Episode 008 | April 20, 2021

Microsoft Research India is constantly exploring how research can enable new technologies that positively impact the lives of people while also opening new frontiers in computer science and technology itself. In this podcast we speak to Dr. Sriram Rajamani, distinguished scientist and Managing Director of the Microsoft Research India Lab. We talk about some of the projects in the lab that are making fundamental changes to the computing at Internet scale, computing at the edge and the role he thinks technology should play in the future to ensure digital fairness and inclusion. Sriram also talks to us about a variety of things his own journey as a researcher, how the lab has changed from the time he joined it years ago, and his vision for the lab.

Sriram’s research interests are in designing, building and analyzing computer systems in a principled manner. Over the years he has worked on various topics including Hardware and Software Verification, Type Systems, Language Design, Distributed Systems, Security and Privacy. His current research interest is in combining Program Synthesis and Machine Learning.

Together with Tom Ball, he was awarded the CAV 2011 Award for “contributions to software model checking, specifically the development of the SLAM/SDV software model checker that successfully demonstrated computer-aided verification techniques on real programs.” Sriram was elected ACM Fellow in 2015 for contributions to software analysis and defect detection, and Fellow of  Indian National Academy of Engineering in 2016.

Sriram was general chair for POPL 2015 in India, and was program Co-Chair for CAV 2005. He co-founded the Mysore Park Series, and the ISEC conference series in India. He serves on the CACM editorial board as co-chair for special regional sections, to bring computing innovations from around the world to CACM.

Sriram has a PhD from UC Berkeley, MS from University of Virginia and BEng from College of Engineering, Guindy, all with specialization in Computer Science. In 2020, he was named as a Distinguished Alumnus by College of Engineering, Guindy.

For more information about the Microsoft Research India click here.

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Transcript

Sriram Rajamani: We are not like an ivory tower lab. You know we are not a lab that just writes papers. We are a lab that has our hands and feet, dirty, we sort of get ourselves dirty sort of get in there, you know, we test our assumptions, see whether it works, learn from them and in that sense actually the problems that we work on are a lot more real than a purely academic environment.

[Music]

Sridhar Vedantham: Welcome to the Microsoft Research India podcast, where we explore cutting-edge research that’s impacting technology and society. I’m your host, Sridhar Vedantham.

[Music]

Sridhar Vedantham: Microsoft Research India is constantly exploring how research can enable new technologies that positively impact the lives of people while also opening new frontiers in computer science and technology itself. In this podcast we speak to Dr. Sriram Rajamani, distinguished scientist and Managing Director of the Microsoft Research India Lab. We talk about some of the projects in the lab that are making fundamental changes to computing at Internet scale, computing at the edge and the role he thinks technology should play in the future to ensure digital fairness and inclusion. Sriram also talks to us about a variety of things his own journey as a researcher, how the lab has changed from the time he joined it many years ago and his vision for the lab.

Sridhar Vedantham: So today we have a very special guest on the podcast, and he is none other than Dr. Sriram Rajamani, who is the Managing Director of the Microsoft Research Lab in India. So Sriram welcome to the podcast.

Sriram Rajamani: Yeah, thank you. Thank you for having me here, Sridhar.

Sridhar Vedantham: OK, you've been around in Microsoft Research for quite a while, right? Can you give me a brief background as to how you joined and when you join and what's your journey been in MSR so far?

Sriram Rajamani: Yeah, so I joined in 1999. And , oh man, it's now 22 years, I guess. I've been here for a while.

Sridhar Vedantham: That's a long time.

Sriram Rajamani: I joined in Microsoft Research in Redmond right after I finished my PhD in Berkeley and then I, you know, my PhD was in formal verification. So, my initial work in Microsoft in Redmond was in the area of formal verification and then at some point I moved to India around 2006 or something like that. So I think I spent about six or seven years in Redmond and my remaining time- another 15 years- in India. So that's been my journey, yeah.

Sridhar Vedantham: OK, so this is interesting, right, because, you know, we constantly hear about India as being this great talent pool for software engineers, but we certainly don't hear as often that it is a great place for a computer science research lab. Why do you think a Microsoft Research lab in India works and what drew you to the lab here?

Sriram Rajamani: I'm a scientist and I joined MSR because I wanted to do high quality science work that is also applicable in the real world, you know. That's why I joined MSR and the reason why I moved to India was because at some point. I just wanted to live here - I wanted to live here because I have family here and so on and then Anandan started the lab and so somehow things came together, and that's why I personally moved. But if you ask, you know, ask me why it makes sense for MSR to have a lab here, the reasons are quite clear.

I think we are such a big country, we have enormous talent. I think talent is the number one reason I think we are here. Particularly unique to India is that we have really strong undergraduate talent, which is why we have programs like our Research Fellow program. But over the past, many years, right, the PhD talent is also getting better and better. As you know, initially when we started, you know, we recruited many PHDs you know from abroad, who had their PhD from abroad and then return just like me. But over the years we've also recruited many PhDs from Indian institutions as well.

So, I think that talent is the number one reason.

The second reason is you know the local tech ecosystem is very different. It started out as a service industry for the West- you know essentially all of the software we were doing, we were servicing companies in the western hemisphere. But over time, India has also become a local consumer of technology, right? Now, be it if you sort of think about, you know Ola or Flipkart, you know, the country is now using technology for its own local purposes. And because of the size and scale of the country, the amount the government and industry is pushing digitization, there's a huge opportunity there as well.

And finally, I would say another reason to have a lab is in a place like India that it's a very unique testbed. You know, cost is a huge concern in a place like India, technology has to be really low cost for it to be adopted here. There are very severe resource constraints. Be it bandwidth…you know if you think about NLP, you know many of our languages don't have data resources. Very unreliable infrastructure- things fail all the time, and so you know, I've heard of saying that you know if you build something so that it works in India, it works anywhere. So it's a test bed to actually build something.

If you can deploy it and make it work here, you can make it work anywhere. So in that sense actually it's also another reason.

Sridhar Vedantham: OK, so basically it works here it's a good certification that it'll work anywhere in the world.

Sriram Rajamani: Yeah, yeah.

Sridhar Vedantham: All right. OK Sriram, so here's something I'm very curious about. How does a research scientist end up becoming the managing director of a lab?

Sriram Rajamani:

So the short answer is that it was rather unplanned, but maybe I can give a more longer answer. You know, I started out, you know, being a researcher like anyone else who joins MSR. My initial projects were all in the area of, you know, formal verification, you know, I built together with Tom Ball something called static driver verifier that used formal methods to improve windows reliability. Then I worked on verifiable design- how can you do better design so that you produce better systems?

Then I worked on, you know, security, and now I work on machine learning and program synthesis. And you know, a common thread in my work has always been the use of programming languages and formal methods to sort of understand how to build various kinds of systems be it drivers, be it secure systems, be it machine learning systems. That has been sort of the theme underlying my research. But to answer your question as to how I sort of became lab director, you know, after some years after I moved back to MSR India, you know Anandan who was the lab director then, you know, he left. There was a leadership churn there, and at the time I was asked whether I would consider being the lab director. The first time I declined and because I had many other technical projects that are going on. But I got the opportunity the second time, you know, when Chandu and Jeanette really encouraged me when Chandu decided to move on. I had been in MSR maybe 15-16 years when that event happened. And one of the reasons why I decided to take this up was I felt very strongly for MSR, and I thought that MSR has given me a lot and I wanted to give back to MSR and MSR India.

And MSR India is easily one of the best CS, computer science industrial labs in this part of the world.

And, you know, it made sense that I actually devote my time to support my colleagues, grow their lab in ambition, impact and I sort of had a sense of purpose in that, and so I decided to take this on. So the real answer your question is I don't think anyone plans to be a lab director, and sometimes you know you get an opportunity to become one, and sometimes you say yes.

Sridhar Vedantham: Great. OK, and you know, given that you've been in the lab here in India for quite a while, how do you see the lab having evolved over the years? I mean, I'm sure there are lots of things that have changed quite a bit. So, what do you think are those things that have changed quite a bit and what's not changed and what are the kind of things that you'd like to preserve going forward?

Sriram Rajamani: Yeah, I think the number one thing that has not changed is quality. MSR… I've now been here for now 21 years and I've been with MSR India you know, from the very beginning. I came here after 6-7 months after the lab started. We've always had great people and the quality of the work we do has always been exceptional. But I think what has changed over the years is that we think much more end to end. When I joined, you know, ’99, we were sort of more academic in nature. We always used to publish in high quality conferences, which we still do. But I think what we do more now is that we think much more end to end. We are no longer satisfied with solving a particular piece of a problem, but we sort of think about how does that piece connect with many, many other pieces, some social, some technical and how do those things fit together broadly to solve problems end to end. We sort of think about that a lot more. As a result, I think we more often than not deploy what we build, you know, either in scale, solutions that actually are adopted by product groups, or actually in our communities to actually validate whether what we think about as something that creates a change, does it indeed create a change and learn from that and use that to even reframe our problems, test our assumptions. And so, you know, I don't think we are, we are not like an ivory tower lab. You know we are not a lab that just writes papers. We just we are a lab that has our hands and feet, dirty, we sort of get ourselves dirty, sort of get in there, you know, we test our assumptions, see whether it works, learn from them and in that sense actually the problems that we work on are a lot more real than a purely academic environment.

I think that that's the way in which things have changed. And I think partly also, Sridhar, as you do that,

we have become a lot more interdisciplinary…

Sridhar Vedantham: Right.

Sriram Rajamani: …you know, if you look at our projects today, right? Because if you want to get something to work end to end, it is not just one piece you build, you know. You have to make it interdisciplinary and many of our projects are interdisciplinary. I think that's the other way in which we’ve changed.

Sridhar Vedantham: Yeah, in fact this particular term, right, interdisciplinary research- is something that I've heard quite often coming from you. Do you want to just bring up a couple of examples of what you mean by interdisciplinary research through by using some projects as examples?

Sriram Rajamani: Yeah, I can give like you know, two or three you know. The first one that comes to mind is our EzPC or you know, our multiparty computation project. And if you look at actually how that project is working, the whole goal is to take computations, be it, DNN training or DNN inference and run it securely with multiple parties. And you know that's a pretty complex problem. There’s compiler people, there's programming languages people, and there's cryptographers. All of them work together to build a solution where the programmer can express their computation in some language, and there's a compiler that compiles it. And then there is, you know, a lot of cryptography smarts are there in order to make this you know, multiparty computation work and, and that's very unique. You can't do this without compiler people and the cryptographers getting together.

Another example, you know, is if you look at our Akupara semantic search work, that's actually a combination of algorithms, work machine learning work and systems work so that we can index trillions of vector indices and look them up right in a reasonable amount of time with a reasonable number of machines. I mean, I can't imagine you doing that without expertise in algorithms, machine learning and systems.

And if you look at our, more recent, you know societal impact projects like we have this project called HAMS which we are using to improve road safety. I mean that has actually quite a bit of tech like computer vision. You have to make that work on a smartphone. So you need to have systems innovation to actually make that work on a smartphone. And it has quite a bit of HCI. I mean it has to work in an environment where you go into a driver license testing RTO and it should just work there, right? You know it has to work with people, the feedback that is given should be consumable. You know if somebody fails a driving test, the feedback has to be given in such a way that it's a positive experience for them even if they fail the test, right? So it has all these interdisciplinary aspects. And so I hope that those give you a little bit of a flavor for what it takes to solve things end to end.

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Sridhar Vedantham: A lot of the listeners of this podcast are not going to be really familiar with all the stuff that we do here at MSR right, at MSR India especially. In your mind, you know, how do you kind of categorize or bucket the different research work that goes on in the lab?

Sriram Rajamani: We now think about our work as being classified into themes and the themes are different from our expertise. If you look at our expertise, right, our expertise has always been, you know, from the beginnings of the lab, we have 4 broad areas of expertise.

We have, you know, people with expertise in algorithms, second in machine learning, and third in systems, very broadly interpreted in including programming languages, distributed systems, networking, security and so on.

And then we have people who do human computer interaction and social sciences, right?

Those are our four areas of expertise, but if you look at the way we organize our work now, it is in the themes. We have 5 themes. One theme is around large-scale machine learning, you know, things like recommendation systems, search, large multilingual learning which spans an entire gamut, from algorithms to practical machine learning algorithms as well as systems, right, you know, in order to build them and scale them.

Then we have two systems related themes. One is data driven systems and networking where we are using telemetry and the enormous amount of data that we get from large scale cloud systems to do machine learning on them and improve those systems themselves. And then the second systems area we have is called co-designed systems, where we think about interdisciplinary systems work that spans distributed systems, security, privacy, programming languages, verifications. So we sort of think about systems much more holistically.

Another thing we have is edge computing, where we sort of think of about machine learning systems, usability in the edge, which is such an important topic from the perspective of India. And the last theme is socio-technical systems and inclusion where we really think about technology as an ally for an enabler for inclusion and empowerment.

And each of these five teams, right, draws on the expertise of people from these various disciplines.

Sridhar Vedantham: Great, so I've heard you many times talking about things like tech at scale, so I think you have a couple of things you know. There are a couple of things that you said that kind of stick in my mind, so there is one tech at scale and then there is one tech in minute form. I forget the exact terms you use and socio-technical computing is also quite big at MSR India right now. Could you give me a flavor of what exactly is happening in, say, the tech at scale area and also the social technical computing area?

Sriram Rajamani: Yeah, so I think the tech at scale is quite important because digital systems are very pervasive now. The pandemic has only accelerated the adoption of digital systems. Most interactions these days are online, and even when we come back from the pandemic, it's going to be hybrid, right? The amount of information is just increasing and increasing and increasing and as a result, right, for any useful user experience we need to be able to sort through this huge amount of information and make the right information available at the right time, right. And that I think is in some sense the primary goal of AI and machine learning and systems at scale, and I think most of our systems at scale work are about how to build systems that use AI and machine learning to process huge, humongous amounts of information in billions and trillions of pages or documents, or vectors, understand them and make sure that their right information is available to you at the right time. And how do you do that reliably, how do you do that securely, how do you do that while preserving privacy? So that I think is the is the crux of our tech at scale.

I already mentioned Akupara, which is a trillion-scale index and serving system that we are building for semantic search. Another at-scale project we are doing is called extreme classification where we are trying to build classifiers that can take an object and classify it into hundreds of millions of categories, right? And just like when we think about machine learning, we think about a picture and classifying it into a cat or a dog or a small number of categories. But in extreme classification we take an object like a web page or a document, and we classify it into, potentially, millions or hundreds of millions of, for example, topics. I mean, what are these topics that this document is talking about, or if this object that I'm talking about is an advertisement, what are the keyword bid phrases that are actually relevant to this advertisement, right? So those kinds of classifications are significantly more complex, and our lab really originated this field and is a thought leader in this field.

Another at-scale work that we are doing is if you take the area of DNN training. Deep neural network training is an extremely resource intensive process. You know if you take, billions and billions of training points and train deep neural networks, that uses a huge number of GPU resources and other hardware resources. Can you do that more efficiently? And we have a project called Gandiva that improves the throughput of all of the infrastructure that we are using to train these kinds of DNNs.

And we want to give you one more example. We have a project called Sankie and what Sankie does is actually, to use all of this telemetry from huge software engineering processes, including coding, testing, development to really improve the productivity of the engineering itself. So, I would say you know those are the kinds of examples of at-scale AI and ML and Systems project that we do. And I think every CS lab has to do that because that is the real world today.

Sridhar Vedantham: And we've actually done a podcast earlier on Sankie, so I think I'll link through to that when we publish the transcript of this podcast.

Sriram Rajamani: Wonderful.

Sridhar Vedantham: Right, and in sociotechnical computing, do you want to talk a little more about that? And this is something that personally I find quite fascinating, you know, because this lab has always had, from the very beginning, a focus on the ICTD space itself. But the kind of projects that are happening now in the lab seem to be taking that to a different level altogether in terms of actually going out there and figuring out the impact and deploying at scale.

Sriram Rajamani: You're right, Sridhar, that Technology for Emerging Markets has always been a really interesting area in the lab from the inception. But one thing that has changed if you sort of think about it, is that...see, when the lab was started, right, the kind of technology that was available to everybody in rural India was very different from the technology that all of us use, right? You know they had maybe feature phones and everybody else, you know, had smartphones and so on. But now connectivity, smartphone penetration and so on has increased significantly, right? So in some sense, I think the smartphone and through 4G and so on, connectivity to the cloud, the cloud and the mobile, and with the smartphone is much more accessible, much more prevalent these days, right? But still the problems are there, you know, bandwidth is a problem, you know, they don't work in local language, English works much better than local language. Those constraints are there, but the technology platform has up leveled throughout the country.

So as a result, right, if you take our own work on socio-technical computing, we are doing technologically more sophisticated things now than we did before because more technologically sophisticated things are accessible to a much broader population of the country. That I think is the way things have changed, which is why we are actually now able to do projects like you know, HAMS where you're using driver license testing. Because even, uh, even an RTO in a rural area, right, they have access to smartphones, right? And you know, they are interested to see whether you know driver license testing can be streamlined. So I think that the high tide has lifted the technology everywhere. I think that's one way in which things have changed. Another one where we are now using peer to peer connectivity, this project called BlendNet, where we are actually helping people share media and other bulky resources better. And even that actually, you know the reason why we are doing this because you know smartphone, the desire to view movies, entertainment, it’s very widespread throughout the country, right? So that's actually another example of projects.

And even just this morning I was actually looking at a piece of news where they were talking about, uh, this company Respirer Living Sciences and we're having a collaboration with them to measure air pollution, and they want to monitor pollution and democratize the data, right. I mean this is now such an important problem and but if you look at what is needed to do that right, we have to solve really hard technical problems. Like how do you make sure that the sensors that are sensing these are reliable? If there's a way in which the sensors are calibrated, if it is erroneous, how do you re-calibrate them? But these are hardcore technology problems that I think are important to solve a societal problem like air pollution.

So another way I think things have changed is that maybe, perhaps, previously all our societal scale problems were sort of low tech- that's no longer true. That doesn't mean actually that the tech works as it is, right?

You know, we still work on projects like Karya where we are trying to do data collection and crowdsourcing for low resource Indian languages and that requires actually us to build user interfaces that work with semi-literate and illiterate users and you know, make sure that we are actually able to cater to the multilingual population in the country and so on, right? So the user centered design and the need to design to people on the other side of the digital divide is still important, right?

But you know at the same time the tech tidal wave has also lifted things up, so I think that's sort of the dynamic here, I think.

Sridhar Vedantham: Right, and there's a bit of a conundrum here, right? Because at one point of time it was assumed that technology itself is going to help people's lives become better and so on. And we've obviously seen technology permeate to levels within society that it's never permeated before. Now this brings about questions of digital inclusion and fairness and equitable access to information and to the benefits of technology. So, a couple of questions here. How do we actually ensure things like digital inclusion and fairness? And given very specific, unprecedented situations like the one that we find ourselves now, in the midst of a pandemic, how does this actually impact people or impact society at large?

Sriram Rajamani: I think in spite of the fact that digital technology has permeated right, it is very clear that technology is still very non inclusive, right? That is also, I think true at the same time. And so there is no silver bullet I think to the question that you're asking. I think it's extremely important for us to think about as scientists and technologists, think about underserved populations, underserved communities and see whether the technologies that we build you know are inclusive, whether they are useful, you know. I give an example of what Manohar Swaminathan is doing with his work on accessibility, where he has done quite a bit of study in schools for visually impaired children, thinking about even the curriculum that they have you know, in STEM and computing, computational thinking, I think, for this population. And seeing whether the tools that we have and even the curriculum that we have and the technologies that we have, are they actually reaching you know this demographic and the answer is no.

And then quite a bit of work needs to be done in order to make sure that you know people with vision impairment, children with vision impairment are getting educated in digital technologies and the technology is inclusive. And there's a huge gap there, so his work is particularly inspiring in that sense. And you know, I think problems like awareness and literacy, they are very hard problems to solve. You know you make a smartphone cheaper. You can actually make you know 4G and 5G maybe more available. But you know things like literacy, cognition and understanding of actually what's going on, those take many, many generations to resolve. So I think one has to think about people’s context, you know people preparedness, when thinking about inclusion.

Sridhar Vedantham: Great, so I'm going to be cognizant of your time. I know you've got a bunch of meetings every day all the time. So before we sign off are there any final thoughts?

Sriram Rajamani: Yeah, so I would say that I think that the pandemic in some way has really accelerated digital transformation, right? But at the same time the pandemic has also exacerbated the gap between the rich and the poor. That has also happened. So I would say that, I think this is a very interesting time, as scientists and technologists. On the one hand, actually, science is our, you know, an important hope for us to get out of the pandemic, you know, be it vaccines, you know, be it digital technology to help us communicate and collaborate even when we are at our homes, technology is such an important thing to do. And in order to actually serve, you know, the large number of people we have to build technology at scale. I think that's such an important thing. At the same time, I think, you know, that the virus doesn't discriminate between you know, rich or poor. It doesn't discriminate based on race or gender, and so if we have to actually get out of the pandemic, you know, we have to actually make sure that the solutions you know be it vaccines, they reach everyone. If anything, the pandemic has taught us that you know, unless we serve everyone problems like the pandemic and same thing with climate change, those are not going to be solved. Those are universal problems and that by definition they are inclusive, right? So I think my closing comment would be for technologists to think about technologies in such a way, technology in such a way that it brings people together. You know, have empathy for people in every shape, size and form and make sure that what we build, serves the whole of the world.

Sridhar Vedantham: OK Siram, thank you so much for your time. This has been a fascinating conversation.

Sriram Rajamani: Yeah, thank you Sridhar, and I wish the listeners health and happiness in the rest of the year as well. Thank you.

[Music Ends]