{"id":811936,"date":"2020-01-22T19:05:55","date_gmt":"2020-01-23T03:05:55","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/?post_type=msr-blog-post&#038;p=811936"},"modified":"2022-01-13T19:27:01","modified_gmt":"2022-01-14T03:27:01","slug":"innovating-in-india-with-dr-sriram-rajamani","status":"publish","type":"msr-blog-post","link":"https:\/\/www.microsoft.com\/en-us\/research\/articles\/innovating-in-india-with-dr-sriram-rajamani\/","title":{"rendered":"Microsoft Research Podcast: Innovating in India with Dr. Sriram Rajamani"},"content":{"rendered":"<p><img loading=\"lazy\" decoding=\"async\" class=\"alignnone size-large wp-image-632085\" src=\"https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2020\/01\/Research_Podcast_Sriram_Site_1400x788-1024x576.png\" alt=\"Sriram\u00a0Rajamani on the Microsoft Research Podcast\" width=\"1024\" height=\"576\" srcset=\"https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2020\/01\/Research_Podcast_Sriram_Site_1400x788-1024x576.png 1024w, https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2020\/01\/Research_Podcast_Sriram_Site_1400x788-300x169.png 300w, https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2020\/01\/Research_Podcast_Sriram_Site_1400x788-768x432.png 768w, https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2020\/01\/Research_Podcast_Sriram_Site_1400x788-1066x600.png 1066w, https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2020\/01\/Research_Podcast_Sriram_Site_1400x788-655x368.png 655w, https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2020\/01\/Research_Podcast_Sriram_Site_1400x788-343x193.png 343w, https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2020\/01\/Research_Podcast_Sriram_Site_1400x788-640x360.png 640w, https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2020\/01\/Research_Podcast_Sriram_Site_1400x788-960x540.png 960w, https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2020\/01\/Research_Podcast_Sriram_Site_1400x788-1280x720.png 1280w, https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2020\/01\/Research_Podcast_Sriram_Site_1400x788.png 1400w\" sizes=\"(max-width: 1024px) 100vw, 1024px\" \/><\/p>\n<p><iframe loading=\"lazy\" src=\"https:\/\/player.blubrry.com\/id\/54576994\/\" width=\"100%\" height=\"138px\" frameborder=\"0\" scrolling=\"no\"><\/iframe><\/p>\n<h3>Episode 103 | January 22, 2020<\/h3>\n<p><a href=\"https:\/\/www.microsoft.com\/en-us\/research\/people\/sriram\/\">Dr. Sriram Rajamani<\/a> is a Distinguished Scientist and the Managing Director of the <a href=\"https:\/\/www.microsoft.com\/en-us\/research\/lab\/microsoft-research-india\/\">Microsoft Research lab in Bangalore<\/a>. He\u2019s 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\u2019s also, as you\u2019ll find out shortly, a world-class storyteller!<\/p>\n<p>Today, Dr. Rajamani talks about the unique challenges and opportunities of leading MSR\u2019s 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 \u2018frugal innovation\u2019 may be one key to solving societal scale problems.<\/p>\n<h3>Related:<\/h3>\n<ul type=\"disc\">\n<li><a href=\"https:\/\/www.microsoft.com\/en-us\/research\/podcast\">Microsoft Research Podcast<\/a>: View more podcasts on Microsoft.com<\/li>\n<li><a class=\"msr-external-link glyph-append glyph-append-open-in-new-tab glyph-append-xsmall\" href=\"https:\/\/itunes.apple.com\/us\/podcast\/microsoft-research-a-podcast\/id1318021537?mt=2\">iTunes<\/a>: Subscribe and listen to new podcasts each week on iTunes<\/li>\n<li><a class=\"msr-external-link glyph-append glyph-append-open-in-new-tab glyph-append-xsmall\" href=\"https:\/\/subscribebyemail.com\/www.blubrry.com\/feeds\/microsoftresearch.xml\">Email<\/a>: Subscribe and listen by email<\/li>\n<li><a class=\"msr-external-link glyph-append glyph-append-open-in-new-tab glyph-append-xsmall\" href=\"https:\/\/subscribeonandroid.com\/www.blubrry.com\/feeds\/microsoftresearch.xml\">Android<\/a>: Subscribe and listen on Android<\/li>\n<li><a class=\"msr-external-link glyph-append glyph-append-open-in-new-tab glyph-append-xsmall\" href=\"https:\/\/open.spotify.com\/show\/4ndjUXyL0hH1FXHgwIiTWU\">Spotify<\/a>: Listen on Spotify<\/li>\n<li><a class=\"msr-external-link glyph-append glyph-append-open-in-new-tab glyph-append-xsmall\" href=\"https:\/\/www.blubrry.com\/feeds\/microsoftresearch.xml\">RSS feed<\/a><\/li>\n<li><a class=\"msr-external-link glyph-append glyph-append-open-in-new-tab glyph-append-xsmall\" href=\"https:\/\/note.microsoft.com\/ww-registration-microsoft-research-newsletter-s.html?wt.mc_id=S-webpage_podcast\">Microsoft Research Newsletter<\/a>: Sign up to receive the latest news from Microsoft Research<\/li>\n<\/ul>\n<hr \/>\n<h3>Transcript<\/h3>\n<p>Sriram\u00a0Rajamani:\u00a0I 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\u00a0have\u00a0to think about:\u00a0cost, bandwidth, the diversity of users;\u00a0I think those are extreme in India. Because of that,\u00a0if you build systems that somehow work in those constraints, you are innovating for the world.<\/p>\n<p><b>Host:\u00a0<\/b><b>You\u2019re 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\u2019m your host, Gretchen Huizinga.<\/b><\/p>\n<p><b>Host: Dr. Sriram\u00a0<\/b><b>Rajamani<\/b><b>\u00a0is a Distinguished Scientist and the Managing Director of the Microsoft Research lab in Bangalore. He\u2019s dedicated his career to advancing globally applicable science in the test<\/b><b>\u00a0<\/b><b>bed that is India. He is, by any measure, a world-class researcher and leader. He\u2019s also, as you\u2019ll find out shortly, a world-class storyteller!<\/b><\/p>\n<p><b>Today, Dr.\u00a0<\/b><b>Rajamani<\/b><b>\u00a0talks about the unique challenges and opportunities of leading MSR\u2019s research efforts in India and what it takes to build a robust research ecosystem in a country of huge disparit<\/b><b>ies<\/b><b>. He also dispels some preconceptions about poor and marginalized populations and explains why \u2018frugal innovation\u2019 may be one key to solving societal scale problems.<\/b><b>\u00a0<\/b><b>That and much more on this episode of the Microsoft Research Podcast.<\/b><\/p>\n<p><b>Host: Sriram\u00a0<\/b><b>Rajamani<\/b><b>, welcome to the podcast<\/b><b>!<\/b><\/p>\n<p>Sriram\u00a0Rajamani:\u00a0Thank you. Excited to be here.<\/p>\n<p><b>Host: You\u2019re a distinguished scientist and the\u00a0<\/b><b>M<\/b><b>anaging\u00a0<\/b><b>D<\/b><b>irector of Microsoft Research India and that\u2019s 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<\/b><b>,<\/b><b>\u00a0in broad strokes<\/b><b>,<\/b><b>\u00a0what you do for a living? What does a day in your life look like? What gets you up in the morning?<\/b><\/p>\n<p>Sriram\u00a0Rajamani: Oh, boy. So, I\u2019m a morning person,\u00a0so I\u2019m 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\u2019s my reading queue.\u00a0So my mornings are usually spent reading at home.\u00a0And 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\u2019s the\u00a0most fun part of my job. I am usually home by five and,\u00a0with my family,\u00a0do a\u00a0group yoga class, in the evening.<\/p>\n<p><b>Host: No way!<\/b><\/p>\n<p>Sriram\u00a0Rajamani:\u00a0That\u2019s sort of my break in the evening. And then,\u00a0late evening,\u00a0there\u2019s Redmond calls. So starting like 8 pm, 9 pm for a couple of hours there\u2019s usually conversations with either researchers in Redmond or product groups in Redmond. I do spend a lot of time traveling because,\u00a0as Microsoft Research, we engage\u00a0with\u00a0academia, not only\u00a0in\u00a0India, but throughout the world,\u00a0and I come here three,\u00a0four times a year. So that hopefully gives you a sense for that I do.<\/p>\n<p><b>Host:\u00a0<\/b><b>Yeah! S<\/b><b>o tell me about your personal passion and what drives you? What questions are you asking that you<\/b><b>\u00a0would<\/b><b>\u00a0really like to answer? What problems would you really like to solve? Tell me what your heart is for research<\/b><b>.<\/b><\/p>\n<p>Sriram\u00a0Rajamani: 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,\u00a0and they do what we intend them to do. So that\u2019s my personal passion. But these days, actually, a lot of my time is spent not\u00a0only\u00a0on my own work, but the work of my colleagues, which ranges from,\u00a0you know,\u00a0mathematics, 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\u2019m a research junkie so\u00a0I,\u00a0actually,\u00a0I spend a lot of time learning. That\u2019s what I do. That\u2019s my main passion is learning.<\/p>\n<p><b>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\u2019s kind of fascinating. What\u2019s your particular guiding mission and what were the particular challenges and opportunities for opening a lab in Bangalore?<\/b><\/p>\n<p>Sriram\u00a0Rajamani: The opportunities,\u00a0in a place like India,\u00a0are many. First of all, it\u2019s a country with more than a billion people, a lot of them very young, right, so there\u2019s a tremendous amount of potential for talent and what we can do there.\u00a0It\u2019s a growing economy.\u00a0And to just give you a sense,\u00a0right,\u00a0when I graduated in\u00a0\u201891, 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\u2019s enough economic opportunities there. There\u2019s 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.\u00a0And if you look at actually languages that are spoken by fewer people, still tens of thousands, there will be\u00a0a thousand five hundred.\u00a0Huge disparities in socio-economic conditions. You know, you will find\u00a0extremely rich people, extremely poor people, everything in between. And wide infrastructure variance. I mean, if you go to a city,\u00a0it\u2019ll\u00a0be just like in the US, and if you go to a village\u00a0there\u2019ll\u00a0be,\u00a0like,\u00a0nothing.\u00a0So in terms of actually why we went there, we went there because of talent and the opportunities. In terms of how we converged\u00a0on what we work on there and what\u2019s sort of the unique value that MSR India brings,\u00a0we\u2019ve\u00a0always tried to strike a balance between globally applicable science and being inspired by India as a test bed. So you know, as a talent,\u00a0right,\u00a0India\u2019s\u00a0always had really good mathematics talent. That\u2019s 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\u00a0expertise in systems and machine learning,\u00a0and we also work on socio-economic development, which is a very local thing in India. And we didn\u2019t plan these areas ahead of time. We sort of meandered around and we have converged on these areas over fifteen years.\u00a0We have sort of evolved over time.\u00a0And actually MSR lets lab directors the flexibility to just evolve\u00a0in that story, which is wonderful.<\/p>\n<p><b>Host: As both a scientist yourself and a leader in technology you\u2019re in a unique position to reflect on trends. And I would say<\/b><b>,<\/b><b>\u00a0both those that you observe and those you create. So what does it take<\/b><b>,<\/b><b>\u00a0in your mind, Sriram, to be a leader in technology today, and how are you executing towards that in an age of AI?<\/b><\/p>\n<p>Sriram\u00a0Rajamani:\u00a0Most of\u00a0a scientist\u2019s 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,\u00a0and 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,\u00a0you know,\u00a0during that time,\u00a0if you sort of think about security,\u00a0we always thought\u00a0about\u00a0security of data much like physical security.\u00a0Like\u00a0you store your valuables in a locker and you lock them up and then you do access control. You sort of, you know,\u00a0decide 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\u00a0a very, you know, fine young researcher in our lab,\u00a0his name is\u00a0Saikat\u00a0Guha.\u00a0Around 2008, he started thinking, oh, no, that\u2019s not the right way to think about security in the internet age. We\u00a0have to actually\u00a0think 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\u00a0view, right?\u00a0You know, he was like a lone ranger working on\u00a0that\u00a0for several years and he built tools to actually codify those ideas and many years later,\u00a0when GDPR came,\u00a0he was already ready\u2026<\/p>\n<p><b>Host: Wow.<\/b><\/p>\n<p>Sriram\u00a0Rajamani:\u00a0\u2026with frameworks and so on. And he built a framework called\u00a0DataMap, over the past decade, that was so influential in how Microsoft thinks about GDPR compliance. Another example I would give you is,\u00a0and I know you\u2019ve had\u00a0Manik\u00a0Varma\u2026<\/p>\n<p><b>Host: I have.<\/b><\/p>\n<p>Sriram\u00a0Rajamani:\u00a0as a guest\u00a0on the\u2026<\/p>\n<p><b>Host: Awesome guy.<\/b><\/p>\n<p>Sriram\u00a0Rajamani: \u2026podcast. So he worked on, you know,\u00a0a machine\u00a0learning system called\u00a0Extreme\u00a0Classification, you know.\u00a0So\u00a0I may refresh your listeners to what that is about. You know, today\u00a0in\u00a0machine learning, people think about classifying\u00a0objects or, you know,\u00a0data into\u00a0a\u00a0small number of classes. You know, you\u00a0could\u00a0take a picture and classify it as a cat or a dog, but\u00a0Manik\u00a0Varma thinks about how to classify things\u00a0so that\u00a0the number of categories could be in the order of millions.<\/p>\n<p><b>Host: Right.<\/b><\/p>\n<p>Sriram\u00a0Rajamani: Right? And when he first started doing that, people thought he was crazy. But today, there are many, many applications\u00a0in\u00a0advertisements, in\u00a0recommendations,\u00a0in\u00a0ranking,\u00a0and\u00a0Extreme\u00a0Classification is now a unique sub-area in machine learning that he started.\u00a0Today, if you go to\u00a0NIPS\u00a0or ICML, there\u00a0is actually workshop in\u00a0Extreme\u00a0Classification.<\/p>\n<p><b>Host: Right.<\/b><\/p>\n<p>Sriram\u00a0Rajamani:\u00a0Right. That\u2019s an example of, again, foresight into how the world\u00a0would\u00a0look 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,\u00a0and have a community that works with you to create that future.<\/p>\n<p><b>Host: MSR India focuses on four key areas of technology research and you\u2019ve alluded to them already, but let\u2019s talk about them specifically: algorithms, systems, ML and AI<\/b><b>,<\/b><b>\u00a0and Technology for Emerging Markets<\/b><b>,<\/b><b>\u00a0or TEM. Talk briefly about how your lab is contributing\u00a0<\/b><b>in<\/b><b>\u00a0each of these areas. We don\u2019t\u00a0<\/b><b>have<\/b><b>\u00a0to 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.<\/b><\/p>\n<p>Sriram\u00a0Rajamani: Algorithms is pretty much the foundations of computing, right? That\u2019s the math behind, you know, data science. The math behind cryptography. The math behind everything that we do in computing.\u00a0And we are very fortunate to have amazing, incredible minds that actually work in this space. A lot of machine learning\u00a0actually\u00a0starts out as algorithms.\u00a0Thinking 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\u2019ll be math equations written on a white board. So our algorithms group does a lot of leading edge work that is going to\u00a0only\u00a0see 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\u00a0on, you know, things like topic modeling, which are now incorporated into working tools that are used by many, many people inside the company today.\u00a0That\u2019s an example.\u00a0One\u00a0other\u00a0thing that people work on\u00a0is, you know,\u00a0you may have heard a lot about deep learning?\u00a0And one of the things that is interesting about deep learning is that,\u00a0even though it works in many cases, we don\u2019t quite even understand why it works, what the limitations of that are,\u00a0when it will fail,\u00a0and so,\u00a0you know,\u00a0people in the algorithms group try and dissect and understand why deep learning does what it does,\u00a0what it\u2019s limitations are,\u00a0and understanding what algorithmic tweaks that we need to do to make it even better. And then moving on, you know,\u00a0to machine learning and artificial intelligence, that\u2019s a very wide spectrum. I already spoke a\u00a0little\u00a0bit about\u00a0extreme\u00a0classification, which talks about classifiers in the large.<\/p>\n<p><b>Host: Yeah.<\/b><\/p>\n<p>Sriram\u00a0Rajamani:\u00a0And, you know,\u00a0we also work on, you know,\u00a0machine learning in the small. We work on, you know,\u00a0Edge ML, which is actually machine learning running on very, very small devices. Devices that you could buy for two dollars or five dollars,\u00a0and, you know, how\u00a0do you make machine learning algorithms work on them?\u00a0You 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\u2019s yesterday. Going forward, what happens is that,\u00a0because of deep neural networks,\u00a0the search is actually done in the higher dimensional space\u00a0and this requires entirely new algorithmic thinking\u00a0and, you know, people in our lab,\u00a0they span\u00a0over\u00a0all the way from the algorithms to machine learning so there\u2019s new algorithms that they\u2019ve been designing on how to do this\u00a0nearest\u00a0neighbor\u00a0search, which\u00a0had the potential to transform the way search engines are built.\u00a0And 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.\u00a0How 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\u00a0if\u00a0something fails we get those signals back,\u00a0crashes,\u00a0we get those signals back.\u00a0How can we use machine learning to map those things back to actual code that people write, so that when something fails we can point\u00a0out,\u00a0hey, this fails because this line of code is actually not working right.\u00a0We 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?\u00a0And\u00a0then\u00a0the 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?\u00a0So those are the kinds of things that we do.<\/p>\n<p><b><i>(music plays)<\/i><\/b><\/p>\n<p><b>Host: Let\u2019s talk for a minute about why India is the ideal place for disruptive technology and how constraints drive innovation<\/b><b>.\u00a0<\/b><b>Ho<\/b><b>w<\/b><b>\u00a0are the realities of life in developing areas of the country turning some current assumptions about technology upside down?<\/b><\/p>\n<p>Sriram\u00a0Rajamani: 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,\u00a0you know,\u00a0there might not even be electric power.\u00a0I think the number one thing that strikes you,\u00a0when you try to build technology in India,\u00a0is 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\u00a0have\u00a0to think about:\u00a0cost, bandwidth, the diversity of users. I already mentioned the number of\u00a0spoken\u00a0languages and so on.\u00a0I 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\u2019ve heard is actually that if you make something work in India, it\u2019ll work anywhere! That\u2019s actually something I\u2019ve heard.\u00a0You know, and it\u2019s so true, right? If you sort of go to a rural area and open up your mobile and press \u2018download\u2019 on something, it just spins forever.<\/p>\n<p><b>Host: Yeah.<\/b><\/p>\n<p>Sriram\u00a0Rajamani:\u00a0Right? How do you build a system that\u00a0supports those users\u00a0as well as users in the city?\u00a0That,\u00a0I think,\u00a0is a tremendous opportunity. So in our lab we actually have thought a lot about this. One of the terms that actually describes\u00a0best what we do is called\u00a0\u2018frugal innovation,\u2019\u00a0innovation that actually thinks about cost, essentially as its core, because if something is not low cost it\u2019s just not going to fly, right?\u00a0And\u00a0the thinking about technology as an amplifier of human ability. I think, so\u00a0technology should not replace people,\u00a0because,\u00a0you know, there\u2019s 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.<\/p>\n<p><b>Host: Right.<\/b><\/p>\n<p>Sriram\u00a0Rajamani: 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.\u00a0You know, distinguishing between their needs and wants.\u00a0Most of us,\u00a0actually,\u00a0in the\u00a0west think about, you know,\u00a0when we work with poor people,\u00a0we think about health, education, right? Those should be their needs, right? But in reality, if you study them,\u00a0they have a lot of wants. You know, they want entertainment. They want employment, right?\u00a0But thinking about poor not as just consumers of information, but producers of information. They have very many interesting things to say.\u00a0Thinking about the lived in experience of the two billion people that are not yet part of the digital economy\u00a0because\u00a0of many, many reasons.\u00a0Illiteracy. Thinking about illiteracy as a cognitive deficit. And thinking\u00a0about,\u00a0you could give them the best smart phone,\u00a0you could\u00a0give them the best 3G\/4G connectivity, but if they don\u2019t have textual literacy,\u00a0how are\u00a0you\u00a0going to connect them and include them? I think the final thing I\u00a0would\u00a0say is that when you design technologies, you know, to serve this kind of community, being completely honest to yourself\u00a0that 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?<\/p>\n<p><b>Host: Right.<\/b><\/p>\n<p>Sriram\u00a0Rajamani: Right?<\/p>\n<p><b>Host: You know you\u2019re harkening back to Ed\u00a0<\/b><b>Cutrell<\/b><b>\u00a0who was on the show and I know he did work in\u00a0<\/b><b>India<\/b><b>\u2026<\/b><\/p>\n<p>Sriram\u00a0Rajamani: Yes, he used to be in\u00a0our\u00a0TEM group for many years.<\/p>\n<p><b>Host: Yeah, and some of the stories he told on the podcast he was on<\/b><b>\u00a0\u2013<\/b><b>\u00a0I encourage people to go listen to that one\u00a0<\/b><b>\u2013\u00a0<\/b><b>because there\u2019s actual stories of things that they thought would work in particular scenarios,\u00a0<\/b><b>that<\/b><b>\u00a0they just wildly didn\u2019t<\/b><b>,<\/b><b>\u00a0<\/b><b>but<\/b><b>\u00a0not for the reasons they thought they wouldn\u2019t, right? It\u2019s like \u2026<\/b><\/p>\n<p>Sriram\u00a0Rajamani: I could tell you a story.<\/p>\n<p><b>Host: Do, please<\/b><b>!<\/b><b>\u00a0I love stories<\/b><b>!<\/b><\/p>\n<p>Sriram\u00a0Rajamani: Yeah, so, we have a researcher. Her name is\u00a0Indrani\u00a0Medhi-Thies. She is one of the world\u2019s leading experts on\u00a0illiteracy. So, you know, she and a bunch of others wanted to build a job website for low income people.\u00a0Sort of like a monster.com\u2026<\/p>\n<p><b>Host: Right.<\/b><\/p>\n<p>Sriram\u00a0Rajamani:\u00a0\u2026or something for like cooks or drivers and, you know, low income labor.\u00a0And so they built it with only pictures, right? Because these people, you know,\u00a0wouldn\u2019t be able to\u00a0read text so\u00a0they built the whole interface using pictures. And I remember, you know,\u00a0there\u2019s a slum near our lab,\u00a0so\u00a0they wanted to do a pilot in\u00a0the\u00a0slum, so there was\u00a0a lot of\u00a0discussion in the lab about how to put a computer there\u00a0so that\u00a0the computer\u00a0wouldn\u2019t\u00a0be stolen!\u00a0And so\u00a0that\u00a0actually you can access it, but,\u00a0you know,\u00a0you can\u2019t walk away with it.\u00a0And then everything, you know, you could apply using pictures. You\u00a0know, you\u00a0could actually look at the job listings and it did all of that, right? And after that, they deployed it and the usage was zero.\u00a0It was there and people were curious about it, but nobody used it!\u00a0And what occurred to\u00a0Indrani\u00a0was that the reason is actually they have no conception of what this thing would even do!\u00a0And so what they did was, you know, they enacted\u00a0something like a soap opera in the lab,\u00a0with actors from the lab.\u00a0There\u2019s 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\u00a0in\u00a0this\u00a0site and then there\u2019s a woman who comes and accesses this computer in the slum and she clicks on this and\u00a0she gets introduced and they meet and she gets the job.\u00a0This is now being run as a screen saver in that computer and then the usage of the thing skyrocketed!<\/p>\n<p><b>Host: Interesting.<\/b><\/p>\n<p>Sriram\u00a0Rajamani: Right? So\u00a0Indrani\u2019s\u00a0main conclusion,\u00a0right,\u00a0is that illiteracy is not just about textual literacy. It\u2019s about lack of context and awareness.\u00a0And unless you actually put yourself in the shoes of a person who has never seen something like this before,\u00a0then you\u2019re not going to fix this by, you know,\u00a0just pictures, right?\u00a0So that\u2019s an example of things that you think that would work, but wouldn\u2019t work.<\/p>\n<p><b>Host: You and your colleagues are tackling some, what you call, societal scale issues. Healthcare, education, agriculture, employment, connectivity, transparency<\/b><b>\u2026<\/b><b>\u00a0we\u2019ve 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<\/b><b>.<\/b><\/p>\n<p>Sriram\u00a0Rajamani: Yeah, so, I can tell you a few stories.\u00a0I already told you\u2026=<\/p>\n<p><b>Host: I love stories<\/b><b>!<\/b><\/p>\n<p>Sriram\u00a0Rajamani: \u2026the illiteracy story.<\/p>\n<p><b>Host:\u00a0<\/b><b>Keep going<\/b><b>!<\/b><\/p>\n<p>Sriram\u00a0Rajamani: Yeah!\u00a0So one project where we have made a lot of traction is a project called <a href=\"https:\/\/www.microsoft.com\/en-us\/research\/project\/99dots\/\">99DOTS<\/a>,\u00a0which is, you know,\u00a0around\u00a0\u00a0technologies for tuberculosis medication adherence.\u00a0This is a project that was initiated by Bill\u00a0Thies\u00a0and Andrew Cross.\u00a0So the context for this is that TB is a curable disease, but you\u00a0have\u00a0to take medication for six\u00a0months. And if someone falls\u00a0out\u00a0of 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\u00a0to\u00a0ensure\u00a0that\u00a0they 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\u00a0gather\u00a0information. Then the healthcare worker could spend all their time on people that are actually not taking medication.\u00a0So they designed a sensing system, which\u00a0they\u00a0iterated many, many\u00a0times, but what finally works is\u00a0actually\u00a0they work with pill manufacturers, and they designed a new paper strip so that actually when they dispense the pill,\u00a0it 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,\u00a0oh, this person is now taking a drug.\u00a0So the computer knows when the calls are coming,\u00a0and when the calls don\u2019t come,\u00a0there\u2019s like a red bar saying, this person hasn\u2019t 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.<\/p>\n<p><b>Host: Right.<\/b><\/p>\n<p>Sriram\u00a0Rajamani: You know, the Gates Foundation,\u00a0USAid, they wanted to fund it. So we spun it off into a separate start-up company called\u00a0Everwell, and it\u2019s walking distance from our office. They employ about twenty people and they\u2019ve enrolled\u00a0more than\u00a0two hundred thousand patients.<\/p>\n<p><b>Host: Wow.<\/b><\/p>\n<p>Sriram\u00a0Rajamani:\u00a0Some 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.\u00a0And so we have\u00a0a research project called HAMS\u00a0where, what we\u2019re doing is,\u00a0just using a smart phone,\u00a0we 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\u2026\u00a0you can imagine how this technology could be used to monitor fleets, how to\u00a0make\u00a0driving safe and a very interesting application of this is in automated driver licensing. So today, if you go to\u00a0Dehradun,\u00a0as of two months,\u00a0if you go\u00a0do a driving test, there\u2019s no instructor.<\/p>\n<p><b>Host: It\u2019s a phone.<\/b><\/p>\n<p>Sriram\u00a0Rajamani: It\u2019s a phone.\u00a0And then you drive,\u00a0and\u00a0then,\u00a0an automatic print\u00a0out gets printed\u00a0out saying\u00a0these are the things you did right, these are the things you didn\u2019t do right,\u00a0and you passed or failed.<\/p>\n<p><b>Host: Oh, interesting.<\/b><\/p>\n<p>Sriram\u00a0Rajamani:\u00a0So let me add a few more, right?<\/p>\n<p><b>Host: Yeah.<\/b><\/p>\n<p>Sriram\u00a0Rajamani: I mentioned HAMS. You know,\u00a0like,\u00a0you know,\u00a0BlendNet\u00a0is another project because connectivity infrastructure is such a big issue. Now if you go\u00a0to, you know,\u00a0rural areas, right,\u00a0you can get text messages by, but if you try and download a video,\u00a0you\u2019ll see the wheel spinning forever and you will\u00a0be\u00a0never able to download something.<\/p>\n<p><b>Host: Right.<\/b><\/p>\n<p>Sriram\u00a0Rajamani: So\u00a0BlendNet\u00a0is a very interesting idea where most of the popular videos and other bulk things you want to download, actually other people want them too.<\/p>\n<p><b>Host: Yeah.<\/b><\/p>\n<p>Sriram\u00a0Rajamani: Odds are that somebody else will have it. So\u00a0BlendNet\u00a0is what is called a\u00a0Cloud Connected Content\u00a0Distribution\u00a0Network where,\u00a0if 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\u00a0actually\u00a0stores who has what video. The\u00a0actual\u00a0video might come from you.<\/p>\n<p><b>Host: Right.<\/b><\/p>\n<p>Sriram\u00a0Rajamani: 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\u00a0WiFi, my phone turning on\u2026<\/p>\n<p><b>Host:\u00a0<\/b><b>And p<\/b><b>eer-to-peer.<\/b><\/p>\n<p>Sriram\u00a0Rajamani:\u00a0\u2026and peer-to-peer, right?\u00a0And using that.<\/p>\n<p><b>Host: So fascinating. You know, just going back to your Bollywood movie download, those are four-hour productions<\/b><b>\u2026<\/b><\/p>\n<p>Sriram\u00a0Rajamani:\u00a0Absolutely!<\/p>\n<p><b>Host: \u2026<\/b><b>that encompass every single human emotion\u2026 and dancing!<\/b><\/p>\n<p>Sriram\u00a0Rajamani: At the same time!<\/p>\n<p><b>Host: You want your money\u2019s worth!<\/b><\/p>\n<p>Sriram\u00a0Rajamani: Absolutely!<\/p>\n<p><b>Host: Well several trends in technology have actually broadened the scope of the problems that we\u00a0<\/b><b><i>could<\/i><\/b><b>\u00a0solve 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.<\/b><\/p>\n<p>Sriram\u00a0Rajamani: Yeah, I think this is a very important question. Maybe I\u2019ll, again\u00a0actually, 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,\u00a0you know,\u00a0today there are these very small devices and they are primarily used as sensors\u00a0and their capability is to just\u00a0transmit\u00a0information to the cloud, and the assumption that they will work only when there is cloud connectivity. But\u00a0the\u00a0Edge ML\u2019s hypothesis is that,\u00a0what if you could actually do machine learning there?\u00a0But now, it\u2019s a very difficult question. First of all, you have to start with the math to figure out can we actually do it?\u00a0That\u2019s where the algorithms people come in. And then,\u00a0after\u00a0the algorithms people figure out that\u00a0actually\u00a0you can do it, then you need the machine learning people to actually design those algorithms.\u00a0We need\u00a0systems 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.\u00a0You know, when you imagine the future,\u00a0right,\u00a0you have to think about what the algorithms are, what the systems are going to look like,\u00a0and,\u00a0actually,\u00a0how people are going to interact with it.<\/p>\n<p><b>Host: All right, let\u2019s talk about talent. You alluded to that at the beginning.\u00a0<\/b><b>Y<\/b><b>ou\u2019re what we call in the United States a 4A high school. You\u2019ve got a lot of kids\u2026<\/b><\/p>\n<p>Sriram\u00a0Rajamani: Yeah.<\/p>\n<p><b>Host: \u2026to choose for your football team.<\/b><\/p>\n<p>Sriram\u00a0Rajamani: Yes.<\/p>\n<p><b>Host: So with billions, one of your problems might\u00a0<\/b><b>not\u00a0<\/b><b>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\u2019s MSR India\u2019s value proposition to get the best and brightest AI talent these days?<\/b><\/p>\n<p>Sriram\u00a0Rajamani: So India is a pretty interesting place from a talent perspective. You know, we have a really strong undergraduate population,\u00a0but our graduate program,\u00a0you know,\u00a0still 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.\u00a0And that\u2019s our hiring opportunity, right?\u00a0So we recruit globally, you know, from people,\u00a0perhaps of Indian origin,\u00a0and there are people like Bill\u00a0Thies\u00a0and Andrew Cross\u00a0who are, you know,\u00a0not 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,\u00a0even if a small fraction of Indians living worldwide want to come back,\u00a0right,\u00a0a 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,\u00a0undergrads, we actually work with undergrads in India. We have a program called the Research Fellow Program where it\u2019s sort of a pre-doctoral program where\u00a0we 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\u00a0West, you know, typically in Europe or\u00a0in\u00a0the US. And, you know, in\u00a0the\u00a0fifteen years\u00a0we have been\u00a0running this program, I think\u00a0we\u00a0would have graduated maybe five hundred such research fellows. Many of them have now finished PhDs and they\u2019ve come back.\u00a0So, you know,\u00a0we spend a lot of time nurturing young talent, you know, because we play the long game.\u00a0That\u2019s the way we work with undergrads. And in terms of value proposition, right,\u00a0there 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,\u00a0actually,\u00a0you combine that with locally relevant work, like Technologies for\u00a0Emerging Markets, where you connect\u00a0with\u00a0the community, think\u00a0about\u00a0India as a test bed,\u00a0and you put those both together and then you get a different kind of energy.\u00a0And that\u2019s what MSR India is.<\/p>\n<p><b>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\u2019re involved in and how they\u2019re bearing fruit.<\/b><\/p>\n<p>Sriram\u00a0Rajamani:\u00a0Yeah, so\u00a0I already mentioned about interdisciplinary collaboration in the lab and I think that\u2019s very central to what we do, but in India, the other thing that\u2019s very important for our lab is collaboration with our ecosystem, which is the academic ecosystem. It\u2019s quite important because the graduate program is still not quite strong. So many of our staff are adjunct faculty in Indian universities,\u00a0so many of us co-teach courses, we co-supervise PhD students, and that\u2019s 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\u00a0Manik\u00a0Varma.\u00a0Manik\u00a0Varma recently was awarded the SSB prize. It\u2019s one of the most prestigious awards in interdisciplinary science.\u00a0Also, right, there is an Indian National Academy\u00a0of Engineering. So we have three fellows from INAE in our lab. I\u2019m one of them, right?\u00a0And\u00a0we have a MacArthur Prize winner. We have a Knuth Prize winner, and so on.\u00a0So, all of these, right,\u00a0are 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\u00a0energy\u00a0of\u00a0the academic community. So that\u2019s actually super important in a place like India.<\/p>\n<p><b>Host: Talk a little bit about the research ecosystem there and some of the work that you\u2019re doing to build community and train people \u2013 you\u2019ve alluded to the Research Fellows Program, but there are other things you\u2019re doing, sort of broader spectrum. Talk a little bit about that.<\/b><\/p>\n<p>Sriram\u00a0Rajamani: So\u00a0one of the things that we\u2019re 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\u2019t have the ability to go to conferences. So if they can\u2019t go to a conference, we try and bring the conference to India!\u00a0So that\u2019s 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\u00a0series\u00a0called Mysore Park\u00a0Series where, you know,\u00a0we 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,\u00a0because, you know, you have to actually get people to talk to each other and we spend a lot of time and energy\u00a0creating those kinds of conversations,\u00a0nurturing those kinds of conversations,\u00a0and the community is very welcoming of us doing that. That\u2019s one of the reasons why people join our lab. When they join our lab, they\u2019re not in a bubble. They\u2019re actually connected to an environment and connected to the ecosystem around us.<\/p>\n<p><b><i>(music plays)<\/i><\/b><\/p>\n<p><b>Host: We\u2019ve 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?<\/b><\/p>\n<p>Sriram\u00a0Rajamani: There\u00a0are\u00a0some people who believe that AI will become like the Schwarzenegger Terminator and come back and kill all of us. I, for one, don\u2019t believe that. I don\u2019t believe that. I\u00a0know\u00a0we are very far away from that. But what\u00a0worries\u00a0me more is not the fact that, you know,\u00a0AI will\u00a0be, you know,\u00a0all powerful and conquer us, but, you know,\u00a0I\u2019m a software\u00a0reliability\u00a0person, I\u2019m a systems person,\u00a0and I actually want systems to work well. My worry is more that, in our enthusiasm\u00a0as\u00a0technologists, we overestimate what AI can do and deploy it before it is ready. That worries me more than,\u00a0you know,\u00a0AI conquering us. AI is, of course, trained by data. And if the data is not representative,\u00a0it\u2019s going to cause huge amounts of bias, and it\u2019s 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,\u00a0not only in terms of research, but I also think about it in terms of investment, now\u00a0that I\u2019m in a lab director position.\u00a0To 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\u00a0an\u00a0illiterate, right, that is going to open doors. You know, if you can\u2019t\u00a0read and write, but if you can speak,\u00a0and the computer can understand you,\u00a0it\u2019s 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\u2026 you know, those are the markets where the money is.<\/p>\n<p><b>Host: Right.<\/b><\/p>\n<p>Sriram\u00a0Rajamani: And that\u2019s where,\u00a0actually,\u00a0people are investing more and more to make, you know,\u00a0your speech\u00a0assistants\u00a0understand, uh, you know, these kinds of languages. But what about\u00a0the hundred and fifty\u00a0languages? What about the\u00a0one thousand five hundred\u00a0languages? What about the tribal languages that are spoken by\u00a0ten thousand\u00a0people? And all of them are illiterate, right? So are we doing enough investment to include\u00a0them\u00a0in this AI driven economy? And so that disparity, I think, is something that I\u00a0worry\u00a0about. I think it\u2019s extremely important to think about entrepreneurship, right? Because, you know, marginalized people, poor people, they want to live better.<\/p>\n<p><b>Host: Right.<\/b><\/p>\n<p>Sriram\u00a0Rajamani: 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\u2019t just donate money to them, but you sort of enable them to be successful\u00a0at\u00a0creating businesses and then creating economic value, which will then lead to an ROI. But the real difficulty in these kinds of\u00a0things, right, even if you do them,\u00a0they are all going to be in the knee of the hockey stick. It\u2019s 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\u2019s a very difficult thing to do.<\/p>\n<p><b>Host:\u00a0<\/b><b>It\u2019s story time and I would love to hear yours. So even though we\u2019ve been telling stories pretty well the whole podcast, let\u2019s get a personal story in here. Tell us a little bit about your history and where you\u2019ve studied<\/b><b>, w<\/b><b>here you\u2019ve worked<\/b><b>, w<\/b><b>hat got you\u00a0<\/b><b>started along your path<\/b><b>,<\/b><b>\u00a0and how you ended up at MSR in your leadership position today<\/b><b>.<\/b><\/p>\n<p>Sriram\u00a0Rajamani: So I did my, you know,\u00a0undergrad in India,\u00a0and like most of my colleagues, I came here for graduate school. I first did my master\u2019s at UVA,\u00a0at\u00a0University of Virginia, and I thought that maybe I wouldn\u2019t 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\u00a0\u2013\u00a0I used to work in formal verification\u00a0for\u00a0hardware circuits\u00a0\u2013 and, you know,\u00a0around the time I graduated, you know,\u00a0I met Jim\u00a0Lattis\u00a0and Amitabh Srivastava. You know,\u00a0Amitabh 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?\u00a0I found it very intriguing.<\/p>\n<p><b>Host: Yeah.<\/b><\/p>\n<p>Sriram\u00a0Rajamani: So I came here\u00a0with that, you know,\u00a0hook 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,\u00a0together with\u00a0compiler-style\u00a0stuff\u00a0that\u00a0the software people do to really think about how to formally validate software.\u00a0Mostly 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,\u00a0actually,\u00a0in 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\u2019s what runs. And,\u00a0you know,\u00a0methodology 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\u00a0cloud where you can actually guarantee that hackers can\u2019t have access to your data.\u00a0So in my own story, I went to MSR India when I was in my mid-thirties\u00a0and three years ago I became a lab director,\u00a0so I\u2019ve had the fortune of being an individual contributor, a researcher, a group manager and now lab director.\u00a0So that\u2019s been my journey.<\/p>\n<p><b>Host: What\u2019s one thing that people might not know about you that may have influenced you to be a researcher or a leader in tech?<\/b><\/p>\n<p>Sriram\u00a0Rajamani: I think probably most people don\u2019t know that I\u2019m 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\u2019m not that old, right? But\u2026<\/p>\n<p><b>Host: It\u2019s true. I\u2019m looking at him. He\u2019s not that old.<\/b><\/p>\n<p>Sriram\u00a0Rajamani: But I grew up in villages in which there\u2019s no electric power. There was no cooking gas,\u00a0so my mom used to cook with charcoal and firewood.<\/p>\n<p><b>Host: Yeah.<\/b><\/p>\n<p>Sriram\u00a0Rajamani: So I have that kind of upbringing,\u00a0and I think that influences me in many, many ways. I\u2019m the first person from my family to ever leave my country, and it\u2019s sort of full-circle for me to be from\u00a0that\u00a0environment, go\u00a0study here,\u00a0and go back and, you know,\u00a0be 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.<\/p>\n<p><b>Host: As we close, I want to give you the last word, Sriram. You\u2019ve 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<\/b><b>,<\/b><b>\u00a0in the long run<\/b><b>,<\/b><b>\u00a0they shouldn\u2019t be afraid of the long run?<\/b><\/p>\n<p>Sriram\u00a0Rajamani: If you want to do science that changes the world,\u00a0you need to give time. It\u2019s extremely important to do that, because to make any research mark,\u00a0it\u2019s going to take many, many years, because you\u2019ve got to try,\u00a0many 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\u2019s hard to actually sustain the energy to run the marathon. The other advice I would give is to not be lonely.\u00a0Not do it alone.\u00a0Build a community of colleagues to collaborate with you. Pick people that have quite different skills from you to collaborate so\u00a0that\u00a0you can actually learn from others. You teach what you know,\u00a0you actually learn from others. Research is very much a social process.\u00a0That\u2019s another thing that I would encourage.\u00a0And the other thing I would encourage is, you know, think about problems that many, many people care about. Real world problems,\u00a0that if you actually solved them,\u00a0it 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\u2019s 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.<\/p>\n<p><b>Host: Sriram\u00a0<\/b><b>Rajamani<\/b><b>,\u00a0<\/b><b>I\u00a0<\/b><b>thank you for coming all the way from Bangalore just to see me!<\/b><\/p>\n<p>Sriram\u00a0Rajamani: Gretchen, you know, I\u2019m so happy that you spent the time thinking about what a lab is,\u00a0and\u00a0doing this podcast. And also thank you for the opportunity to share the story with your audience. The gratitude is mutual.<\/p>\n<p><b><i>(music plays)<\/i><\/b><\/p>\n<p><b><i>To learn more about Dr. Sriram\u00a0<\/i><\/b><b><i>Rajamani<\/i><\/b><b><i>,<\/i><\/b><b><i>\u00a0and the latest innovations out of MSR\u2019s lab in India, visit <a href=\"https:\/\/www.microsoft.com\/en-us\/research\/\">Microsoft.com\/research<\/a><\/i><\/b><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Episode 103 | January 22, 2020 Dr. Sriram Rajamani is a Distinguished Scientist and the Managing Director of the Microsoft Research lab in Bangalore. He\u2019s 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\u2019s also, as you\u2019ll find out [&hellip;]<\/p>\n","protected":false},"author":39507,"featured_media":632085,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"msr-content-parent":199562,"footnotes":""},"research-area":[],"msr-locale":[268875],"class_list":["post-811936","msr-blog-post","type-msr-blog-post","status-publish","has-post-thumbnail","hentry","msr-locale-en_us"],"msr_assoc_parent":{"id":199562,"type":"lab"},"_links":{"self":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-blog-post\/811936"}],"collection":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-blog-post"}],"about":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/types\/msr-blog-post"}],"author":[{"embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/users\/39507"}],"version-history":[{"count":5,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-blog-post\/811936\/revisions"}],"predecessor-version":[{"id":811954,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-blog-post\/811936\/revisions\/811954"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/media\/632085"}],"wp:attachment":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/media?parent=811936"}],"wp:term":[{"taxonomy":"msr-research-area","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/research-area?post=811936"},{"taxonomy":"msr-locale","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-locale?post=811936"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}