{"id":464682,"date":"2018-02-07T07:46:08","date_gmt":"2018-02-07T15:46:08","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/?p=464682"},"modified":"2018-05-23T14:48:36","modified_gmt":"2018-05-23T21:48:36","slug":"getting-linked-data-science-dr-igor-perisic","status":"publish","type":"post","link":"https:\/\/www.microsoft.com\/en-us\/research\/podcast\/getting-linked-data-science-dr-igor-perisic\/","title":{"rendered":"Getting LinkedIn to Data Science with Dr. Igor Perisic"},"content":{"rendered":"<div id=\"attachment_464832\" style=\"width: 1010px\" class=\"wp-caption aligncenter\"><a class=\"msr-external-link glyph-append glyph-append-open-in-new-tab glyph-append-xsmall\" rel=\"noopener noreferrer\" target=\"_blank\" href=\"https:\/\/www.blubrry.com\/microsoftresearch\/31215424\/011-getting-linked-in-to-data-science-with-dr-igor-perisic\/\"><img loading=\"lazy\" decoding=\"async\" aria-describedby=\"caption-attachment-464832\" class=\"wp-image-464832 size-full\" src=\"https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2018\/02\/MicrosoftResearch_Podcast_IgorPerisic_MSR-BlogHeader_1000x400.jpg\" alt=\"\" width=\"1000\" height=\"400\" srcset=\"https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2018\/02\/MicrosoftResearch_Podcast_IgorPerisic_MSR-BlogHeader_1000x400.jpg 1000w, https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2018\/02\/MicrosoftResearch_Podcast_IgorPerisic_MSR-BlogHeader_1000x400-300x120.jpg 300w, https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2018\/02\/MicrosoftResearch_Podcast_IgorPerisic_MSR-BlogHeader_1000x400-768x307.jpg 768w\" sizes=\"auto, (max-width: 1000px) 100vw, 1000px\" \/><p id=\"caption-attachment-464832\" class=\"wp-caption-text\"><span class=\"sr-only\"> (opens in new tab)<\/span><\/a> Dr. Igor Perisic &#8211; Chief Data Officer<\/p><\/div>\n<p><b>Episode 11, February 7, 2018<\/b><\/p>\n<p>Big data is a big deal, and if you follow the popular technical press, you\u2019ll have heard all the metaphors: data is the new oil, the new bacon, the new currency, the new electricity. It\u2019s even been called the new black. While data may not actually be any of these things, we can say this: in today\u2019s networked world, data is increasingly valuable, and it is essential to research, both basic and applied.<\/p>\n<p>Today, we welcome a special guest to the podcast. <a class=\"msr-external-link glyph-append glyph-append-open-in-new-tab glyph-append-xsmall\" rel=\"noopener noreferrer\" target=\"_blank\" href=\"https:\/\/www.linkedin.com\/in\/igorperisic\/\">Dr. Igor Perisic<span class=\"sr-only\"> (opens in new tab)<\/span><\/a> is the Vice President of Engineering and Chief Data Officer at LinkedIn, the social network for business and employment. On this episode, Dr. Perisic talks about the key attributes of a data scientist, how <a href=\"https:\/\/www.microsoft.com\/en-us\/research\/research-area\/artificial-intelligence\/\">AI and machine learning<\/a> are helping personalize member experiences, why we should all be big open source fans, and how LinkedIn is partnering with other researchers through their innovative Economic Graph program to \u201ccreate economic opportunity for every member of the global workforce.\u201d<\/p>\n<p><b>Relate<\/b><b>d:<\/b><\/p>\n<ul type=\"disc\">\n<li><a href=\"https:\/\/www.microsoft.com\/en-us\/research\/podcast\">Microsoft Research Podcast<\/a>: Visit our podcast page on Microsoft.com<\/li>\n<li><a class=\"msr-external-link glyph-append glyph-append-open-in-new-tab glyph-append-xsmall\" rel=\"noopener noreferrer\" target=\"_blank\" href=\"https:\/\/itunes.apple.com\/us\/podcast\/microsoft-research-a-podcast\/id1318021537?mt=2\">iTunes<span class=\"sr-only\"> (opens in new tab)<\/span><\/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\" rel=\"noopener noreferrer\" target=\"_blank\" href=\"https:\/\/subscribebyemail.com\/www.blubrry.com\/feeds\/microsoftresearch.xml\">Email<span class=\"sr-only\"> (opens in new tab)<\/span><\/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\" rel=\"noopener noreferrer\" target=\"_blank\" href=\"https:\/\/subscribeonandroid.com\/www.blubrry.com\/feeds\/microsoftresearch.xml\">Android<span class=\"sr-only\"> (opens in new tab)<\/span><\/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\" rel=\"noopener noreferrer\" target=\"_blank\" href=\"https:\/\/open.spotify.com\/show\/4ndjUXyL0hH1FXHgwIiTWU\">Spotify<span class=\"sr-only\"> (opens in new tab)<\/span><\/a>: Listen on Spotify<\/li>\n<li><a class=\"msr-external-link glyph-append glyph-append-open-in-new-tab glyph-append-xsmall\" rel=\"noopener noreferrer\" target=\"_blank\" href=\"https:\/\/www.blubrry.com\/feeds\/microsoftresearch.xml\">RSS feed<span class=\"sr-only\"> (opens in new tab)<\/span><\/a><\/li>\n<li><a class=\"msr-external-link glyph-append glyph-append-open-in-new-tab glyph-append-xsmall\" rel=\"noopener noreferrer\" target=\"_blank\" href=\"https:\/\/github.com\/linkedin\">LinkedIn on GitHub<span class=\"sr-only\"> (opens in new tab)<\/span><\/a><\/li>\n<li><a class=\"msr-external-link glyph-append glyph-append-open-in-new-tab glyph-append-xsmall\" rel=\"noopener noreferrer\" target=\"_blank\" href=\"https:\/\/www.linkedin.com\/pulse\/making-hard-choices-quest-ethics-machine-learning-igor-perisic\/\">Making Hard Choices: The Quest for Ethics in Machine Learning<span class=\"sr-only\"> (opens in new tab)<\/span><\/a><\/li>\n<li><a class=\"msr-external-link glyph-append glyph-append-open-in-new-tab glyph-append-xsmall\" rel=\"noopener noreferrer\" target=\"_blank\" href=\"https:\/\/engineering.linkedin.com\/blog\/topic\/economic-graph-challenge\">The Economic Graph Research Program<span class=\"sr-only\"> (opens in new tab)<\/span><\/a><\/li>\n<\/ul>\n<hr \/>\n<p><strong>Transcript<\/strong><\/p>\n<p>Igor Perisic: Fundamentally, at the core of everything, you want an interaction to be very natural. Whether the design is superb, and it fits exactly the way that you would anticipate it, it just feels natural. And that\u2019s exactly where our field comes into play. How do you make that thing natural? It\u2019s not just a design perspective, but it\u2019s also, what is the content that you\u2019re showing? How are you going there? So, it\u2019s transforming that experience, instead of just making it better.<\/p>\n<p><strong>Host: 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.<\/strong><\/p>\n<p><strong>Big data is a big deal, and if you follow the popular technical press, you\u2019ll have heard all the metaphors: data is the new oil, the new bacon, the new currency, the new electricity. It\u2019s even been called the new black. While data may not actually be any of these things, we can say this: in today\u2019s networked world, data is increasingly valuable, and it is essential to research, both basic and applied.<\/strong><\/p>\n<p><strong>Today, we welcome a special guest to the podcast. Dr. Igor Perisic is the Vice President of Engineering and Chief Data Officer at LinkedIn, the social network for business and employment. Today, Dr. Perisic talks about the key attributes of a data scientist, how AI and machine learning are helping personalize member experiences, why we should all be big open source fans, and how LinkedIn is partnering with other researchers through their innovative Economic Graph program to \u201ccreate economic opportunity for every member of the global workforce.\u201d That and much more on this episode of the Microsoft Research Podcast.<\/strong><\/p>\n<p><strong>Host: Igor Perisic, it\u2019s great to have you here on the podcast joining us via Skype from Mountainview. Welcome.<\/strong><\/p>\n<p>Igor Perisic: Thank you very much.<\/p>\n<p><strong>Host: Yeah. Igor, you\u2019re the VP of Engineering at LinkedIn, and you\u2019re also the Chief Data Officer. So, in broad strokes, give us an overview of what you do, and define your responsibilities in each role.<\/strong><\/p>\n<p>Igor Perisic: So, I\u2019m an engineer. I build things. I build the data systems, the infrastructure, that powers the back end of LinkedIn, and the offline systems upon which we can manipulate our data. And I\u2019m a scientist in the sense that I build also the algorithms and the personalizations that we can provide to our members using those systems, to make the experience better for members. So, the Chief Data Officer is bridging these two, and has a little bit of a component around policy, in the sense of making sure that on one side, the security, engineering and legal are talking together to address the problems that can arise from there.<\/p>\n<p><strong>Host: LinkedIn has been both a thought leader and early adopter in the field of data science. Talk a little bit about that field in general, and, more specifically, its history and current role at LinkedIn.<\/strong><\/p>\n<p>Igor Perisic: Well, so history. we can go far, far back. I mean, the terms \u201cdata\u201d and \u201cscience\u201d have been defined, let\u2019s say, in the English dictionary, somewhere around the 17th century. But fundamentally, I think it had another change of direction somewhere within the last ten years. It became somewhere around 2007 or 8-ish, which we worked at LinkedIn, and D.J. Patil, who was my product counterpart at that time, somewhat coined a term within the, let\u2019s say, more recent generation of it, which was more about an individual who would really do five things: who could hack code, create code, who can reason around data and see the product that it has inherently within it, and hack the product for it. An individual who was really good about machine learning, so it can actually create those algorithms. An individual that could communicate. So, you can see the story. You can, maybe, write it up. But you can communicate that to somebody else, and not just towards somebody who\u2019s very knowledgeable about it. And a great engineer who would build infrastructure to it. Because you\u2019ve got to remember that 5 or 6, 7 years ago, the infrastructure wasn\u2019t there. So, you have to build everything. Today, I see it migrating back more towards the ability to create the story, the ability to see the pattern through the data, and then how it can actually help a product.<\/p>\n<p><strong>Host: And the term \u201cdata scientist\u201d is also a very connotative term right now.<\/strong><\/p>\n<p>Igor Perisic: Yeah, I think originally it was because we created something that was different. And we gave it those two terms. Then, later on, as with everything, something becomes very hot and sexy, as a career. So, then everything becomes \u201cdata science.\u201d<\/p>\n<p><strong>Host: Right.<\/strong><\/p>\n<p>Igor Perisic: So, we do the same thing as we call it, a relevance engineer is the same thing as we call it, data scientist, or that we would call it just researchers, research engineers, data engineers. They all migrate around the same topics, so certainly.<\/p>\n<p><strong>Host: Now, you just mentioned a term, \u201crelevance,\u201d which is a term I\u2019ve seen along and around your website. What does \u201crelevance\u201d mean?<\/strong><\/p>\n<p>Igor Perisic: Actually, that\u2019s a good question. It\u2019s certainly something that\u2019s very specific at LinkedIn, where we move towards being much more data-informed in the way that we build products and we take decisions through time. And then bringing all this optimization, techniques\u2026 we didn\u2019t want to call it like optimizers, optimize this, optimize that\u2026 but making things more relevant. And that\u2019s where we sort of keyed around the term \u201crelevance,\u201d as the science to make things more relevant, which is fundamentally to make something more personal to an individual.<\/p>\n<p><strong>Host: I love that. And I think that resonates with a lot of people. So, let\u2019s move over to the mission of LinkedIn and it\u2019s, to quote, \u201cgiving economic opportunity to every member of the global workforce.\u201d So, tell us what role data science and research play in making that mission happen.<\/strong><\/p>\n<p>Igor Perisic: Well, we do have a fundamental role at the core, because when you create economic opportunity for every member of the global workforce, you\u2019re creating that economic opportunity for an individual. And that individual is always unique. Certainly, from, let\u2019s say, a statistical perspective, it\u2019s part of a bigger bucket. But at the core, that opportunity is unique for that individual. And in that situation, it means that you need to actually build up your products or build up your experiences to that individual, and to tailor it to him or her. And that\u2019s exactly where our field comes into play. You can create broad-scope experiences, but when it starts getting into this personalization aspect, that\u2019s where, exactly where machine learning comes in, or AI.<\/p>\n<p><strong>[music]<\/strong><\/p>\n<p><strong>Host: So, in a recent post on your engineering blog last year, it was called \u201cCelebrating Research Excellence in LinkedIn.\u201d You gave a shout out to your researchers for 3 things. One of course is making your products better. But you also referred to contributing to the open source community, and producing world-class research, peer-reviewed research. So, why is the work of your researchers important to the scientific community at large, and vice versa, I would say?<\/strong><\/p>\n<p>Igor Perisic: [laughs] I grew up with an ideal of science. And I, um, there was something that resonated to me from a very, very long time. And I don&#8217;t know why. You know, you grow up and there\u2019s some sentences that stick with you. And one that really stick with me from very early on was, \u201cStanding on the shoulder of the giants.\u201d And the reason for that is I could see the broad scientific community as just one community. And however great you were, you were standing on somebody\u2019s shoulders. And sooner or later, somebody would stand on your shoulders. So, it was never something that it was done for yourself. It was something that was done for the greater community and leveraging all that knowledge and moving it forward. So, then of course, within that scope, contributing back is very critical and very important on two things: one, well, if you have the next generation, the next individuals; and also, a measure of quality. You may think that what you\u2019ve done is great, but it may be completely wrong. So, once you expose it, it\u2019s when you get that feedback that you can actually learn. Being right or wrong is not necessarily fundamentally the most valuable thing, compared to just the experience of learning.<\/p>\n<p><strong>Host: So, there\u2019s been always a bit of tension in the research community between proponents of applied research and basic research, especially in industry. What\u2019s your philosophy about the role of basic research in industry, where the ROI may be years out instead of immediate, and what role does basic research play at LinkedIn, a company whose product research is basically embedded?<\/strong><\/p>\n<p>Igor Perisic: So, I\u2019m a big proponent of research, whether it\u2019s basic or applied. And it\u2019s all a matter of context. I believe that if you only limit yourself to just applied research, you tend to sort of focus into just one area, and just dig in more and more and more and more. And you\u2019re preventing yourself from actually seeing this paradigm shift that can occur. Basic research has this goodness, or this overall perspective of, that there\u2019s something that\u2019s extremely challenging, and there\u2019s something that advances our understanding, and in itself is valuable. The application will follow or will not follow, but it will spark ideas. Within the industry, it\u2019s actually really hard to do just basic research. It\u2019s just a matter of whether you have the ability to do so. In the end, the majority of companies are, need to generate a certain amount of revenue, so you have to be able to actually balance the two. There, Microsoft, I think it was very, very good at balancing the basic as well as applied research. At LinkedIn when we started, we were a startup, so you can\u2019t really do basic research or fundamental research and go to, let\u2019s say, the CEO at that time, in your group of 5 or 6, and work on something and, \u201cDon\u2019t worry about it, in 200 years, it will make a difference.\u201d Because you don\u2019t know whether the company is going to be there two or three years down the road. So, you have to play within in, but still have a very good perspective and overall view that those two worlds are sort of intertwined. You can see the work that Microsoft has done through the years. Fundamentally, you can say, \u201cWell, at which point of time does, let\u2019s say, um, quantum computing become basic to applied?\u201d Well, at the beginning, it was certainly basic. And today, it\u2019s almost applied. Although, the window is very far ahead.<\/p>\n<p><strong>Host: Yeah.<\/strong><\/p>\n<p>Igor Perisic: It\u2019s not happening tomorrow, but it is happening.<\/p>\n<p><strong>Host: So, let\u2019s talk about research cycle for a second, specifically at LinkedIn. What does it look like time-wise and outcome-wise for you, and how is it similar and different from maybe some other things? Because you have a really unique approach to research at LinkedIn.<\/strong><\/p>\n<p>Igor Perisic: Yeah. Our cycles are much shorter. Like, for example, the thing that we\u2019ve published, I made it sure that, very early on, that publications would be about things that we\u2019ve developed and shipped on the site and had affected our members\u2019 experiences, compared to things that we\u2019ve developed in an offline environment on some subqueries and whatnot, and it moved some metric. It had to have gone all the way in. So, in this case, our cycles are different in a sense that the end value is around whether it actually went all the way down to the site. Now, tying into what you said, there\u2019s also another cycle. It\u2019s, how long do you research before you make a difference? And there, our window at LinkedIn is I think most often, except for infrastructure, where it takes time to develop, it\u2019s probably maybe 1 or 2 years out. So, it\u2019s certainly not Microsoft Research.<\/p>\n<p><strong>Host: Right. So, what\u2019s the relationship between the researchers at, say, Microsoft Research now, and LinkedIn, now that you\u2019re sort of part of the same family?<\/strong><\/p>\n<p>Igor Perisic: Well, it\u2019s in multiple dimensions. One is, in some places, we were, let\u2019s say for example, the neural networks, and using CNNs and GPUs, Microsoft was far ahead of the curve, and then we\u2019re benefiting from the learnings from it. For example, how to set up the topology of our clusters, how do we set up the topology of our, let\u2019s say, rankers for it. So, then we\u2019re benefiting from knowledge that somebody else has done, if you want. And there\u2019s also another side as well. We have a different perspective at times, and then it\u2019s just sharing it, as I mentioned a couple of times here, that science advances, or research advances, when you bridge those different types of connections with different types of fields. So, we bring another perspective. We\u2019ve been interacting very closely with the New England team, who has been our, let\u2019s say, partner and front door to the rest of Microsoft Research. As you can expect, there\u2019s lots of interest for Microsoft Research. It\u2019s good that it\u2019s a little bit centralized, so we can actually navigate around all those expectations and demands.<\/p>\n<p><strong>[music]<\/strong><\/p>\n<p><strong>Host: So, you talk about the importance of what you call \u201cconversations\u201d with your members. And there are multiple, multi-level conversations going on at any given time. So, from a technical point of view, how do you stay on top of those and make sense of those conversations in a meaningful way?<\/strong><\/p>\n<p>Igor Perisic: I think it\u2019s a new perspective that I\u2019ve looked at it, and you tend to think that once you\u2019ve found out this new paradigm, the way that you\u2019re going to communicate about it, it makes sense, and you wonder why you didn\u2019t see it before. And probably in 5 to 10 years, I would look at myself and what a complete idiot I was. But that\u2019s the nature of things. Trying to find a model that can help you reason about a problem that you have. In this situation, it\u2019s about conversations in a sense that very early on, we try to do things with our members, get our members to do things. And in this case, a conversation is really an informal chat, if you want, between two individuals, and it\u2019s between with let\u2019s say LinkedIn and the member. And early one, what we\u2019ve done is say, well, we just blasted things. We just sent you emails like there\u2019s no tomorrow. We did very good optimization saying that, well, the more email you get, the more likely you\u2019re going to do something. Which is probably wrong.<\/p>\n<p><strong>Host: Not me.<\/strong><\/p>\n<p>Igor Perisic: Uh. My point. But apparently, somebody had done a study, and that seemed to be good. Of course, when you look at high-level statistics, it seems to be fine. But the conversation was more like me screaming at you. And then when you start shifting by saying, well, there\u2019s a lot of conversation that we can have, so which one should I communicate to you, like, right now? And today, or in the recent past, we\u2019ve moved more into thinking about, well, this conversation doesn\u2019t, don\u2019t stop just with the action. That there\u2019s some sense of, what are you doing at LinkedIn, and what do you want to achieve at LinkedIn? And those goals don\u2019t stop tomorrow, don\u2019t stop on the click, don\u2019t stop on the view, don\u2019t stop on the share. They go for a longer period of time. And the problem then starts shifting, because very often the techniques would be, I need to find, let\u2019s say, a way to \u2013 use some different techniques \u2013 to figure out what is your likelihood to act on something? It\u2019s a probability. And in that case, whether you do logistic regressions, whether you do different types of all the way up to uh, neural nets, you come up with a number. And that number, and then you optimize around it. And it\u2019s usually just about that activity. Now, if you view it in the context of a conversation, that activity doesn\u2019t stop right there. It continues. We\u2019re having a dialogue. It\u2019s going to go on for a little while. You can\u2019t just optimize for the next step. So how do you go in and out of that?<\/p>\n<p><strong>Host: Right.<\/strong><\/p>\n<p>Igor Perisic: And I felt that it\u2019s the right time to think about it. Although, we had thought about it already some years ago, simply because of the shift that we\u2019re seeing nowadays with more and more of just voice-driven interfaces appearing in a lot of places, which becomes another way to communicate with an individual. Like, in the beginning, it was email, then it was notification, then you have mobile pushes, or pull downs, then you have Windows tiles on the desktop. And it becomes more and more natural, like it\u2019s voice-driven. And if it\u2019s voice-driven, then that, it is becoming a real dialogue. So, once you have that interface that you can leverage to build up your application, how are you thinking about your optimizations? If you\u2019re thinking them still in the logistic regression work, it\u2019s not going to work.<\/p>\n<p><strong>Host: What does work?<\/strong><\/p>\n<p>Igor Perisic: Well, we\u2019ve taken some early steps a couple of years ago to look more around the quadratic constraints and quadratic programming. It worked for us, that first item, across multiple different types of dialogues that we\u2019re having with members, and they work at, let\u2019s say within a couple of steps. Overall, where that is all going is still research. Just one or two steps ahead.<\/p>\n<p><strong>Host: Right.<\/strong><\/p>\n<p>Igor Perisic: And we\u2019re making good progress. And it\u2019s going to actually be very, very, interesting to see how those things are actually shifting.<\/p>\n<p><strong>Host: Yeah. Well, it\u2019s an exciting time with so much research in machine learning, and people trying things, to see how it impacts both the technology and the people that they\u2019re working with. So, you said at one point that machine learning actually helps transform, not just inform, but transform your interactions with your members. Is that what you\u2019re talking about here?<\/strong><\/p>\n<p>Igor Perisic: Fundamentally, at the core of everything, you want an interaction to be very natural. Whether the design is superb, and it fits exactly the way that you would anticipate it, it just feels natural. And that\u2019s exactly where our field comes into play. How do you make that thing natural? It\u2019s not just a design perspective, but it\u2019s also, what is the content that you\u2019re showing? How are you going there? So, it\u2019s transforming that experience, instead of just making it better.<\/p>\n<p><strong>Host: Right. So, I\u2019m a LinkedIn member myself.<\/strong><\/p>\n<p>Igor Perisic: Thank you.<\/p>\n<p><strong>Host: Yeah. And I got to tell you, I have noticed, over the last year, a difference in how I\u2019m getting notified. And one thing I\u2019ve noticed is that they seem more personal to me? Like, I would get a notification about a person that I know, or care about, as opposed to just feeling like you\u2019re trying to pull me into the app, right?<\/strong><\/p>\n<p>Igor Perisic: So, that\u2019s exactly what we worked over the last, I would say two years, to make it exactly that, compared to me pinging you, \u201cHey, come back, come back, come back.\u201d I don&#8217;t know why you would come back, but here it is. Here\u2019s like 2,000 reasons for you to come back, pick one\u2026 To more, hey, here\u2019s something of value within the context of your interest of LinkedIn, or your value propositions we believe are important. Here\u2019s something that hey, on one side, you ought to know, it would be a good thing, and on the other hand, it\u2019s to share within the network also.<\/p>\n<p><strong>Host: Right. So, I have to say, that makes me happy that the kinds of technologies you\u2019re working on, especially machine-learning technologies that might be helping to broaden your understanding of what I like, what I\u2019m interested in, is actually playing out in the real world for people like me. And I tend to be skeptical and cynical of, you know, high-tech notifications. I don\u2019t like those red notifications on my phone, but anyway\u2026<\/strong><\/p>\n<p>Igor Perisic: Sure. It\u2019s kind of interesting when you pick up the red thing. We associated red with danger. And it never occurred to me why would it be red. Maybe it\u2019s saying like, oh, you need to do something like right now. But we have so many of them. And that\u2019s why I think we\u2019re ready for that next iteration, that other change. I look at my mailbox, and it has like numbers in the hundreds. From time to time I clear it out. I look at my phone, and it\u2019s interesting to see how some interfaces have decided to keep the number low, and others to keep the number high. And to me, it seems that the ones that keep the number high is those that are still screaming at you, and the ones that keep the number low is to say like, we understand that you have a busy life. We understand that you have a lot of things and a lot of applications on your phone. Let\u2019s make sure that if I put a number out there, it\u2019s something that would be really good for you to know, compared to just, \u201cCome back, come back. come back!\u201d<\/p>\n<p><strong>Host: And that\u2019s a fine line, right? Because you\u2019re competing for people\u2019s attention in an increasingly crowded world. And where do you, you know, stop ALL CAPS, and whisper to\u2026?<\/strong><\/p>\n<p>Igor Perisic: But it seems to be working, right?<\/p>\n<p><strong>Host: Yeah, for me\u2026.<\/strong><\/p>\n<p>Igor Perisic: No, but we can see it also. Of course, everybody is, to some extent, informed by the metrics of the product performances. And we see that change.<\/p>\n<p><strong>Host: Yeah.<\/strong><\/p>\n<p>Igor Perisic: Originally, people would be confused about, \u201cHey, why LinkedIn?\u201d And now it becomes more obvious. And then it becomes more natural, because you actually steer the conversation to the right places.<\/p>\n<p><strong>Host: Right. Which is interesting. This whole topic of conversation that we\u2019re on right now is talking about how technology is actually making me feel more personal towards a particular product. Which is somewhat, sort of, backwards. But I like it. It\u2019s working. Good job.<\/strong><\/p>\n<p>Igor Perisic: Data science, the terms themselves, were not \u201cglued together\u201d to define what we\u2019re doing less than 7, 8 years ago, let\u2019s say. Granted, there are some others that you can, that were sort of anticipating that it was going to go there. But it\u2019s similar with this sense of personalization or this sense of, it makes sense within the scope of what I do, or who it is. And in the past, we used to call it an augmentation of yourself.<\/p>\n<p><strong>Host: Oh, yeah.<\/strong><\/p>\n<p>Igor Perisic: An agent that works, that extends and works for you. And it fits naturally within what you do. And similar to data science back then, people would look at you blank and say, \u201cWhat are you talking about? What does it mean?\u201d And today, you can start seeing it. You say, well, let\u2019s bring all these AI, let\u2019s bring all this technology to make me quote-unquote better. Better as an understanding of what\u2019s happening around me. Better in the way that I can connect, interact with it, augment the way that I can deal with the problems that I need to do at work, and make me better at that.<\/p>\n<p><strong>[music]<\/strong><\/p>\n<p><strong>Host: Most of the researchers that I\u2019ve talked to on this podcast are big \u201copen source\u201d fans, and I think you are too. You\u2019ve published articles, given talks, and you\u2019re on the open source council, or you oversee the open source council at LinkedIn. Why is open source a good thing, especially for researchers?<\/strong><\/p>\n<p>Igor Perisic: Well, I started by saying that I love science because we all stand on the shoulder of giants. But open source is certainly a movement that has just accelerated. And I have to say that in the beginning that, everybody was wondering, is it going to stick for real? Is it going to stay forever? But it just fit so naturally, the way that we develop things as developers. If you look at any company that has more than two people, eventually there\u2019s some level of abstraction that have built, and people just leverage the code of each other. And open source, it\u2019s just a further extension to reaching out to the entire community. I was always a big proponent, even prior to starting at LinkedIn, because we were just leveraging solutions, and every other individuals had developed. For example, at LinkedIn, the search engine, originally, the core of it was Lucene, an Apache open source project. Then we felt like, well, since we\u2019re riding on those giants, we should contribute back. And some other individuals will create businesses around it, and that creates a whole ecosystem that becomes that much, much better. Then through it, we stumbled upon truths, some of them being that you actually write a much better code when you actually think about open sourcing it, compared to when it\u2019s kept internally. And not only do you write better code, but you have a better way to reason about it. You look at it, does it make sense, does it not make sense? Does it complement what is out there, or does it not complement what is out there? And a lot of goodness comes through that.<\/p>\n<p><strong>Host: So, when we talked before, you mentioned open source code as similar to a peer-reviewed research paper that you\u2019re publishing in some way. Can you explain a little bit about what you think about that?<\/strong><\/p>\n<p>Igor Perisic: I usually take two allegories about open source. One, is it feels like peer review, because in a sense, you\u2019re writing a piece of software, you\u2019re documenting it, and you\u2019re pushing it out there. But there\u2019s so many publications. And if your paper is not good, nobody is going to actually build other things from it. You\u2019re not going to be cited much. And open source is the same. If you\u2019re not, investing into your product to make it great, nobody\u2019s going to actually really leverage from it. And I used to take another one, which is I guess through my life experience. Open sourcing is a little baby also. You cherish it. It grows. But then at some point of time, you\u2019ve got to let it have its own life. And it\u2019s like your kid that grew up and now becomes mature. And they\u2019re going to do something that you may not have envisioned, which is perfect. And open sourcing is the same. You sometimes have the tendency to keep that kid at home. I\u2019d say, \u201cWell, no, let them discover the rest of the world.\u201d<\/p>\n<p><strong>Host: Igor, I just open sourced my daughter at the University of Washington.<\/strong><\/p>\n<p>Igor Perisic: Congratulations.<\/p>\n<p><strong>Host: I totally get what you mean. Let\u2019s talk about LinkedIn\u2019s relationship with the larger academic and research community through public\/private research partnerships. You have a program called The Economic Graph. And it started I think as a challenge, but now it\u2019s an ongoing program. Give us an overview of what it is and how it came about, and what research you focus on.<\/strong><\/p>\n<p>Igor Perisic: So, the Economic Graph challenge. The idea of letting external individuals from LinkedIn look at our data and answer some extremely pertinent and relevant questions, was there from the time that I joined LinkedIn, and probably even before that. So, how do we create an environment where we preserve that privacy of the individual, and open it up to the community? That was what the Economic Graph challenge was. We basically reached out to the, to global community, friends and family, and others, to say, \u201cWell, if you had LinkedIn\u2019s data, what are the questions that you would want to answer?\u201d And within that, we had more than 200 proposals that came back. We selected, uh, 12. The ideas were just brilliant. They ranged from, how do you think about micro industries and different environments from a geo perspective, like the fashion industry in Milan, or, let\u2019s say, the rise of electric cars in the valley, for example, to, how do women define themselves, or write about themselves on LinkedIn compared to men, if you control for multiple factors? But again, the resources are limited, so you reach out to the community to go&#8230;.<\/p>\n<p><strong>Host: So, the people that participate in this with you, the academics or researchers, come from all different walks of the research life, and they participate by presenting a proposal and getting access to some of the data from LinkedIn? Is that how that works?<\/strong><\/p>\n<p>Igor Perisic: Well, originally, we, as I mentioned, we just broad-casted and we got a lot of proposals back. And then we would evaluate on the idea, the interestingness of the idea, but also the ability to execute on that idea. Meaning that you have the ability to, using our data, answer the question.<\/p>\n<p><strong>Host: Right.<\/strong><\/p>\n<p>Igor Perisic: Most ended up by being from academia.<\/p>\n<p><strong>Host: What\u2019s your overall goal with Economic Graph?<\/strong><\/p>\n<p>Igor Perisic: To create economic opportunity for every member of the global workforce. And that\u2019s where I got to believe a bit more into the vision and mission of LinkedIn, to create that economic opportunity. And not only \u201can\u201d economic opportunity, but the right one for you; the one that you aspire to. So, the way that we used the Economic Graph, on one side, it still needs to be descriptive, because it provides the value of the labor market. It provides an image, whether it\u2019s instantaneous, or whether it\u2019s in the past. It provides a sense of, \u201cWhat are people doing in the labor market, and what are the movements within it?\u201d It\u2019s a living entity, and we provide a picture of it. And there\u2019s tremendous value to it, because the timelines are very quick. You don\u2019t have to wait 6 months, or 3 months, to get a government statistic, but it provides a good picture, so then that is extremely valuable.<\/p>\n<p><strong>Host: Yeah.<\/strong><\/p>\n<p>Igor Perisic: The other is, to understand and see that careers are also living, they migrate, they shift from one domain to another. You have multiple careers in your lifetime. And understanding what it takes and what it doesn\u2019t take, and helping all members to actually build up their careers. So, that\u2019s where the Economic Graph is also moving, to figure it out, how do I share that information with you? How do I help you nurture your career? And in the end, well, that\u2019s to create economic opportunity and get you along your life cycle. Everybody, I believe, starts a job thinking that it\u2019s going to be the job they\u2019re going to do forever. I started LinkedIn thinking that, \u201cEhh, 2 to 3 years of my life,\u201d and 10 years later, I\u2019m still here. But\u2026 and actually, I\u2019m still here because of the vision and the belief of the ability that we can move it forward. But once you think in that route, you\u2019re seeing the fluidity of the labor market as being something that is important. And I wasn\u2019t an economist before coming to LinkedIn, whatsoever. But there\u2019s some very fascinating mind-frame around it, or frameworks to think about it.<\/p>\n<p><strong>[music]<\/strong><\/p>\n<p><strong>Host: You\u2019re a researcher. You\u2019ve ended up parlaying a research interest into a career. What advice would you give researchers who were heading into the field of maybe data science, or even more generally, as they prepare for their next steps after grad school?<\/strong><\/p>\n<p>Igor Perisic: The main one, be curious. Continue to be curious. There\u2019s a sense of, if you went into getting a PhD, you\u2019re wanting to solve a problem, there\u2019s something that was missing, you went at it, and you dig in, and you attempted to move the answer a bit further along\u2026 that\u2019s great. It\u2019s certainly one of the best experiences. On the other hand, make sure that once that happens, don\u2019t just limit the focus. I think that\u2019s, at times, what researchers are missing, that, yes, it\u2019s valuable to dig in, very much, but be aware of the rest, and make connections from that to maybe better what you\u2019re doing, see different paradigms, different perspectives. I was listening to Fabiola Gianotti just recently, who is the head of the CERN. She studied first humanities before moving into physics. And I view myself as a scientist also. And the things that I value more, that I wish I had more going to school, is actually humanities. Not math or the rest, because I feel like, no, I get these anyway. So, that\u2019s a given. But the rest if missing. And if you think about it this way, today at LinkedIn, I\u2019m trying to understand, from a member\u2019s perspective, what are the struggles that they\u2019re going through in order to make their career better? And you get that more by this site exposure, by this interest. Make these connections. And these connections have to be outside of just your domain. If I had one encouragement, it\u2019s keep the wonder. Just go for it. Just keep the wonder and be willing to learn different topics. And challenge yourself a lot.<\/p>\n<p><strong>Host: Igor Perisic, thank you so much. It\u2019s been a delight to talk to you today.<\/strong><\/p>\n<p>Igor Perisic: You&#8217;re welcome very much. Thank you.<\/p>\n<p><strong>To learn more about how researchers are harnessing and using data to make the digital world \u2013 and your experience with it \u2013 better, visit <a href=\"https:\/\/www.microsoft.com\/en-us\/research\/\">Microsoft.com\/research<\/a><\/strong><\/p>\n<p>&nbsp;<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Episode 11, February 7, 2018 &#8211; Big data is a big deal, and if you follow the popular technical press, you\u2019ll have heard all the metaphors: data is the new oil, the new bacon, the new currency, the new electricity. It\u2019s even been called the new black. While data may not actually be any of these things, we can say this: in today\u2019s networked world, data is increasingly valuable, and it is essential to research, both basic and applied.<\/p>\n","protected":false},"author":37074,"featured_media":464841,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"msr-url-field":"https:\/\/player.blubrry.com\/id\/31215424\/","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"_classifai_error":"","msr-author-ordering":[],"msr_hide_image_in_river":0,"footnotes":""},"categories":[194475,194455,240054,194460],"tags":[186831,194810,186418],"research-area":[13563,13555],"msr-region":[256048],"msr-event-type":[],"msr-locale":[268875],"msr-post-option":[],"msr-impact-theme":[],"msr-promo-type":[],"msr-podcast-series":[],"class_list":["post-464682","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-database-data-analytics-platforms","category-machine-learning","category-msr-podcast","category-search-and-information-retrieval","tag-big-data","tag-big-data-science","tag-machine-learning","msr-research-area-data-platform-analytics","msr-research-area-search-information-retrieval","msr-region-global","msr-locale-en_us"],"msr_event_details":{"start":"","end":"","location":""},"podcast_url":"https:\/\/player.blubrry.com\/id\/31215424\/","podcast_episode":"","msr_research_lab":[],"msr_impact_theme":[],"related-publications":[],"related-downloads":[],"related-videos":[],"related-academic-programs":[],"related-groups":[269241],"related-projects":[171035,170958],"related-events":[],"related-researchers":[],"msr_type":"Post","featured_image_thumbnail":"<img width=\"480\" height=\"280\" src=\"https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2018\/02\/MicrosoftResearch_Podcast_IgorPerisic_MCR-AcademicCarousel_480x280.jpg\" class=\"img-object-cover\" alt=\"a man wearing glasses and looking at the camera\" decoding=\"async\" loading=\"lazy\" srcset=\"https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2018\/02\/MicrosoftResearch_Podcast_IgorPerisic_MCR-AcademicCarousel_480x280.jpg 480w, https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2018\/02\/MicrosoftResearch_Podcast_IgorPerisic_MCR-AcademicCarousel_480x280-300x175.jpg 300w\" sizes=\"auto, (max-width: 480px) 100vw, 480px\" \/>","byline":"","formattedDate":"February 7, 2018","formattedExcerpt":"Episode 11, February 7, 2018 - Big data is a big deal, and if you follow the popular technical press, you\u2019ll have heard all the metaphors: data is the new oil, the new bacon, the new currency, the new electricity. It\u2019s even been called the&hellip;","locale":{"slug":"en_us","name":"English","native":"","english":"English"},"_links":{"self":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/posts\/464682","targetHints":{"allow":["GET"]}}],"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\/37074"}],"replies":[{"embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/comments?post=464682"}],"version-history":[{"count":16,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/posts\/464682\/revisions"}],"predecessor-version":[{"id":487640,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/posts\/464682\/revisions\/487640"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/media\/464841"}],"wp:attachment":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/media?parent=464682"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/categories?post=464682"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/tags?post=464682"},{"taxonomy":"msr-research-area","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/research-area?post=464682"},{"taxonomy":"msr-region","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-region?post=464682"},{"taxonomy":"msr-event-type","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-event-type?post=464682"},{"taxonomy":"msr-locale","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-locale?post=464682"},{"taxonomy":"msr-post-option","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-post-option?post=464682"},{"taxonomy":"msr-impact-theme","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-impact-theme?post=464682"},{"taxonomy":"msr-promo-type","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-promo-type?post=464682"},{"taxonomy":"msr-podcast-series","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-podcast-series?post=464682"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}