end up to study engineering? How do you decide on that?  

CHANDRA: Yeah. So in India, as it happens, these IITs are very competitive exams. So in … around … during our time, like close to half a million people gave the test, students gave the test, and the top 2,000 got into IIT. Those are tests of physics, chemistry, and math. Those are the only three subjects, and among those, the top few would try … typically pick computer science. So it was more I enjoyed math. That was my … that was what I really enjoyed. And then, uh, because I got selected into the IITs, that was of course a kind of a dream for many people, to go study there. The education level there is really high, really good. And that is how I ended up in IIT. It was kind of unplanned. It wasn’t, you know, when I got my … when I got through IIT … I wanted to go to another IIT because the one I went to, Kharagpur, it was close to home, but I wanted to go to Bombay because it’s a big city. Mumbai. It’s a big city. Bollywood runs out of Bombay. I thought I could get into Bollywood. Not, not really. [LAUGHS] But I did go to Kharagpur, which is closest to … this is the one where it has … it’s the oldest IIT, and it was very close to home. So I ended up going there and studying computer science. 

GEHRKE: And why computer science?

CHANDRA: Yeah, so, uh, computer science because it was … it had a lot of math, so once I got … the way I got exposed to computers was I was … in high school, I studied the theory before I got to touch a computer. There was one computer in school.  

GEHRKE: One computer in the whole …  

CHANDRA: One computer, and everyone had to go there and see what a computer is. But we did get … we had books to teach you everything about what binary is, how computers were invented. That was around the time I enjoyed reading about computers …  

GEHRKE: So you did like algorithms on like sheets of paper?  

CHANDRA: So, yeah, so you draw the flow charts … 

GEHRKE: Right.  

CHANDRA: I enjoyed some of the flow charts. I remember some of the flow charts like how do you have the greatest common factor and things like that. I enjoyed doing those algorithms, and there was that similarity with math. You need to have a good math background to, to enjoy those things in computers. So I did a lot of programming on pen and paper, and someone would correct it. And then we got to start learning … BASIC was the first language that I learned. I really liked coding.  

GEHRKE: What kind of computer was it, actually?  

CHANDRA: So this was, um, you know, these, these dumb computers with one mainframe behind, so this was one of the Sun computers back then.  

GEHRKE: Oh, wow. OK, uh-huh. 

CHANDRA: And we had just these dumb terminals through which you would get access to these … [LAUGHS] 

GEHRKE: And that was BASIC, not, not Pascal or anything? That’s interesting …  

CHANDRA: It started with BASIC. Yeah, BASIC, and then FORTRAN was the next one, then C. So those were the languages that I learned. And computers because I just enjoyed … I would have picked either math or computers. Those were my two things, and computers was just fun. It was more … and that was just the time, you know, when you would reserve some time to play a computer game, Pac-Man and things like that, so those things were fun back then. This was, uh, late ’80s, early ’90s.  

GEHRKE: And then what I’ve heard is that to get into the IITs is super competitive, so did you then study a lot or you played a lot, or what was the … ?  

CHANDRA: That’s a funny story. So you know, when I went into IIT, I was … the interesting thing is once you go there, everyone who comes there is from all over India; these are the people who are top of their class. So everyone else is as good as you. So you, you then end up studying very hard because that’s the culture. Everyone is coming in there. And, uh, in the first semester at IIT, I was No. 1 in the IIT, all across IIT, and that was like, “Whoa, that was, that was easy.” I didn’t really put in a lot of effort. My elder brother was there, too. He was in the last year, and he was, he was more of the fun kind. I was more of the, you know, the studious kind. He came and told me don’t do Thing A, Thing B; don’t get into alcohol or, or party and all of that stuff. I ended up doing all of that. Don’t run for elections. I ran for elections and all that stuff. 

GEHRKE: Oh really. Where did you run for elections? The student parliament?

CHANDRA: Within the institute, so I was the secretary of sports—volleyball and all of that stuff. So I did … a lot of fun, as well. So in the end I was like No. 3 graduating. But I did have a lot of fun, too. I did a lot of social, cultural things. I was on the volleyball team and things like that at IIT. 

GEHRKE: And coming once more back, I mean so to get into the IIT … I mean, for, for me as a German, this is so, you know, unusual because we don’t have these centralized entrance exams, except for medicine. 

CHANDRA: Yeah.  

GEHRKE: But, um, I heard the test is really, really hard. And you actually in your last year of high school, you don’t really study for high school anymore. You just study for that test. How, how, how is that actually?  

CHANDRA: Yeah. And now it has become even more competitive. During our time, it was … there were fewer seats. There were like 2,000 people from all across … there were five IITs, six IITs, back then. And yeah, studying towards the end … so you start studying just physics, chemistry, math. Back during our time, we didn’t have as much tuitions and stuff. I didn’t have many … anyone … like the last six months, I’ve had something. But now, people go to these, these other towns which are meant for coaching people for IITs, and they have these different sections … 

GEHRKE: They live away from their parents? 

CHANDRA: They go away from their parents; they live in a hostel. And all they’re preparing for is the IITs. We don’t have that … I didn’t have that during our time. But now it has become so much even more competitive. More students take it, and it’s like a centralized exam for, for studying. But it does … you know, in the end, the experience was worth it. If you ask me, “Hey, was all this studying worth it?” I think getting into IITs, of course, the professors are good, but the students are exceptional, the kind of people you’re interacting with, that ambience. And now when I look at my classmates, everyone’s doing well. And you find people doing different things. Not, not everyone is in, is in tech. They go do different things, and they excel in that field because of the kind of people that they select into these IITs. So I think in the end, it was stressful, but it was worth it.  

GEHRKE: It was a great opportunity. Yeah, I mean, and, and then you made sort of the decision, not only after the IITs, to, to stay in India and to take probably a very good job … 

CHANDRA: Yeah.  

GEHRKE: … but to come to the US and, you know, learn even more. So what, what drove you to that decision?  

CHANDRA: Yeah, that was kind of like the way I studied computer science. It was not … at least I had a passion for computer science. I didn’t want to do a PhD, by the way, when I was coming here. So you would ask why. So when, when I was graduating, I got the highest-paying job that year among all the undergrads. And that was a big deal. That was back then, Synopsys, one of the EDA companies, the CAD companies, right, the VLSI companies. So I would have taken that, but as it happens, usually the people who are at the top of the class, they would apply outside and they would come here to study. And that was the reason I had applied. But then the, the thing I really wanted to do in my career was to be in business. I wasn’t really looking to be an academic back then. I was like, you know, I’ll go study an MBA. 

GEHRKE: And you studied to, to get a PhD instead?

CHANDRA: Yeah. No, so I was like, you know, I’ll apply to PhD programs, and they give a master’s anyways, and after that I’ll go do an MBA. I wanted to be the business guy. So that was the reason I applied, and … but the reason … the, the person who had convinced me to come here was a professor at Cornell. I had other top schools, but there was a professor at Cornell who was a networking guru at that time. I won’t name him. He’s still a very good friend of mine. So he convinced me to come there. I was a fan of his work, um, and I decided to come to Cornell for him. I said no to other schools. And then I land here; this was 1999. I send a message as soon as I get to Ithaca saying, “Hey, I’m here. I would love to meet you,” and he says, “Well, you know, I’m really sorry, but I left Cornell to do a startup.” And then I was a bit … I was very upset. For a few months, I didn’t know … what am I doing here. I gave everything up. I had other colleges where I could have gone. But to be here, I came to study computer science and the person I came here for is no longer here. It was disappointing. But then I was lucky that Professor Ken Birman adopted me. He was like, “Hey, you have a fellowship. You do what you want. I’m not going to interfere. You just do what you want.” And that’s what convinced me to do a PhD … that in the sense, the first few months were disappointing, but then once I got the freedom, I really was like I was getting paid some money for just learning. And that bit really got me very excited. The fact that I had all the independence to pick what I want, to work on the things that I want. And that’s what convinced me that I don’t want to do an MBA. I want to … I can do what I would do with an MBA after doing a PhD. So that’s what got me to do a PhD at Cornell.  

GEHRKE: It’s, it’s super interesting because, I mean, if you hear that story, for many people, it would be kind of frightening, right. You come there … well, you have this person who you wanted to work with and maybe he … there was sort of a plan set up or so, and now, I mean you have to switch advisors. OK, that’s one thing. But the second thing is a PhD sounds so frightening to many people because it’s like a step into the unknown, right. So your PhD by definition, you don’t know whether you’re going to get there, right, because it’s research, and research sometimes leads you into the wrong path and sometimes you don’t get the result that you want. So how, how do you deal personally with that uncertainty?  

CHANDRA: For me, it is more I like the unstructured part of it. I like the fact that I could take it in many directions and grow it, and I want that, that level of flexibility, and the more I realized … I think problem picking becomes important, and Ken helped me a bit with it. So initially, I told him I want to do wireless. This was back in 1999. It’s six months into a course, and I’m like I want to write a paper. This is what we want to do, on reliable multicast but for wireless systems. At that time, wireless was very new; people didn’t have cellphones, uh, and such. So … and he said go for it; it was worth it. And then I started exploring it with another grad student. We wrote a paper on it. And that was a good learning experience, which I really enjoyed. The fact that I’m venturing into the unknown, and Ken was explicit. He was like I’m not the expert in wireless; you have to learn it yourself. We did it ourselves. We wrote the paper. It got accepted. And all that was … really helped me … it gave me the confidence that it is possible to explore and do new things. And that’s what got me excited. And that’s what got me into the space of networking, as well. It’s all about wireless, and, um, and getting people more connected at low cost. How do you get everyone connected to the internet? And that’s the space. I think there is a passion within me around that, as well. And the fact that during my PhD, I got the opportunity to go explore, just try everything, and we just made … kept making the right bets, as well, with respect to papers and what got accepted. I did, I did an internship at Microsoft Research, as well, during my PhD. This was three years into my PhD. I came here a few times and that helped me, as well. That helped me further. I worked with Victor Bahl, who was my intern manager, but he was my mentor, and that helped me further go towards my career goals.  

GEHRKE: And Victor is now a technical fellow in Azure, where he’s the CTO of our Azure for Operators efforts.

CHANDRA: That’s right. Yeah. 

GEHRKE: And maybe, maybe one thought about networking, right. So networking seems to me like this field which is pretty hard because without the hardware, networking doesn’t work. But without the right kind of network protocols and the software, it doesn’t work. So you don’t only … you can’t only do one thing, right. You cannot do only just … 

CHANDRA: The one layer … 

GEHRKE: … the software … and you also have to do the hardware, and they have to sort of co-evolve. How, how does this work in networking research? Explain that a little bit to our audience. How does networking research actually make progress if both of these have to sort of work in lockstep?  

CHANDRA: That’s a great point, Johannes. And that is one of the things with networking. Right when I was an undergrad, I started getting excited by this layered diagram of networking—the seven-layer diagram, the seven-layer OSI stack that we … 

GEHRKE: Oh, yeah, I never understood that completely. [LAUGHTER] 

CHANDRA: Yeah, it’s all the way from the physical layer, so if you think of the physical layer as one hop …  

GEHRKE: Yeah.  

CHANDRA: MAC layer … so networking is all about how do you send bits across two computers anywhere in the world. And at the lowest layer, it’s about how do you send the bits across. The layer above it makes it reliable over one hop. That’s the medium access layer. The layer above that ensures that you can communicate not just over one hop but anywhere on the internet using IP. The level above that, with TCP, you make sure that end-to-end communication is more reliable. So every step, every layer that you go above … 

GEHRKE: Got it …  

CHANDRA: … helps to make sure that your network is better. Now of course once you start layering things, it makes it harder to interoperate. It makes things inefficient because you’re adding headers per layer, which makes it … when you’re consuming bandwidth, you’re introducing extra latency. But that’s an opportunity. At the very least, what this layer diagram has done is that it has ensured innovation across different layers as long as they’re good enough APIs for each layer to communicate with the next layer, so that is the key part of networking research, where over the years it has kept evolving. Every layer has changed. The hardware, we’ve seen Ethernet go from bits per second to kilobits to megabits, gigabits. Now it’s hundreds of gigabits. We’ll soon hit terabits, as well, which people are talking about with 6G—to every layer. When we think of the MAC layer, the TCP layer, all of those have been evolving and that has led to applications. A lot of times, a lot of people just worry about the applications: is my media application … can I watch things on Netflix? Well, underlying that is all the bandwidth that the network provides.  

GEHRKE: Got it. So, so one way to think about this is that as long as I make my hardware have the same APIs, I can even go … I can sort of significantly evolve my hardware and all the other parts of the network stack will work? 

CHANDRA: Exactly, exactly. 

GEHRKE: I see. 

CHANDRA: So you could be innovating on the radio—you make that radio faster—but as long as you keep the APIs the same, the TCP layer would work as is with the layers on the bottom.

GEHRKE: Got it. So, so I hear this magic word “6G” from you a lot these days. Can you just explain a little bit. What is 6G all about, and why is it interesting? 

CHANDRA: Yeah, so every … over the network, we’ve seen these standards evolve over time. Every 10 years, we see from 2G to 3G to 4G. Now 5G. 

GEHRKE: Why 10 years? Why 10 years?  

CHANDRA: Ten years is usually the time it takes to come up with a new innovation, drive the standard, drive alignment across different stakeholders to see this is what the next standard should be. Because then once we finalize the standard is when you’ll have all the other vendors like people who build the hardware to base stations, to cellphones, to modems, everyone can then align and build something that is … you know, you have your Qualcomm modem talking with say an Ericsson hardware with the AT&T carrier, which is running on Azure cloud.  

GEHRKE: Because everything has to interoperate. That’s why we have the standards.  

CHANDRA: Yeah, so that’s why we have the standards, which evolve in a 10-year time frame. With 6G, we are looking at 10x more bandwidth. Your throughputs will go much higher. And one-tenth the latency. Can you get to sub millisecond latency? And the kind of scenarios that we are thinking of are … think of, uh, we can think of completing the feedback loop like robotics and so on, where you’re getting the information, you need to send all this to the cloud because this is huge amounts of information, you need to act on it using AI, and you need to send the feedback so that your robot can perform in time. This could be something in a racetrack, something in, uh, in the middle of … on the roads, or it could be in the middle of a farm. So this is what the vision is. And along with that, the other vision that we have with 6G is to bring internet all over the world. That is right now still around 40 percent of the population in the world—that’s close to 3 billion people in the world—doesn’t have internet access. They just cannot … they, they just don’t have access to the internet.  

GEHRKE: And why does 6G help with that? 

CHANDRA: 6G should make connectivity more affordable.  

GEHRKE: So 6G is also cheaper, even though it’s faster and lower latency?  

CHANDRA: It will be.  

GEHRKE: That seems so contradictory. Why is that the case?

CHANDRA: No, so I think it will be high speed and low latency in areas where it is needed, but the other feature it should bring in is affordable connectivity in regions that are not connected. And these are in … a lot of it is in the emerging markets, where the people are not connected. And it is not just people. Now we’re also talking about people and things because, you know, if you think of the entire world’s surface, close to 80 percent of the world’s surface, which includes ocean and land, doesn’t have terrestrial internet. So how do you bring internet connectivity throughout the world? That’s one of the challenges that people are looking at with 6G, along with some of the other things around sustainability, security, trust. These are all issues, as well, but at an underlying layer, the fundamental thing we want is high speed, lower latency, and connectivity—affordable connectivity—everywhere. We can’t be leaving 3 billion people in the world behind with no internet when it is so central to the way we are. It defines everything we do, and yet there are so many people in the world who don’t have internet access.  

GEHRKE: You mentioned one word, one word “farm,” and we’ll get to that in a second. I just wanted to ask one more question because it just sounds a little bit like magic to me that, you know, you get lower latency, higher bandwidth, and lower cost.  

CHANDRA: Yeah. 

GEHRKE: Why don’t I get this with 5G if I just push the hardware along? 

CHANDRA: So this is where the research would come in, and I think it won’t be the … so when you think of a standard, we think of different components of the standard. One part of the standard is the spectrum. Which part of the spectrum do you operate on? 

GEHRKE: Right.  

CHANDRA: That could define the throughputs that you get. Now the high speed usually comes with a limited range, as well. Like, you know, like one of the technologies that people are talking about—we are investigating here at Microsoft Research, as well—is terahertz networks. This is a part of the spectrum where you get huge amounts of bandwidth. It’s still following Shannon’s law, but it is just in that part of the spectrum that until now, people said couldn’t be used for communication. But what we are showing is that well, you could, you could use it for communication in that part of the spectrum. Once you get that bandwidth, it also helps us reduce latency by a significant amount. So that’s one thing people are looking at. Along with that and other technology people are looking at to overcome this problem of short range, like 100 meters, to go beyond that, is smart surfaces. So one of the things we’re building is rather than just have these base stations, what do we … if we have smart surfaces, which are programmable and can then make sure that wherever people are, wherever things are, you can provide connectivity there by, by channeling the signals in that particular region. Along with that, people are also looking at affordability. People are looking at different forms, other forms of communication. Like previously, we’ve looked at other parts of the spectrum like lower … terahertz is going further closer to light, that part of the spectrum. The other part of the spectrum is lower in the TV spectrum, for example, a radio spectrum. Once you go lower in the spectrum, your connectivity can just, just go really long distances. So one of the innovations that we had done a lot at Microsoft Research was on using TV spectrum to send and receive information. The benefit is this spectrum is not being used in many places, and using that, you can provide very low-cost point-to-multipoint connectivity in different regions.  

GEHRKE: Makes sense. And so 6G encompasses all of those?

CHANDRA: 6G would encompass … right now, it’s still being defined. It’s still early. But as far as research goes, we are working with the community on all these aspects. The other aspect about 6G is AI-driven networks. So can you make your networks much more intelligent? Right now, you define these networks in standards, and the standard’s written, and that’s what is implemented. But you could adapt parts of it based on what’s happening around you, and you can use the spectrum better. You can use it to make sure that you’re getting much more efficiencies in your, in your system. You can prioritize things better. So that’s again one of the other themes that, uh, that we’re investing in and a few of the other, other research labs are investing in, as well. 

GEHRKE: Super interesting. And I mean you mentioned this word “farm” before. You’re, of course, known for FarmVibes. And maybe just explain very briefly what FarmVibes is and then also explain … you know, you started out here doing this in Microsoft Research, but then you actually went to a product group, right. What made you, you know, what made you take that decision? And, you know, you actually now finally here in Microsoft Research again. So maybe tell us a little bit about that, that journey.  

CHANDRA: Yeah. So I’ll start with why did I even pick agriculture, right. So as I said I did spend a lot of time in my grandparents’ farms in Bihar. This was in north India. 

GEHRKE: What were they farming? 

CHANDRA: So they used to farm wheat, sugar cane, rice, and, uh, they had farms there. And back then, I did not like anything to do with agriculture. So I used to go there with my brothers and sister and, you know, I, I did do … like I played kabaddi there. I learned how to ride a bicycle with the people there.  

GEHRKE: What, what is kabaddi? 

CHANDRA: Kabaddi is like, uh, it’s a funner form of rugby. Not, not … it’s, uh, it’s … there are two teams, and you essentially have to bring the other team down, so it’s … you play it in the sand; you get really dirty playing it. Growing up in those villages, it was fun. But spending time in those villages, I didn’t really look forward to them. The reason was that, you know, the rest of the year, you were in this city, which is maintained by the Tatas, which has water, electricity, clean roads, everything, and then, the rest of the three, four months, I was in this village, which did not have electricity. They didn’t have toilets. If you have to go to the bathroom, you have to go out in the fields in the middle of the night in the winter. It wasn’t what you’d look out … look forward to. But that’s how … that’s one of the things that I grew up with. But one of the things that really stuck with me was the poverty that exists in these villages. Like one of the times, my mom, she did some prayers, she had an offering, and she left it outside. And there were a group of kids, they hadn’t had anything to eat during the day. They were just there to grab something to eat. And that has been something that, um, has really … it’s been in my, my mind during my undergrad, and even over here, one of the, one of the things I always want with any project I’m working on is this bigger mission, things that can impact the people I grew up with, and be it with TV white spaces for providing internet connectivity to what I saw was very primitive forms of agriculture. These, these farmers, they would do hand-based seeding. Over here, you use tractors. They would go with the hand and put the seeds. They would do … use bullock-driven tractors. Like to till, they would just go with a bullock. They’d put this hitch on it and then go till the fields, and this, it’s very primitive. So what we, what we want to do is to enable data-driven agriculture, and the bigger goal here is to help address the world’s food problem. The world needs 50 percent more food compared to today’s levels. And in order to grow that 50 percent more food, we need to get there … not just food. We need to grow good food, nutritious food, and we need to get there without harming the planet. The soils are not getting any richer. The water levels are receding. So that’s the big picture of what we want to do with FarmBeats. Our goal is … one of the most promising approaches to get there is what we call data-driven agriculture. That is, can you, can farmers, use data and AI to remove guesswork as part of their farming operations? You know, the farmers that we work with, um, like even when I was doing networking here, I would actually go and volunteer in farms here, and I’d cold call various farmers. What I realized is that these farmers … 

GEHRKE: Like here in, in Washington state, right?

CHANDRA: In Washington state. In fact, there’s a Starbucks right here. There was a barista who knew me. She said that, “Hey, I’m going to Spokane, Eastern Washington, this weekend.” If she’s in Eastern Washington, maybe farms. Who farms there? She says, “My grandfather.” I was like, can you connect me to him? So I would just cold call them. I talk to a lot of farmers, and what I realize is that these farmers, they know a lot about their farm. They’ve been farming there for a long time, yet a lot of decisions they make is based on guesswork. 

GEHRKE: Right.  

CHANDRA: That is where all the data-driven farming piece came in. So through FarmVibes and previously FarmBeats … with FarmBeats, we built, we built a data, data platform for agriculture. Then I had moved over to the product side; we shipped it as a product. We announced partnerships with Land O’ Lakes. We announced partnerships with … well, Land O’ Lakes, their agriculture platform is now running on that. We announced partnerships with Bayer Corporation, with USDA, and other organizations, as well. Then while I was there, what I realized is that the engineering team is now on track. They are delivering this product. But that’s not enough to help us address the world’s food problem. We need to add intelligence on top of what we are building. We need to bring all the innovations that we’re doing in AI for this field. And that was one of the reasons and of course working with you and the fact that the networking … networking is one of the key components that can help us—networking and AI—so that was one of the other reasons why I came back. And with FarmVibes, that’s the problem that we are addressing. With FarmVibes, it’s the, it’s the farm intelligence-speak; it’s the intelligence that sits … that can light up scenarios, these scenarios that we talk of when we think of data-driven agriculture, sustainable agriculture. The kind of things we want to do is help a farmer take the right decisions for what will make them more productive, what will make them reduce their emissions, what will help them sequester more carbon. These are the kind of questions we want to help a farmer, a farmer answer. And some of that is very fundamental research. We’ve come up with ways to see through the clouds, to do very hyperlocal microclimate prediction, to combine different models to make much more accurate predictions to help farmers. And that’s, that’s the kind of thing we are enabling as part of FarmVibes.  

GEHRKE: Well, and so just curious, I mean here in Washington state, what is, what is grown on those farms, and how, how have you helped so far?  

CHANDRA: Yeah, so there are farms here … there’s one farm in Eastern Washington. We work with this farmer Andrew Nelson, who is a fifth-generation wheat farmer. This is an hour east of Spokane, so if you go to Spokane, you have to drive another hour. It’s interesting. When you go to Andrew’s farm, like we are about 15, 20 minutes from his farm and you lose internet … cell connectivity. 

GEHRKE: It’s completely gone.  

CHANDRA: [LAUGHS] So you’re like off the grid. And then you reach his house, and then he set up this TV white spaces thing. He has some connectivity in his farm. 

GEHRKE: Through satellite or TV white spaces? 

CHANDRA: TV white spaces and a fiber to his home. So there’s a fiber that he’s paid to bring fiber to his home and then that lights up the area around his farm using some of the technologies that we have been inventing here. And with Andrew, this is just one use case, but you could replicate it across other farms, as well. He uses some of the techniques throughout his farming life cycle, all the way from planning what to farm to planting—what to plant, where to plant—to in production, like, for example, doing chemical application. Where do I apply herbicides? Do I need to spray pesticides? Where do I spray it? Rather than spraying it throughout. To harvest. That is, when should I harvest? How should I … what route should I take? To post-harvest. Monitoring things and deciding when and where to sell certain things to get more profit. So he uses it throughout his, um, his farming life cycle, and he’s seen a lot of benefit. Like Andrew’s talked about how in one part of the farm, he could double his yield.  

GEHRKE: Double? I was just going to ask actually how much benefit he got from it. 

CHANDRA: Yeah, double the yield. And in another part of the farm, he’s talked about 40 percent reduction in chemical costs. You know, for a farmer, one of the input costs is chemicals. And using these precision techniques that we built, Andrew’s been able to save 40 percent. That’s, that’s huge.

GEHRKE: It’s also good for the environment.  

CHANDRA: It’s good for the environment, as well. He’s not putting in more chemicals than are needed. So these are real use cases with farmers in our, in Washington state. We’re also working, for example, we announced a partnership in India, in Maharashtra. This is one of the Centres of Excellence that’s being put up for FarmVibes. So this is again, they are building AI capable … this is across Oxford University; there’s an organization in India called Agriculture Development Trusts; and Microsoft. And working with, of course, Microsoft India, our sales team there. They’ve set up this Centre of Excellence in a village called Baramati in India, where they are going to be taking the same techniques we built and adapting it for smallholder farmers in, in, in that region. So really excited about the value it brings. Yeah. 

GEHRKE: Ranveer, it seems like, you know, here at Microsoft, you had the amazing opportunity to really have huge impact. You know, you start on research, then deliver a product, now even extending the product to more use cases. Do you have any career advice for our listeners given where you are and where you’re going?  

CHANDRA: Yeah. And as a student, if there are students listening to this, I would say consider going after a PhD. It gives you that exposure, uh, the opportunity to learn, to dig deep, to know a lot about, about a field. If you’re a professional, one of the things I would say is try to go after your passion. If you give your work a bigger meaning than just making money, you’ll go beyond the 9-to-5 or 9-to-6 schedule to make that happen. Like, you know, Johannes, one funny incident is over here, working at Microsoft, most people sit in front of the computer. When I had started working on FarmBeats, every day, I would be driving to this farm. There was another farm about 40 minutes’ drive from here. Every day, summer or winter, I would be driving there to do the experiments. And I would go there, and a few days, especially when it rains, it gets really gloomy [LAUGHS] and you have to go in boots to a farm that is muddy, half flooded. I’d be like, “Why am I doing this? I could be sitting there …” And then the, the way I would argue to myself is, you know, even if 1 percent of what I’m doing works, it will help the lives of so many farmers worldwide. And then that just gave me the extra energy to go even more, to, to just give it everything that I have to make that difference. So that’s something which I tell students, as well. Give whatever work you do—you’re working in AI, you’re working in systems, you’re working in, in building the next plane or building the next ship—give your work a bigger meaning. You will, you’ll enjoy it. It just, um, you’ll give it a lot more than, than just thinking about it as work. And, you’re right, that at Microsoft, we get that opportunity to make that wholesome impact, that is as you did, as well. We get to go to the products. If something ships as part of a Microsoft product, it touches the lives of so many people. Like one of the projects I was with was Xbox, for the Xbox, when I designed that Xbox wireless controller protocol. Now over 100 million people use it, and one of the most common congratulatory messages I get is still around the Xbox. When I’m giving a talk, someone will come and say, “My son said thanks to you because you helped make the Xbox successful.” So that’s one of the opportunities we get. But not just that. We get the opportunity to come do research and think bigger about the problem; take it to a different level and then influence the next generation of product. So this is, uh, thank you. I think this is an awesome place to work, to realize that mission, that, that vision of what we want to achieve in our lives.  

GEHRKE: Yeah, I think, I mean it speaks so much to me because that’s something that I was also really excited about. Making the transition from university here to Microsoft, as well.  

Thanks again, Ranveer, for the great conversation.  

CHANDRA: Yeah, thank you, Johannes.

Transcript 

ALEJANDRO CUEVAS: 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.  

JENNIFER SCURRELL: So I’m a political scientist and traditionally our dissertations look sometimes very different. So that means not really collaborating with other people on papers. With my dissertation, I’m kind of sometimes very alone. So one of the goals this summer was really to explore teamwork, and I will say, it was so much fun, especially in this really, really interdisciplinary team.

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.  

Thank you for a wonderful summer.  

CUEVAS: Thank you so much. 

SCURRELL: Thank you.