Microsoft Research Podcast

Microsoft Research Podcast

An ongoing series of conversations bringing you right up to the cutting edge of Microsoft Research.

Innovating in India with Dr. Sriram Rajamani

January 22, 2020 | By Microsoft blog editor

Sriram Rajamani on the Microsoft Research Podcast

Episode 103 | January 22, 2020

Dr. Sriram Rajamani is a Distinguished Scientist and the Managing Director of the Microsoft Research lab in Bangalore. He’s dedicated his career to advancing globally applicable science in the testbed that is India. He is, by any measure, a world-class researcher and leader. He’s also, as you’ll find out shortly, a world-class storyteller!

Today, Dr. Rajamani talks about the unique challenges and opportunities of leading MSR’s research efforts in India and what it takes to build a robust research ecosystem in a country of huge disparities. He also dispels some preconceptions about poor and marginalized populations and explains why ‘frugal innovation’ may be one key to solving societal scale problems.

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Transcript

Sriram Rajamani: I think the number one thing that strikes you when you try to build technology in India is the resource constraints. You know, if you want to build technology that actually fits the lowest common denominator, that actually works everywhere, the resource constraints that you have to think about: cost, bandwidth, the diversity of users; I think those are extreme in India. Because of that, if you build systems that somehow work in those constraints, you are innovating for the world.

Host: You’re listening to the Microsoft Research Podcast, a show that brings you closer to the cutting-edge of technology research and the scientists behind it. I’m your host, Gretchen Huizinga.

Host: Dr. Sriram Rajamani is a Distinguished Scientist and the Managing Director of the Microsoft Research lab in Bangalore. He’s dedicated his career to advancing globally applicable science in the test bed that is India. He is, by any measure, a world-class researcher and leader. He’s also, as you’ll find out shortly, a world-class storyteller! 

Today, Dr. Rajamani talks about the unique challenges and opportunities of leading MSR’s research efforts in India and what it takes to build a robust research ecosystem in a country of huge disparities. He also dispels some preconceptions about poor and marginalized populations and explains why ‘frugal innovation’ may be one key to solving societal scale problems. That and much more on this episode of the Microsoft Research Podcast. 

Host: Sriram Rajamani, welcome to the podcast!

Sriram Rajamani: Thank you. Excited to be here.

Host: You’re a distinguished scientist and the Managing Director of Microsoft Research India and that’s a big job that encompasses a lot. Not only your own research, but the research of the people that you supervise and guide and direct. So tell us, in broad strokes, what you do for a living? What does a day in your life look like? What gets you up in the morning?

Sriram Rajamani: Oh, boy. So, I’m a morning person, so I’m up quite early. I usually read in the morning. I have a big reading list. My, you know, colleagues, they send me a lot of reading material about work that they do and I usually have a week or two worth of reading material in advance. That’s my reading queue. So my mornings are usually spent reading at home. And then, most of the day is actually spent in small group discussions where we sort of take a research topic, get into it with real depth and we ask many difficult questions. Are we doing the right thing? Are we investing this right? Should we change direction? Should we pivot? That’s the most fun part of my job. I am usually home by five and, with my family, do a group yoga class, in the evening.

Host: No way!

Sriram Rajamani: That’s sort of my break in the evening. And then, late evening, there’s Redmond calls. So starting like 8 pm, 9 pm for a couple of hours there’s usually conversations with either researchers in Redmond or product groups in Redmond. I do spend a lot of time traveling because, as Microsoft Research, we engage with academia, not only in India, but throughout the world, and I come here three, four times a year. So that hopefully gives you a sense for that I do.

Host: Yeah! So tell me about your personal passion and what drives you? What questions are you asking that you would really like to answer? What problems would you really like to solve? Tell me what your heart is for research.

Sriram Rajamani: Yeah, so my personal research is in systems. My PhD was in formal verification. A lot of my personal research quest is actually understanding these extremely complicated computing systems that have really transformed everything around us and understanding what it takes to build them so that they are stable, they are robust, and they do what we intend them to do. So that’s my personal passion. But these days, actually, a lot of my time is spent not only on my own work, but the work of my colleagues, which ranges from, you know, mathematics, algorithms, to artificial intelligence to machine learning to systems to human computer interaction. A lot of my energy gets spent on understanding these various topics. I’m a research junkie so I, actually, I spend a lot of time learning. That’s what I do. That’s my main passion is learning.

Host: Microsoft Research has labs around the world and each one brings something unique to the research endeavor. So give our listeners a brief history of MSR India. It’s kind of fascinating. What’s your particular guiding mission and what were the particular challenges and opportunities for opening a lab in Bangalore?

Sriram Rajamani: The opportunities, in a place like India, are many. First of all, it’s a country with more than a billion people, a lot of them very young, right, so there’s a tremendous amount of potential for talent and what we can do there. It’s a growing economy. And to just give you a sense, right, when I graduated in ‘91, most of my class left to the US to study, but now, if you look at the people that are graduating, a lot of them are staying back because there’s enough economic opportunities there. There’s a lot of interesting things actually happening in India. It is a very interesting test bed. To give you a sense, right, there is about a hundred and fifty spoken languages, each with, you know, a hundred thousand to a million or more people speaking those languages, which you can see how different that is from a place like the US. And if you look at actually languages that are spoken by fewer people, still tens of thousands, there will be a thousand five hundred. Huge disparities in socio-economic conditions. You know, you will find extremely rich people, extremely poor people, everything in between. And wide infrastructure variance. I mean, if you go to a city, it’ll be just like in the US, and if you go to a village there’ll be, like, nothing. So in terms of actually why we went there, we went there because of talent and the opportunities. In terms of how we converged on what we work on there and what’s sort of the unique value that MSR India brings, we’ve always tried to strike a balance between globally applicable science and being inspired by India as a test bed. So you know, as a talent, right, India’s always had really good mathematics talent. That’s one of the reasons why we work in algorithms. We have a very strong set of people that work in algorithms there. Over time, we have built expertise in systems and machine learning, and we also work on socio-economic development, which is a very local thing in India. And we didn’t plan these areas ahead of time. We sort of meandered around and we have converged on these areas over fifteen years. We have sort of evolved over time. And actually MSR lets lab directors the flexibility to just evolve in that story, which is wonderful.

Host: As both a scientist yourself and a leader in technology you’re in a unique position to reflect on trends. And I would say, both those that you observe and those you create. So what does it take, in your mind, Sriram, to be a leader in technology today, and how are you executing towards that in an age of AI?

Sriram Rajamani: Most of a scientist’s job is to predict how the world is going to look like, you know, five years from now, ten years from now. And nobody really has a perfect crystal ball, right? So a lot of it is actually based on your intuition, the intuition of your colleagues, social conversations you have with people, and painting a picture of how the world is going to be five years from now, ten years from now. Let me give some examples, to sort of illustrate what I mean. So I was a grad student at Berkeley in the late 90s, and, you know, during that time, if you sort of think about security, we always thought about security of data much like physical security. Like you store your valuables in a locker and you lock them up and then you do access control. You sort of, you know, decide who gets access to your house and similarly you decide who gets access to your data. So most of security was about access control. We have a very, you know, fine young researcher in our lab, his name is Saikat Guha. Around 2008, he started thinking, oh, no, that’s not the right way to think about security in the internet age. We have to actually think about not only who has access to data, but what they do with it. Which is a real conceptual shift in how they think about security. And when he started thinking about it in 2008, there were very few people that subscribed to that view, right? You know, he was like a lone ranger working on that for several years and he built tools to actually codify those ideas and many years later, when GDPR came, he was already ready…

Host: Wow.

Sriram Rajamani: …with frameworks and so on. And he built a framework called DataMap, over the past decade, that was so influential in how Microsoft thinks about GDPR compliance. Another example I would give you is, and I know you’ve had Manik Varma…

Host: I have.

Sriram Rajamani: as a guest on the… 

Host: Awesome guy.

Sriram Rajamani: …podcast. So he worked on, you know, a machine learning system called Extreme Classification, you know. So I may refresh your listeners to what that is about. You know, today in machine learning, people think about classifying objects or, you know, data into a small number of classes. You know, you could take a picture and classify it as a cat or a dog, but Manik Varma thinks about how to classify things so that the number of categories could be in the order of millions.

Host: Right.

Sriram Rajamani: Right? And when he first started doing that, people thought he was crazy. But today, there are many, many applications in advertisements, in recommendations, in ranking, and Extreme Classification is now a unique sub-area in machine learning that he started. Today, if you go to NIPS or ICML, there is actually workshop in Extreme Classification.

Host: Right.

Sriram Rajamani: Right. That’s an example of, again, foresight into how the world would look several years down the line. A lot of what you need to paint a picture of the future is to have a hypothesis, have self-confidence in it, and have a community that works with you to create that future.

Host: MSR India focuses on four key areas of technology research and you’ve alluded to them already, but let’s talk about them specifically: algorithms, systems, ML and AI, and Technology for Emerging Markets, or TEM. Talk briefly about how your lab is contributing in each of these areas. We don’t have to get granular, but give us an overview of the vision for each of these areas and why they kind of go together, overlap and have their own space as well.

Sriram Rajamani: Algorithms is pretty much the foundations of computing, right? That’s the math behind, you know, data science. The math behind cryptography. The math behind everything that we do in computing. And we are very fortunate to have amazing, incredible minds that actually work in this space. A lot of machine learning actually starts out as algorithms. Thinking that actually happens today will lead to machine learning algorithms maybe five years down the line, ten years down the line. And today, if you look at them, they’ll be math equations written on a white board. So our algorithms group does a lot of leading edge work that is going to only see the light of day five years down the line, ten years down the line. But that said, things that they did ten years ago are now seeing the light of day. For example, we worked on, you know, things like topic modeling, which are now incorporated into working tools that are used by many, many people inside the company today. That’s an example. One other thing that people work on is, you know, you may have heard a lot about deep learning? And one of the things that is interesting about deep learning is that, even though it works in many cases, we don’t quite even understand why it works, what the limitations of that are, when it will fail, and so, you know, people in the algorithms group try and dissect and understand why deep learning does what it does, what it’s limitations are, and understanding what algorithmic tweaks that we need to do to make it even better. And then moving on, you know, to machine learning and artificial intelligence, that’s a very wide spectrum. I already spoke a little bit about extreme classification, which talks about classifiers in the large.

Host: Yeah.

Sriram Rajamani: And, you know, we also work on, you know, machine learning in the small. We work on, you know, Edge ML, which is actually machine learning running on very, very small devices. Devices that you could buy for two dollars or five dollars, and, you know, how do you make machine learning algorithms work on them? You know, another very interesting topic that we work on is something called Approximate Nearest Neighbor. Let me say what that is. Today, the way search engines work is by using something called information retrieval, but that’s yesterday. Going forward, what happens is that, because of deep neural networks, the search is actually done in the higher dimensional space and this requires entirely new algorithmic thinking and, you know, people in our lab, they span over all the way from the algorithms to machine learning so there’s new algorithms that they’ve been designing on how to do this nearest neighbor search, which had the potential to transform the way search engines are built. And then moving on to systems. You know, systems is the foundation of infrastructure on which everything else is built, including AI and ML. So, we work on the interaction between machine learning and systems. We sort of think about how machine learning can make systems better. How can we get the signals that actually come from our data centers where the data centers are constantly running billions and billions of computation and if something fails we get those signals back, crashes, we get those signals back. How can we use machine learning to map those things back to actual code that people write, so that when something fails we can point out, hey, this fails because this line of code is actually not working right. We try to use machine learning to figure out how to optimize COGS, which is cost of goods, right? We also try and build systems for better machine learning. How do you build better infrastructure so that we can utilize GPUs better and do better GPU training? And then the final area, which is Technology for Emerging Markets, you know, we do things ranging from public health to education to we study illiteracy, we study human rights, how to build technologies so that they are just and fair? So those are the kinds of things that we do.

(music plays)

Host: Let’s talk for a minute about why India is the ideal place for disruptive technology and how constraints drive innovationHow are the realities of life in developing areas of the country turning some current assumptions about technology upside down?

Sriram Rajamani: As I mentioned, right, India has wide, you know, socio-economic disparity. In Bangalore, you could go to a mall that would look much like Bellevue Square. And on the other end, you could go to a rural area in which, you know, there might not even be electric power. I think the number one thing that strikes you, when you try to build technology in India, is the resource constraints. You know, if you want to build technology that actually fits the lowest common denominator, that actually works everywhere, the resource constraints that you have to think about: cost, bandwidth, the diversity of users. I already mentioned the number of spoken languages and so on. I think those are extreme in India. Because of that, right, if you build systems that somehow work in those constraints, you are innovating for the world. One saying I’ve heard is actually that if you make something work in India, it’ll work anywhere! That’s actually something I’ve heard. You know, and it’s so true, right? If you sort of go to a rural area and open up your mobile and press ‘download’ on something, it just spins forever.

Host: Yeah.

Sriram Rajamani: Right? How do you build a system that supports those users as well as users in the city? That, I think, is a tremendous opportunity. So in our lab we actually have thought a lot about this. One of the terms that actually describes best what we do is called ‘frugal innovation,’ innovation that actually thinks about cost, essentially as its core, because if something is not low cost it’s just not going to fly, right? And the thinking about technology as an amplifier of human ability. I think, so technology should not replace people, because, you know, there’s no point in doing that, right? So the point is actually to use technology to amplify human ability because the real scarcity is actually talent.

Host: Right.

Sriram Rajamani: Right? And skill. So how can we amplify skill that a few people have to serve more people? Thinking about poor underserved populations a lot more carefully. You know, distinguishing between their needs and wants. Most of us, actually, in the west think about, you know, when we work with poor people, we think about health, education, right? Those should be their needs, right? But in reality, if you study them, they have a lot of wants. You know, they want entertainment. They want employment, right? But thinking about poor not as just consumers of information, but producers of information. They have very many interesting things to say. Thinking about the lived in experience of the two billion people that are not yet part of the digital economy because of many, many reasons. Illiteracy. Thinking about illiteracy as a cognitive deficit. And thinking about, you could give them the best smart phone, you could give them the best 3G/4G connectivity, but if they don’t have textual literacy, how are you going to connect them and include them? I think the final thing I would say is that when you design technologies, you know, to serve this kind of community, being completely honest to yourself that it actually works. You know, doing rigorous scientific evaluations to actually see whether it makes a difference or is this a shiny object that you just designed in a lab, you know, just because it is fun?

Host: Right.

Sriram Rajamani: Right?

Host: You know you’re harkening back to Ed Cutrell who was on the show and I know he did work in India

Sriram Rajamani: Yes, he used to be in our TEM group for many years.

Host: Yeah, and some of the stories he told on the podcast he was on – I encourage people to go listen to that one – because there’s actual stories of things that they thought would work in particular scenarios, that they just wildly didn’t, but not for the reasons they thought they wouldn’t, right? It’s like …

Sriram Rajamani: I could tell you a story.

Host: Do, please! I love stories!

Sriram Rajamani: Yeah, so, we have a researcher. Her name is Indrani Medhi-Thies. She is one of the world’s leading experts on illiteracy. So, you know, she and a bunch of others wanted to build a job website for low income people. Sort of like a monster.com…

Host: Right.

Sriram Rajamani: …or something for like cooks or drivers and, you know, low income labor. And so they built it with only pictures, right? Because these people, you know, wouldn’t be able to read text so they built the whole interface using pictures. And I remember, you know, there’s a slum near our lab, so they wanted to do a pilot in the slum, so there was a lot of discussion in the lab about how to put a computer there so that the computer wouldn’t be stolen! And so that actually you can access it, but, you know, you can’t walk away with it. And then everything, you know, you could apply using pictures. You know, you could actually look at the job listings and it did all of that, right? And after that, they deployed it and the usage was zero. It was there and people were curious about it, but nobody used it! And what occurred to Indrani was that the reason is actually they have no conception of what this thing would even do! And so what they did was, you know, they enacted something like a soap opera in the lab, with actors from the lab. There’s a woman who sort of is complaining to her husband that she needs domestic help. And then the husband goes and registers the fact that they need domestic work in this site and then there’s a woman who comes and accesses this computer in the slum and she clicks on this and she gets introduced and they meet and she gets the job. This is now being run as a screen saver in that computer and then the usage of the thing skyrocketed!

Host: Interesting.

Sriram Rajamani: Right? So Indrani’s main conclusion, right, is that illiteracy is not just about textual literacy. It’s about lack of context and awareness. And unless you actually put yourself in the shoes of a person who has never seen something like this before, then you’re not going to fix this by, you know, just pictures, right? So that’s an example of things that you think that would work, but wouldn’t work.

Host: You and your colleagues are tackling some, what you call, societal scale issues. Healthcare, education, agriculture, employment, connectivity, transparency we’ve talked about quite a few of these already. Give us some more context for the research projects that your teams are working on that might give us cause for hope for some societal scale solutions.

Sriram Rajamani: Yeah, so, I can tell you a few stories. I already told you…=

Host: I love stories!

Sriram Rajamani: …the illiteracy story.

Host: Keep going!

Sriram Rajamani: Yeah! So one project where we have made a lot of traction is a project called 99DOTS, which is, you know, around  technologies for tuberculosis medication adherence. This is a project that was initiated by Bill Thies and Andrew Cross. So the context for this is that TB is a curable disease, but you have to take medication for six months. And if someone falls out of medication regimen, then they get something called drug resistant TB, which is both contagious and fatal. So the only way to cure TB is to make sure that a healthcare worker meets with the patient every day for six months to ensure that they have taken medication. And you can imagine how cumbersome it is for both the healthcare worker and the patient, right? So suppose we could use technology to gather information. Then the healthcare worker could spend all their time on people that are actually not taking medication. So they designed a sensing system, which they iterated many, many times, but what finally works is actually they work with pill manufacturers, and they designed a new paper strip so that actually when they dispense the pill, it reveals a phone number to which the patient is actually counseled to give a free call. On the other side, a computer picks up the call and it records that, oh, this person is now taking a drug. So the computer knows when the calls are coming, and when the calls don’t come, there’s like a red bar saying, this person hasn’t called and then the counselor spends time on that patient. Now this was started out as a research project in our lab and then we spun it off into a non-profit because now the government wanted to adopt it.

Host: Right.

Sriram Rajamani: You know, the Gates Foundation, USAid, they wanted to fund it. So we spun it off into a separate start-up company called Everwell, and it’s walking distance from our office. They employ about twenty people and they’ve enrolled more than two hundred thousand patients.

Host: Wow.

Sriram Rajamani: Some other examples are, one of the things we work on these days is road safety. So traffic accidents are a huge killer of people in India. And so we have a research project called HAMS where, what we’re doing is, just using a smart phone, we can monitor both the behavior of the driver and the surroundings so we can actually know whether a driver is sleepy, whether they are wearing a seatbelt, you know, whether they were talking on the phone when they were driving… you can imagine how this technology could be used to monitor fleets, how to make driving safe and a very interesting application of this is in automated driver licensing. So today, if you go to Dehradun, as of two months, if you go do a driving test, there’s no instructor.

Host: It’s a phone.

Sriram Rajamani: It’s a phone. And then you drive, and then, an automatic print out gets printed out saying these are the things you did right, these are the things you didn’t do right, and you passed or failed.

Host: Oh, interesting.

Sriram Rajamani: So let me add a few more, right?

Host: Yeah.

Sriram Rajamani: I mentioned HAMS. You know, like, you know, BlendNet is another project because connectivity infrastructure is such a big issue. Now if you go to, you know, rural areas, right, you can get text messages by, but if you try and download a video, you’ll see the wheel spinning forever and you will be never able to download something.

Host: Right.

Sriram Rajamani: So BlendNet is a very interesting idea where most of the popular videos and other bulk things you want to download, actually other people want them too.

Host: Yeah.

Sriram Rajamani: Odds are that somebody else will have it. So BlendNet is what is called a Cloud Connected Content Distribution Network where, if you want to download a Bollywood movie, what you do is actually you use the 2G/3G only to actually say what you want, and the cloud has some meta data which actually stores who has what video. The actual video might come from you.

Host: Right.

Sriram Rajamani: I just connect to the cloud and say I want this movie, but the actual movie comes by your phone turning on your Bluetooth or your local WiFi, my phone turning on…

Host: And peer-to-peer.

Sriram Rajamani: …and peer-to-peer, right? And using that.

Host: So fascinating. You know, just going back to your Bollywood movie download, those are four-hour productions

Sriram Rajamani: Absolutely!

Host: …that encompass every single human emotion… and dancing!

Sriram Rajamani: At the same time!

Host: You want your money’s worth!

Sriram Rajamani: Absolutely!

Host: Well several trends in technology have actually broadened the scope of the problems that we could solve today, you know, hyper compute power, sophisticated algorithms and massive amounts of data, but people in the field are starting to recognize that we need more than computer scientists to solve these problems. So give us your take on the trend towards interdisciplinary research, especially in the light of technology for emerging markets.

Sriram Rajamani: Yeah, I think this is a very important question. Maybe I’ll, again actually, in the spirit of storytelling, let me actually give that as an example with a particular project, right? You know, I mentioned a few times, Edge ML, right? Edge ML is about running machine learning on very small devices. This is actually the dream that, you know, today there are these very small devices and they are primarily used as sensors and their capability is to just transmit information to the cloud, and the assumption that they will work only when there is cloud connectivity. But the Edge ML’s hypothesis is that, what if you could actually do machine learning there? But now, it’s a very difficult question. First of all, you have to start with the math to figure out can we actually do it? That’s where the algorithms people come in. And then, after the algorithms people figure out that actually you can do it, then you need the machine learning people to actually design those algorithms. We need systems people and compilers people to compile those algorithms to run on those small devices. And then you actually you need HCI people to think about what this might really solve. You know, when you imagine the future, right, you have to think about what the algorithms are, what the systems are going to look like, and, actually, how people are going to interact with it.

Host: All right, let’s talk about talent. You alluded to that at the beginning. You’re what we call in the United States a 4A high school. You’ve got a lot of kids…

Sriram Rajamani: Yeah.

Host: …to choose for your football team.

Sriram Rajamani: Yes.

Host: So with billions, one of your problems might not just be that you have a lot of people to choose from, but you have a competitive environment for getting the best talent to come to work with you. So what’s MSR India’s value proposition to get the best and brightest AI talent these days?

Sriram Rajamani: So India is a pretty interesting place from a talent perspective. You know, we have a really strong undergraduate population, but our graduate program, you know, still lacks critical mass with the number PhDs that come out of India. So one of the things we do is that our PhD recruiting is very global. And that’s our hiring opportunity, right? So we recruit globally, you know, from people, perhaps of Indian origin, and there are people like Bill Thies and Andrew Cross who are, you know, not of Indian origin, but they want to come live there because of India as a test bed. So, you know, I think one of the things we have done very cleverly, if I may say so, is to think about recruiting very, very globally, particularly at the PhD level. And, you know, even if a small fraction of Indians living worldwide want to come back, right, a small fraction of a billion is still a very large number, right? So every year, right, even if ten people want to return you just, you know, pick the best of those ten and hire them. And then, actually, undergrads, we actually work with undergrads in India. We have a program called the Research Fellow Program where it’s sort of a pre-doctoral program where we take undergrads and they spend one to two years with us as research apprentices. And then they go off to do grad school in the West, you know, typically in Europe or in the US. And, you know, in the fifteen years we have been running this program, I think we would have graduated maybe five hundred such research fellows. Many of them have now finished PhDs and they’ve come back. So, you know, we spend a lot of time nurturing young talent, you know, because we play the long game. That’s the way we work with undergrads. And in terms of value proposition, right, there are people like me who want to do honest-to-god good science, right? And they want to live in India. Here is an environment where you could do research like anybody else in the world if you choose to live in India. And then, actually, you combine that with locally relevant work, like Technologies for Emerging Markets, where you connect with the community, think about India as a test bed, and you put those both together and then you get a different kind of energy. And that’s what MSR India is.

Host: Collaboration seems to be a big trend in an era of AI and ML research. So first I want you to tell us why collaboration is really important in your world particularly, and then tell us about some of the collaborations you’re involved in and how they’re bearing fruit.

Sriram Rajamani: Yeah, so I already mentioned about interdisciplinary collaboration in the lab and I think that’s very central to what we do, but in India, the other thing that’s very important for our lab is collaboration with our ecosystem, which is the academic ecosystem. It’s quite important because the graduate program is still not quite strong. So many of our staff are adjunct faculty in Indian universities, so many of us co-teach courses, we co-supervise PhD students, and that’s a very integral part of what we do. And I think that has actually built real trust and credibility with the academic ecosystem. Now you mentioned Manik Varma. Manik Varma recently was awarded the SSB prize. It’s one of the most prestigious awards in interdisciplinary science. Also, right, there is an Indian National Academy of Engineering. So we have three fellows from INAE in our lab. I’m one of them, right? And we have a MacArthur Prize winner. We have a Knuth Prize winner, and so on. So, all of these, right, are not just bragging about our staff. I think these are really awards to collaborations that these people had with the community. And these recognitions come not because these guys sit in a lab and work, but they share the work and bring the energy of the academic community. So that’s actually super important in a place like India.

Host: Talk a little bit about the research ecosystem there and some of the work that you’re doing to build community and train people – you’ve alluded to the Research Fellows Program, but there are other things you’re doing, sort of broader spectrum. Talk a little bit about that.

Sriram Rajamani: So one of the things that we’re doing is to, for example, bring conferences into India. Travel grants for Indian academics are very, very hard to get, right? You know, we are actually privileged to be in a place like MSR where we can travel and go to conferences, right? But many students in India, they just don’t have the ability to go to conferences. So if they can’t go to a conference, we try and bring the conference to India! So that’s something that we try and do. So we participate in a lot of those kinds of activities. We also organize workshops. Years ago I started a series called Mysore Park Series where, you know, we get high quality peer interaction. You know, people get to a community in a small group and discuss topics for like four days, five days, because, you know, you have to actually get people to talk to each other and we spend a lot of time and energy creating those kinds of conversations, nurturing those kinds of conversations, and the community is very welcoming of us doing that. That’s one of the reasons why people join our lab. When they join our lab, they’re not in a bubble. They’re actually connected to an environment and connected to the ecosystem around us.

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Host: We’ve talked about what gets you up in the morning, Sriram, but this is the part of the podcast where I ask what could possibly go wrong. So given the power of AI and its potential for both great good and great harm, is there anything that keeps you up at night? And if so, what are you doing to mitigate it?

Sriram Rajamani: There are some people who believe that AI will become like the Schwarzenegger Terminator and come back and kill all of us. I, for one, don’t believe that. I don’t believe that. I know we are very far away from that. But what worries me more is not the fact that, you know, AI will be, you know, all powerful and conquer us, but, you know, I’m a software reliability person, I’m a systems person, and I actually want systems to work well. My worry is more that, in our enthusiasm as technologists, we overestimate what AI can do and deploy it before it is ready. That worries me more than, you know, AI conquering us. AI is, of course, trained by data. And if the data is not representative, it’s going to cause huge amounts of bias, and it’s going to take decisions that systematically amplify, you know, human biases that people have. People are aware of this and, you know, that keeps me up at night because we really think about whether the AI is actually really helping people, not only in terms of research, but I also think about it in terms of investment, now that I’m in a lab director position. To give you a sense, right, one of the biggest promises of AI is natural language processing because you can now talk to a computer. And if you are an illiterate, right, that is going to open doors. You know, if you can’t read and write, but if you can speak, and the computer can understand you, it’s going to bring you into the part of the digital economy. But look at the investments in NLP: they are all in English, in German… you know, those are the markets where the money is.

Host: Right.

Sriram Rajamani: And that’s where, actually, people are investing more and more to make, you know, your speech assistants understand, uh, you know, these kinds of languages. But what about the hundred and fifty languages? What about the one thousand five hundred languages? What about the tribal languages that are spoken by ten thousand people? And all of them are illiterate, right? So are we doing enough investment to include them in this AI driven economy? And so that disparity, I think, is something that I worry about. I think it’s extremely important to think about entrepreneurship, right? Because, you know, marginalized people, poor people, they want to live better.

Host: Right.

Sriram Rajamani: And they have a lot of energy in them, right? I think creating entrepreneurship opportunities for them so that they can generate economic value, so that you don’t just donate money to them, but you sort of enable them to be successful at creating businesses and then creating economic value, which will then lead to an ROI. But the real difficulty in these kinds of things, right, even if you do them, they are all going to be in the knee of the hockey stick. It’s going to be many years of investment before you see the exponential that has come up, right? So I think the biggest challenge is actually in persevering through this exponential. It’s a very difficult thing to do.

Host: It’s story time and I would love to hear yours. So even though we’ve been telling stories pretty well the whole podcast, let’s get a personal story in here. Tell us a little bit about your history and where you’ve studied, where you’ve worked, what got you started along your path, and how you ended up at MSR in your leadership position today.

Sriram Rajamani: So I did my, you know, undergrad in India, and like most of my colleagues, I came here for graduate school. I first did my master’s at UVA, at University of Virginia, and I thought that maybe I wouldn’t be a researcher. So I went and became a programmer. I wrote software in the Silicon Valley for a few years and I wrote hundreds of thousands of lines of code. And then after a few years, I decided I really wanted to do research. So I went back to the PhD program at UC Berkeley and I did my PhD in formal verification. And after I did my PhD – I used to work in formal verification for hardware circuits – and, you know, around the time I graduated, you know, I met Jim Lattis and Amitabh Srivastava. You know, Amitabh was running this place called Programmer Productivity Research Center. And they recruited me to see whether these kinds of formal methods for hardware, can it be used for software? I found it very intriguing.

Host: Yeah.

Sriram Rajamani: So I came here with that, you know, hook in mind, and I met Tom Ball, who is still a researcher here, and he and I did many years of collaboration where we sort of tried to combine formal methods, both in the hardware area, together with theorem proving, together with compiler-style stuff that the software people do to really think about how to formally validate software. Mostly analysis work is what I did when I was here. And then I went back to India around 2005, you know, a few months after our lab started, and my initial work was on design, software design. So there, actually, in sort of finding bugs in a driver after the driver is written. Think about how might you write it so that, by construction, your software is actually better. So we designed a language called P, where you design software in a high-level language and you analyze your design and make sure your design is robust and then you generate code from it and that’s what runs. And, you know, methodology from this is actually what is now being used to run your USB stack, right? And then I worked on security in MSR Cambridge to build cloud where you can actually guarantee that hackers can’t have access to your data. So in my own story, I went to MSR India when I was in my mid-thirties and three years ago I became a lab director, so I’ve had the fortune of being an individual contributor, a researcher, a group manager and now lab director. So that’s been my journey.

Host: What’s one thing that people might not know about you that may have influenced you to be a researcher or a leader in tech?

Sriram Rajamani: I think probably most people don’t know that I’m a village boy. My dad used to be in the agricultural department in southern India. He worked for the government. So I was born and I grew up in villages with no electric power. I’m not that old, right? But…

Host: It’s true. I’m looking at him. He’s not that old.

Sriram Rajamani: But I grew up in villages in which there’s no electric power. There was no cooking gas, so my mom used to cook with charcoal and firewood.

Host: Yeah.

Sriram Rajamani: So I have that kind of upbringing, and I think that influences me in many, many ways. I’m the first person from my family to ever leave my country, and it’s sort of full-circle for me to be from that environment, go study here, and go back and, you know, be a considerable part of our lab, work on technologies that benefit, you know, rural people, you know, people living in poor areas and so on.

Host: As we close, I want to give you the last word, Sriram. You’ve compared research to a marathon. Tell our listeners who may be just getting into the race what they have to look forward to and why, in the long run, they shouldn’t be afraid of the long run?

Sriram Rajamani: If you want to do science that changes the world, you need to give time. It’s extremely important to do that, because to make any research mark, it’s going to take many, many years, because you’ve got to try, many things will fail, you know, some things will work. And even if some things work, it has to actually gather critical mass. It has to attract attention from people. The right environment should be there for it to get deployed and so on. So things take a long time. So one advice I would give is, you know, just be prepared for the long haul, you know. It takes many, many years to make mark. As a result, it is extremely important to pick problems that you like. Pick areas that you like so that you have fun. Otherwise it’s hard to actually sustain the energy to run the marathon. The other advice I would give is to not be lonely. Not do it alone. Build a community of colleagues to collaborate with you. Pick people that have quite different skills from you to collaborate so that you can actually learn from others. You teach what you know, you actually learn from others. Research is very much a social process. That’s another thing that I would encourage. And the other thing I would encourage is, you know, think about problems that many, many people care about. Real world problems, that if you actually solved them, it will make a real difference. And work on those problems that are hard to solve, you know, rather than count the number of papers you publish, right? I will say, I think it’s far more satisfying to do a few things that change the way science progresses, change the way a field changes, you know, rather than have a laundry list of publications.

Host: Sriram Rajamanithank you for coming all the way from Bangalore just to see me!

Sriram Rajamani: Gretchen, you know, I’m so happy that you spent the time thinking about what a lab is, and doing this podcast. And also thank you for the opportunity to share the story with your audience. The gratitude is mutual.

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To learn more about Dr. Sriram Rajamani, and the latest innovations out of MSR’s lab in India, visit Microsoft.com/research 

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