![\"Sriram\u00a0Rajamani](\"https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2020\/01\/Research_Podcast_Sriram_Site_1400x788-1024x576.png\")
<\/p>\n<\/p>\n
Dr. Sriram Rajamani<\/a> is a Distinguished Scientist and the Managing Director of the 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 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 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 Host:\u00a0<\/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 Host: Dr. Sriram\u00a0<\/b>Rajamani<\/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>\u00a0<\/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 Today, Dr.\u00a0<\/b>Rajamani<\/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>ies<\/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>\u00a0<\/b>That and much more on this episode of the Microsoft Research Podcast.<\/b><\/p>\n Host: Sriram\u00a0<\/b>Rajamani<\/b>, welcome to the podcast<\/b>!<\/b><\/p>\n Sriram\u00a0Rajamani:\u00a0Thank you. Excited to be here.<\/p>\n Host: You\u2019re a distinguished scientist and the\u00a0<\/b>M<\/b>anaging\u00a0<\/b>D<\/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>\u00a0in broad strokes<\/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 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 Host: No way!<\/b><\/p>\n 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 Host:\u00a0<\/b>Yeah! S<\/b>o tell me about your personal passion and what drives you? What questions are you asking that you<\/b>\u00a0would<\/b>\u00a0really like to answer? What problems would you really like to solve? Tell me what your heart is for research<\/b>.<\/b><\/p>\n 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 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 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 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>\u00a0both those that you observe and those you create. So what does it take<\/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 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 Host: Wow.<\/b><\/p>\n 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 Host: I have.<\/b><\/p>\n Sriram\u00a0Rajamani:\u00a0as a guest\u00a0on the\u2026<\/p>\n Host: Awesome guy.<\/b><\/p>\n 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 Host: Right.<\/b><\/p>\n 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 Host: Right.<\/b><\/p>\n 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 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>\u00a0and Technology for Emerging Markets<\/b>,<\/b>\u00a0or TEM. Talk briefly about how your lab is contributing\u00a0<\/b>in<\/b>\u00a0each of these areas. We don\u2019t\u00a0<\/b>have<\/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 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 Host: Yeah.<\/b><\/p>\n 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 (music plays)<\/i><\/b><\/p>\n Host: Let\u2019s talk for a minute about why India is the ideal place for disruptive technology and how constraints drive innovation<\/b>.\u00a0<\/b>Ho<\/b>w<\/b>\u00a0are the realities of life in developing areas of the country turning some current assumptions about technology upside down?<\/b><\/p>\n 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 Host: Yeah.<\/b><\/p>\n 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 Host: Right.<\/b><\/p>\n 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 Host: Right.<\/b><\/p>\n Sriram\u00a0Rajamani: Right?<\/p>\n Host: You know you\u2019re harkening back to Ed\u00a0<\/b>Cutrell<\/b>\u00a0who was on the show and I know he did work in\u00a0<\/b>India<\/b>\u2026<\/b><\/p>\n Sriram\u00a0Rajamani: Yes, he used to be in\u00a0our\u00a0TEM group for many years.<\/p>\n Host: Yeah, and some of the stories he told on the podcast he was on<\/b>\u00a0\u2013<\/b>\u00a0I encourage people to go listen to that one\u00a0<\/b>\u2013\u00a0<\/b>because there\u2019s actual stories of things that they thought would work in particular scenarios,\u00a0<\/b>that<\/b>\u00a0they just wildly didn\u2019t<\/b>,<\/b>\u00a0<\/b>but<\/b>\u00a0not for the reasons they thought they wouldn\u2019t, right? It\u2019s like \u2026<\/b><\/p>\n Sriram\u00a0Rajamani: I could tell you a story.<\/p>\n Host: Do, please<\/b>!<\/b>\u00a0I love stories<\/b>!<\/b><\/p>\n 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 Host: Right.<\/b><\/p>\n 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 Host: Interesting.<\/b><\/p>\n 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 Host: You and your colleagues are tackling some, what you call, societal scale issues. Healthcare, education, agriculture, employment, connectivity, transparency<\/b>\u2026<\/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><\/p>\n Sriram\u00a0Rajamani: Yeah, so, I can tell you a few stories.\u00a0I already told you\u2026=<\/p>\n Host: I love stories<\/b>!<\/b><\/p>\n Sriram\u00a0Rajamani: \u2026the illiteracy story.<\/p>\n Host:\u00a0<\/b>Keep going<\/b>!<\/b><\/p>\nRelated:<\/h3>\n
\n
\nTranscript<\/h3>\n
![\"Sriram\u00a0Rajamani](\"https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2020\/01\/Research_Podcast_Sriram_Site_1400x788-960x540.png\")
","byline":"","formattedDate":"January 22, 2020","formattedExcerpt":"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,…","locale":{"slug":"en_us","name":"English","native":"","english":"English"},"_links":{"self":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/posts\/632067"}],"collection":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/users\/39507"}],"replies":[{"embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/comments?post=632067"}],"version-history":[{"count":10,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/posts\/632067\/revisions"}],"predecessor-version":[{"id":886953,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/posts\/632067\/revisions\/886953"}],"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=632067"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/categories?post=632067"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/tags?post=632067"},{"taxonomy":"msr-research-area","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/research-area?post=632067"},{"taxonomy":"msr-region","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-region?post=632067"},{"taxonomy":"msr-event-type","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-event-type?post=632067"},{"taxonomy":"msr-locale","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-locale?post=632067"},{"taxonomy":"msr-post-option","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-post-option?post=632067"},{"taxonomy":"msr-impact-theme","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-impact-theme?post=632067"},{"taxonomy":"msr-promo-type","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-promo-type?post=632067"},{"taxonomy":"msr-podcast-series","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-podcast-series?post=632067"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}