{"id":199989,"date":"2014-06-30T13:42:31","date_gmt":"2014-06-30T13:42:31","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/events\/msr-nyc-data-science-seminar-series\/"},"modified":"2025-08-06T12:01:54","modified_gmt":"2025-08-06T19:01:54","slug":"msr-nyc-data-science-seminar-series","status":"publish","type":"msr-event","link":"https:\/\/www.microsoft.com\/en-us\/research\/event\/msr-nyc-data-science-seminar-series\/","title":{"rendered":"MSR NYC Data Science Seminar Series"},"content":{"rendered":"\n\n<p><strong>Venue:<\/strong> Microsoft Research New York (<a class=\"msr-external-link glyph-append glyph-append-open-in-new-tab glyph-append-xsmall\" href=\"http:\/\/www.bing.com\/maps\/#Y3A9NDcuNjgwMDk5fi0xMjIuMTIwNTk4Jmx2bD00JnN0eT1yJnE9NjQxJTIwQXZlbnVlJTIwb2YlMjB0aGUlMjBBbWVyaWNhcyUyQyUyME5ldyUyMFlvcmslMkMlMjBVbml0ZWQlMjBTdGF0ZXM=\" target=\"_blank\">641 Avenue of the Americas, New York<span class=\"sr-only\"> (opens in new tab)<\/span><\/a>)<\/p>\n<p><strong>Host:<\/strong> Ceren Budak, Post Doc Researcher<span id=\"label-external-link\" class=\"sr-only\" aria-hidden=\"true\">Opens in a new tab<\/span><\/p>\n<p>This seminar series is bringing data science researchers from Columbia University, NYU, Cornell Tech and Microsoft Research together. Our goal is to increase interactions within the broader New York data science community, and to provide a new forum for discussions on data science research.<\/p>\n<h2>Format<\/h2>\n<p>The events in this series start with a formal talk session (45 minutes). Invited speakers present short talks, providing their views on the opportunities and challenges in data science research. The second part of the event (2 hours) is a wine and cheese social designed to enable researchers to exchange ideas in a relaxed setting.<span id=\"label-external-link\" class=\"sr-only\" aria-hidden=\"true\">Opens in a new tab<\/span><\/p>\n<h3>Microsoft<\/h3>\n<p>Ceren Budak, Post Doc Researcher<\/p>\n<p><a href=\"https:\/\/www.microsoft.com\/en-us\/research\/people\/duncan\/\">Duncan Watts<span class=\"sr-only\"> (opens in new tab)<\/span><\/a>, Principal Researcher<\/p>\n<p><a href=\"https:\/\/www.microsoft.com\/en-us\/research\/people\/jchayes\/\">Jennifer Chayes<span class=\"sr-only\"> (opens in new tab)<\/span><\/a>, Distinguished Scientist \/ Managing Director, Microsoft Research New England & New York City<\/p>\n<h3>Other<\/h3>\n<p><a class=\"msr-external-link glyph-append glyph-append-open-in-new-tab glyph-append-xsmall\" target=\"_blank\" href=\"http:\/\/www.cis.upenn.edu\/~mkearns\/\">Michael Kearns<span class=\"sr-only\"> (opens in new tab)<\/span><\/a>, University of Pennsylvania<\/p>\n<p><a class=\"msr-external-link glyph-append glyph-append-open-in-new-tab glyph-append-xsmall\" target=\"_blank\" href=\"http:\/\/www.soc.cornell.edu\/faculty\/macy.html\">Michael Macy<span class=\"sr-only\"> (opens in new tab)<\/span><\/a>, Cornell University<\/p>\n<p><a class=\"msr-external-link glyph-append glyph-append-open-in-new-tab glyph-append-xsmall\" target=\"_blank\" href=\"http:\/\/people.stern.nyu.edu\/cperlich\/\">Claudia Perlich<span class=\"sr-only\"> (opens in new tab)<\/span><\/a>, Dstillery; NYU Stern<\/p>\n<p><a class=\"msr-external-link glyph-append glyph-append-open-in-new-tab glyph-append-xsmall\" target=\"_blank\" href=\"http:\/\/www.cs.columbia.edu\/~blei\/\">David Blei<span class=\"sr-only\"> (opens in new tab)<\/span><\/a>, Columbia University<\/p>\n<p><a class=\"msr-external-link glyph-append glyph-append-open-in-new-tab glyph-append-xsmall\" target=\"_blank\" href=\"http:\/\/tech.cornell.edu\/deborah-estrin\/\">Deborah Estrin<span class=\"sr-only\"> (opens in new tab)<\/span><\/a>, Cornell Tech<\/p>\n<p><a class=\"msr-external-link glyph-append glyph-append-open-in-new-tab glyph-append-xsmall\" target=\"_blank\" href=\"http:\/\/yann.lecun.com\/\">Yann LeCun<span class=\"sr-only\"> (opens in new tab)<\/span><\/a>, NYU and Facebook<\/p>\n<p><a class=\"msr-external-link glyph-append glyph-append-open-in-new-tab glyph-append-xsmall\" target=\"_blank\" href=\"http:\/\/mornaaman.com\/\">Mor Naaman<span class=\"sr-only\"> (opens in new tab)<\/span><\/a>, Cornell Tech<\/p>\n<p><a class=\"msr-external-link glyph-append glyph-append-open-in-new-tab glyph-append-xsmall\" target=\"_blank\" href=\"http:\/\/www1.cs.columbia.edu\/~jebara\/\">Tony Jebara<span class=\"sr-only\"> (opens in new tab)<\/span><\/a>, Columbia University<span id=\"label-external-link\" class=\"sr-only\" aria-hidden=\"true\">Opens in a new tab<\/span><\/p>\n<p><a href=\"https:\/\/www.microsoft.com\/en-us\/research\/video\/msr-nyc-data-science-seminar-series\/\">MSR NYC Data Science Seminar Series<\/a><\/p>\n<p><a href=\"https:\/\/www.microsoft.com\/en-us\/research\/video\/msr-nyc-data-science-seminar-series-2\/\">MSR NYC Data Science Seminar Series #2<\/a><\/p>\n<p><a href=\"https:\/\/www.microsoft.com\/en-us\/research\/video\/msr-nyc-data-science-seminar-series-3-topic-models-and-user-behavior\/\">MSR NYC Data Science Seminar Series #3 &#8211; Topic Models and User Behavior<\/a><\/p>\n<p><a href=\"https:\/\/www.microsoft.com\/en-us\/research\/video\/msr-nyc-data-science-seminar-series-4-what-makes-us-human-machine-learning-challenges-in-digital-advertising\/\">MSR NYC Data Science Seminar Series #4 &#8211; What Makes us Human? Machine Learning Challenges in Digital Advertising<\/a><span id=\"label-external-link\" class=\"sr-only\" aria-hidden=\"true\">Opens in a new tab<\/span><\/p>\n<div class=\"conM \">\n<p>\t<div data-wp-context='{\"items\":[]}' data-wp-interactive=\"msr\/accordion\">\n\t\t\t\t\t<div class=\"clearfix\">\n\t\t\t\t<div\n\t\t\t\t\tclass=\"btn-group align-items-center mb-g float-sm-right\"\n\t\t\t\t\tdata-bi-aN=\"accordion-collapse-controls\"\n\t\t\t\t>\n\t\t\t\t\t<button\n\t\t\t\t\t\tclass=\"btn btn-link m-0\"\n\t\t\t\t\t\tdata-bi-cN=\"Expand all\"\n\t\t\t\t\t\tdata-wp-bind--aria-controls=\"state.ariaControls\"\n\t\t\t\t\t\tdata-wp-bind--aria-expanded=\"state.ariaExpanded\"\n\t\t\t\t\t\tdata-wp-bind--disabled=\"state.isAllExpanded\"\n\t\t\t\t\t\tdata-wp-class--inactive=\"state.isAllExpanded\"\n\t\t\t\t\t\tdata-wp-on--click=\"actions.onExpandAll\"\n\t\t\t\t\t\ttype=\"button\"\n\t\t\t\t\t>\n\t\t\t\t\t\tExpand all\t\t\t\t\t<\/button>\n\t\t\t\t\t<span aria-hidden=\"true\"> | <\/span>\n\t\t\t\t\t<button\n\t\t\t\t\t\tclass=\"btn btn-link m-0\"\n\t\t\t\t\t\tdata-bi-cN=\"Collapse all\"\n\t\t\t\t\t\tdata-wp-bind--aria-controls=\"state.ariaControls\"\n\t\t\t\t\t\tdata-wp-bind--aria-expanded=\"state.ariaExpanded\"\n\t\t\t\t\t\tdata-wp-bind--disabled=\"state.isAllCollapsed\"\n\t\t\t\t\t\tdata-wp-class--inactive=\"state.isAllCollapsed\"\n\t\t\t\t\t\tdata-wp-on--click=\"actions.onCollapseAll\"\n\t\t\t\t\t\ttype=\"button\"\n\t\t\t\t\t>\n\t\t\t\t\t\tCollapse all\t\t\t\t\t<\/button>\n\t\t\t\t<\/div>\n\t\t\t<\/div>\n\t\t\t\t<ul class=\"msr-accordion\">\n\t\t\t\t\t\t\t\t<li class=\"m-0\" data-wp-context='{\"id\":\"accordion-content-6234\"}' data-wp-init=\"callbacks.init\">\n\t\t<div class=\"accordion-header\">\n\t\t\t<button\n\t\t\t\taria-controls=\"accordion-content-6234\"\n\t\t\t\tclass=\"btn btn-collapse\"\n\t\t\t\tdata-wp-bind--aria-expanded=\"state.isExpanded\"\n\t\t\t\tdata-wp-on--click=\"actions.onClick\"\n\t\t\t\tid=\"accordion-button-6233\"\n\t\t\t\ttype=\"button\"\n\t\t\t>\n\t\t\t\tJune 25, 2015\t\t\t<\/button>\n\t\t<\/div>\n\t\t<div\n\t\t\taria-labelledby=\"accordion-button-6233\"\n\t\t\tclass=\"msr-accordion__content\"\n\t\t\tdata-wp-bind--inert=\"!state.isExpanded\"\n\t\t\tdata-wp-run=\"callbacks.run\"\n\t\t\tid=\"accordion-content-6234\"\n\t\t>\n\t\t\t<div class=\"msr-accordion__body\">\n\t\t\t\t<p><strong>Speaker:<\/strong> <a class=\"msr-external-link glyph-append glyph-append-open-in-new-tab glyph-append-xsmall\" target=\"_blank\" href=\"http:\/\/www.cis.upenn.edu\/~mkearns\/\">Michael Kearns<span class=\"sr-only\"> (opens in new tab)<\/span><\/a>, University of Pennsylvania<\/p>\n<p><b>Title:<\/b> <strong>From \u201cIn\u201d to \u201cOver\u201d: Behavioral Experiments on Whole-Network Computation<\/strong><\/p>\n<p><b>Abstract:<\/b>\u00a0 We report on a series of behavioral experiments in human computation on three different tasks over networks: graph coloring, community detection (or graph clustering), and competitive contagion. While these tasks share similar action spaces and interfaces, they capture a diversity of computational challenges: graph coloring is a search problem, clustering is an optimization problem, and competitive contagion is a game-theoretic problem. In contrast with much of the recent literature on human-subject experiments in networks, in which collectives of subjects are embedded \u201cin\u201d the network, and have only local information and interactions, here individual subjects have a global (or \u201cover\u201d) view and must solve \u201cwhole network\u201d problems alone. Our primary findings are that subject performance is impressive across all three problem types; that subjects find diverse and novel strategies for solving each task; and that collective performance can often be strongly correlated with known algorithms.<\/p>\n<p>Joint work with Lili Dworkin.<\/p>\n<p><b>Bio:<\/b> Michael Kearns is a professor in the Computer and Information Science department at the University of Pennsylvania, where he holds the National Center Chair and has joint appointments in the Wharton School. He is founding director of Penn\u2019s Networked and Social Systems Engineering (NETS) program (<a class=\"msr-external-link glyph-append glyph-append-open-in-new-tab glyph-append-xsmall\" target=\"_blank\" href=\"http:\/\/www.nets.upenn.edu\/\">www.nets.upenn.edu<span class=\"sr-only\"> (opens in new tab)<\/span><\/a>), and founding director of Penn&#8217;s Warren Center for Network and Data Sciences (<a class=\"msr-external-link glyph-append glyph-append-open-in-new-tab glyph-append-xsmall\" target=\"_blank\" href=\"http:\/\/www.warrencenter.upenn.edu\/\">www.warrencenter.upenn.edu<span class=\"sr-only\"> (opens in new tab)<\/span><\/a>). His research interests include topics in machine learning, algorithmic game theory, social networks, and computational finance. He has worked and consulted extensively in the technology and finance industries. He is a fellow of the American Academy of Arts and Sciences, the Association for Computing Machinery, and the Association for the Advancement of Artificial Intelligence.<\/p>\n<p><span id=\"label-external-link\" class=\"sr-only\" aria-hidden=\"true\">Opens in a new tab<\/span><\/p>\n\t\t\t<\/div>\n\t\t<\/div>\n\t<\/li>\n\t\t<li class=\"m-0\" data-wp-context='{\"id\":\"accordion-content-6236\"}' data-wp-init=\"callbacks.init\">\n\t\t<div class=\"accordion-header\">\n\t\t\t<button\n\t\t\t\taria-controls=\"accordion-content-6236\"\n\t\t\t\tclass=\"btn btn-collapse\"\n\t\t\t\tdata-wp-bind--aria-expanded=\"state.isExpanded\"\n\t\t\t\tdata-wp-on--click=\"actions.onClick\"\n\t\t\t\tid=\"accordion-button-6235\"\n\t\t\t\ttype=\"button\"\n\t\t\t>\n\t\t\t\tApril 16, 2015\t\t\t<\/button>\n\t\t<\/div>\n\t\t<div\n\t\t\taria-labelledby=\"accordion-button-6235\"\n\t\t\tclass=\"msr-accordion__content\"\n\t\t\tdata-wp-bind--inert=\"!state.isExpanded\"\n\t\t\tdata-wp-run=\"callbacks.run\"\n\t\t\tid=\"accordion-content-6236\"\n\t\t>\n\t\t\t<div class=\"msr-accordion__body\">\n\t\t\t\t<p><strong>Speaker:<\/strong> <a class=\"msr-external-link glyph-append glyph-append-open-in-new-tab glyph-append-xsmall\" target=\"_blank\" href=\"http:\/\/www.soc.cornell.edu\/faculty\/macy.html\">Michael Macy<span class=\"sr-only\"> (opens in new tab)<\/span><\/a>, Cornell University<\/p>\n<p><b>Title:<\/b> <strong>On A Scale From 1 to 5, How Much Confidence Do You Have in Survey Results?<\/strong><\/p>\n<p><b>Abstract:<\/b> From astronomy to neuroscience to particle physics, scientific knowledge depends heavily on the available tools for observation. Since the introduction of stratified sampling in 1934, the survey has been the single most important observational tool for social science. During this time, impressive advances have taken place in our ability to reduce sampling error (e.g. Respondent Driven Sampling), measurement error (e.g. SEM), specification error (e.g. linear mixed models), and inferential error (e.g. Propensity Score Matching). Nevertheless, increasing confidence in survey technology has paradoxically reinforced a debilitating theoretical blinder that has compromised the ability of social science to elicit confidence in predictions. What is worse, this blinder has largely escaped notice through a combination of ideological bias and reluctance to pull back the covers on problems for which we have no solution. The good news is that a solution is finally on the horizon, with the potential for social science to begin to close the gap with the physical and life sciences in predictive ability. The bad news is \u2026 (to be continued).<\/p>\n<p><b>Bio:<\/b> Michael Macy earned his B.A. and Ph.D from Harvard, along with an M.A. from Stanford. He is currently Goldwin Smith Professor of Arts and Sciences and Director of the Social Dynamics Laboratory at Cornell, with a dual appointment in the Departments of Sociology and Information Science. With support from the National Science Foundation, the Department of Defense, and Google, his research team has used computational models, online laboratory experiments, and digital traces of device-mediated interaction to explore familiar but enigmatic social patterns, such as circadian rhythms, the emergence and collapse of fads, the spread of self-destructive behaviors, cooperation in social dilemmas, the critical mass in collective action, the spread of high-threshold contagions on small-world networks, the polarization of opinion, segregation of neighborhoods, and assimilation of minority cultures. Recent research uses 509 million Twitter messages to track diurnal and seasonal mood changes in 54 countries, telephone logs for 12B calls in the UK to measure the economic correlates of network structure, and hundreds of millions of Yahoo! email logs in 90 countries to test Huntington\u2019s theory of the &#8220;clash of civilizations.&#8221; His research has been published in leading journals, including Science, PNAS, American Journal of Sociology, American Sociological Review, and Annual Review of Sociology.<\/p>\n<p><span id=\"label-external-link\" class=\"sr-only\" aria-hidden=\"true\">Opens in a new tab<\/span><\/p>\n\t\t\t<\/div>\n\t\t<\/div>\n\t<\/li>\n\t\t<li class=\"m-0\" data-wp-context='{\"id\":\"accordion-content-6238\"}' data-wp-init=\"callbacks.init\">\n\t\t<div class=\"accordion-header\">\n\t\t\t<button\n\t\t\t\taria-controls=\"accordion-content-6238\"\n\t\t\t\tclass=\"btn btn-collapse\"\n\t\t\t\tdata-wp-bind--aria-expanded=\"state.isExpanded\"\n\t\t\t\tdata-wp-on--click=\"actions.onClick\"\n\t\t\t\tid=\"accordion-button-6237\"\n\t\t\t\ttype=\"button\"\n\t\t\t>\n\t\t\t\tFebruary 5, 2015\t\t\t<\/button>\n\t\t<\/div>\n\t\t<div\n\t\t\taria-labelledby=\"accordion-button-6237\"\n\t\t\tclass=\"msr-accordion__content\"\n\t\t\tdata-wp-bind--inert=\"!state.isExpanded\"\n\t\t\tdata-wp-run=\"callbacks.run\"\n\t\t\tid=\"accordion-content-6238\"\n\t\t>\n\t\t\t<div class=\"msr-accordion__body\">\n\t\t\t\t<p>You can view the recording of the talk <a class=\"invalidLink\" href=\"https:\/\/www.microsoft.com\/en-us\/research\/video\/msr-nyc-data-science-seminar-series-4-what-makes-us-human-machine-learning-challenges-in-digital-advertising\/\" target=\"_self\">here<\/a><\/p>\n<p><strong>Speaker:<\/strong> <a class=\"msr-external-link glyph-append glyph-append-open-in-new-tab glyph-append-xsmall\" rel=\"noopener noreferrer\" target=\"_blank\" href=\"http:\/\/people.stern.nyu.edu\/cperlich\/\">Claudia Perlich<span class=\"sr-only\"> (opens in new tab)<\/span><\/a>, Dstillery & NYU Stern<\/p>\n<p><b>Title:<\/b> <strong>What makes us human? Machine learning challenges in digital advertising<\/strong><\/p>\n<p><b>Abstract:<\/b> Digital advertising is one of the largest and open playgrounds for machine learning, data mining and related analytic approaches. This talk will touch on a number of challenges which arise in this environment: 1) high volume data streams of around 30 Billion daily consumer touch points, 2) low latency requirements on scoring and automated bidding decisioning within 100ms and 3) adversarial modeling in the light of advertising fraud and bots. Specifically, we will discuss an automated learning system implemented at Dstillery, that uses privacy friendly data representation to build sparse targeting models for thousands of products in Millions of dimensions. The solution incorporates ideas from transfer learning, Bayesian priors, stochastic gradient descent, hashing and learning rate estimation. On the sidelines, but of no less importance, are topics on bid optimization, data reliability, cross-device identification and observational methods for causal inference. Finally, I will touch on a few higher-level lessons around incentive misalignments\/measurement issues in the advertising industry and pose the paradox of big data and predictive modeling: You never have the data you need.<\/p>\n<p><b>Bio:<\/b> Claudia Perlich currently acts as Chief Scientist at Dstillery and designs, develops, analyzes and optimizes the machine learning that drives digital advertising. An active industry speaker and frequent contributor to academic and industry publications, Claudia was recently named winner of the Advertising Research Foundation\u2019s (ARF) Grand Innovation Award, was selected as member of the Crain\u2019s NY annual 40 Under 40 list, WIRED\u2019s Smart List, and FastCompany\u2019s 100 Most Creative People. She has published over 50 scientific articles, and holds multiple patents in machine learning. Claudia has a PhD in Information Systems from NYU and worked in the Predictive Modeling Group at IBM\u2019s Watson Research Center, concentrating on data analytics and machine learning for real-world applications. She also teaches in the NYU Stern MBA program.<\/p>\n<p><span id=\"label-external-link\" class=\"sr-only\" aria-hidden=\"true\">Opens in a new tab<\/span><\/p>\n\t\t\t<\/div>\n\t\t<\/div>\n\t<\/li>\n\t\t<li class=\"m-0\" data-wp-context='{\"id\":\"accordion-content-6240\"}' data-wp-init=\"callbacks.init\">\n\t\t<div class=\"accordion-header\">\n\t\t\t<button\n\t\t\t\taria-controls=\"accordion-content-6240\"\n\t\t\t\tclass=\"btn btn-collapse\"\n\t\t\t\tdata-wp-bind--aria-expanded=\"state.isExpanded\"\n\t\t\t\tdata-wp-on--click=\"actions.onClick\"\n\t\t\t\tid=\"accordion-button-6239\"\n\t\t\t\ttype=\"button\"\n\t\t\t>\n\t\t\t\tDecember 4th, 2014\t\t\t<\/button>\n\t\t<\/div>\n\t\t<div\n\t\t\taria-labelledby=\"accordion-button-6239\"\n\t\t\tclass=\"msr-accordion__content\"\n\t\t\tdata-wp-bind--inert=\"!state.isExpanded\"\n\t\t\tdata-wp-run=\"callbacks.run\"\n\t\t\tid=\"accordion-content-6240\"\n\t\t>\n\t\t\t<div class=\"msr-accordion__body\">\n\t\t\t\t<p>You can view the recording of the talk <a class=\"invalidLink\" href=\"https:\/\/www.microsoft.com\/en-us\/research\/video\/msr-nyc-data-science-seminar-series-3-topic-models-and-user-behavior\/\" target=\"_self\">here<\/a><\/p>\n<p><strong>Speaker:\u00a0<\/strong><a class=\"msr-external-link glyph-append glyph-append-open-in-new-tab glyph-append-xsmall\" rel=\"noopener noreferrer\" href=\"http:\/\/www.cs.columbia.edu\/~blei\/\" target=\"_blank\">David Blei<span class=\"sr-only\"> (opens in new tab)<\/span><\/a>, Columbia University<\/p>\n<p><strong>Title:<\/strong> <strong>Topic Models and User Behavior<\/strong><\/p>\n<p><strong>Abstract:<\/strong> Probabilistic topic models provide a suite of tools for analyzinglarge document collections. Topic modeling algorithms discover thelatent themes that underlie the documents and identify how eachdocument exhibits those themes. Topic modeling can be used to helpexplore, summarize, and form predictions about documents. Topicmodeling ideas have been adapted to many domains, including images,music, networks, genomics, and neuroscience.<\/p>\n<p>Traditional topic modeling algorithms analyze a document collectionand estimate its latent thematic structure. However, many collectionscontain an additional type of data: how people use the documents. Forexample, readers click on articles in a newspaper website, scientistsplace articles in their personal libraries, and lawmakers vote on acollection of bills. Behavior data is essential both for makingpredictions about users (such as for a recommendation system) and forunderstanding how a collection and its users are organized.<\/p>\n<p>In this talk, I will review the basics of topic modeling and describeour recent research on collaborative topic models, models thatsimultaneously analyze a collection of texts and its correspondinguser behavior. We studied collaborative topic models on 80,000scientists&#8217; libraries from Mendeley and 100,000 users&#8217; click data fromthe arXiv. Collaborative topic models enable interpretablerecommendation systems, capturing scientists&#8217; preferences and pointingthem to articles of interest. Further, these models can organize thearticles according to the discovered patterns of readership. Forexample, we can identify articles that are important within a fieldand articles that transcend disciplinary boundaries.<\/p>\n<p>More broadly, topic modeling is a case study in the large field ofapplied probabilistic modeling. Finally, I will survey some recentadvances in this field. I will show how modern probabilistic modelinggives data scientists a rich language for expressing statisticalassumptions and scalable algorithms for uncovering hidden patterns inmassive data.<\/p>\n<p><strong>Bio:<\/strong> David Blei is a Professor of Statistics and Computer Science atColumbia University. His research is in statistical machine learning,involving probabilistic topic models, Bayesian nonparametric methods,and approximate posterior inference. He works on a variety ofapplications, including text, images, music, social networks, userbehavior, and scientific data.David earned his Bachelor&#8217;s degree in Computer Science and Mathematics from Brown University (1997) and his PhD in Computer Science from theUniversity of California, Berkeley (2004). Before arriving toColumbia, he was an Associate Professor of Computer Science atPrinceton University. He has received several awards for hisresearch, including a Sloan Fellowship (2010), Office of NavalResearch Young Investigator Award (2011), Presidential Early CareerAward for Scientists and Engineers (2011), Blavatnik Faculty Award(2013), and ACM-Infosys Foundation Award (2013).<\/p>\n<p><span id=\"label-external-link\" class=\"sr-only\" aria-hidden=\"true\">Opens in a new tab<\/span><\/p>\n\t\t\t<\/div>\n\t\t<\/div>\n\t<\/li>\n\t\t<li class=\"m-0\" data-wp-context='{\"id\":\"accordion-content-6242\"}' data-wp-init=\"callbacks.init\">\n\t\t<div class=\"accordion-header\">\n\t\t\t<button\n\t\t\t\taria-controls=\"accordion-content-6242\"\n\t\t\t\tclass=\"btn btn-collapse\"\n\t\t\t\tdata-wp-bind--aria-expanded=\"state.isExpanded\"\n\t\t\t\tdata-wp-on--click=\"actions.onClick\"\n\t\t\t\tid=\"accordion-button-6241\"\n\t\t\t\ttype=\"button\"\n\t\t\t>\n\t\t\t\tSeptember 23, 2014\t\t\t<\/button>\n\t\t<\/div>\n\t\t<div\n\t\t\taria-labelledby=\"accordion-button-6241\"\n\t\t\tclass=\"msr-accordion__content\"\n\t\t\tdata-wp-bind--inert=\"!state.isExpanded\"\n\t\t\tdata-wp-run=\"callbacks.run\"\n\t\t\tid=\"accordion-content-6242\"\n\t\t>\n\t\t\t<div class=\"msr-accordion__body\">\n\t\t\t\t<p>You can view the recording of the talk <a class=\"invalidLink\" title=\"\" href=\"https:\/\/www.microsoft.com\/en-us\/research\/video\/msr-nyc-data-science-seminar-series-2\/\" target=\"_self\">here<\/a><\/p>\n<p><strong>Speaker:<\/strong> <a class=\"msr-external-link glyph-append glyph-append-open-in-new-tab glyph-append-xsmall\" rel=\"noopener noreferrer\" target=\"_blank\" href=\"http:\/\/tech.cornell.edu\/deborah-estrin\/\">Deborah Estrin<span class=\"sr-only\"> (opens in new tab)<\/span><\/a>, Cornell Tech<\/p>\n<p><strong>Title: Small, n=me, data<\/strong><\/p>\n<p><strong>Abstract:<\/strong> Consider a new kind of cloud-based app that would create a picture of an individuals life over time by continuously, securely, and privately analyzing the digital traces they generate 24&#215;7. The social networks, search engines, mobile operators, online games, and e-commerce sites that they access every hour of most every day extensively use these digital traces to tailor service offerings and to improve system performance and in some cases to target advertisements. Our premise is that these diverse and messy, but highly personalized, data can be analyzed to draw powerful inferences about an individual, and for that individual. Use of applications that are fueled by these traces could enhance, and even transform, our experiences as consumers, patients, passengers, customers, family members, as well as users of online media. This talk will discuss precedents for small data in mobile health, and the opportunities and challenges of broadening the scope of small data capture, storage, and use.<\/p>\n<p><strong>Bio:<\/strong> Deborah Estrin (PhD, MIT (1985); BS, UCB (1980)) is a Professor of Computer Science at Cornell Tech in New York City (<a class=\"msr-external-link glyph-append glyph-append-open-in-new-tab glyph-append-xsmall\" rel=\"noopener noreferrer\" title=\"\" href=\"http:\/\/tech.cornell.edu\/deborah-estrin\" target=\"_blank\">http:\/\/tech.cornell.edu\/deborah-estrin<span class=\"sr-only\"> (opens in new tab)<\/span><\/a>) and a Professor of Health Policy and Research at Weill Cornell Medical College. She is a co-founder of Open mHealth (<a class=\"msr-external-link glyph-append glyph-append-open-in-new-tab glyph-append-xsmall\" rel=\"noopener noreferrer\" target=\"_blank\" href=\"http:\/\/openmhealth.org\/\">http:\/\/openmhealth.org<span class=\"sr-only\"> (opens in new tab)<\/span><\/a>\/). Her current focus is on mobile health and small data, leveraging the pervasiveness of mobile devices and digital interactions for health and life management (TEDMED <a class=\"msr-external-link glyph-append glyph-append-open-in-new-tab glyph-append-xsmall\" rel=\"noopener noreferrer\" target=\"_blank\" href=\"https:\/\/smalldata.tech.cornell.edu\/narrative.php\">https:\/\/smalldata.tech.cornell.edu\/<span class=\"sr-only\"> (opens in new tab)<\/span><\/a>). Estrin was the founding director of the NSF-funded Science and Technology Center for Embedded Networked Sensing (CENS) at UCLA (2002-12). Awards include: ACM Athena Lecturer (2006) and Anita Borg Institute&#8217;s Women of Vision Award for Innovation (2007). She is an elected member of the American Academy of Arts and Sciences (2007) and National Academy of Engineering (2009).<\/p>\n<p><span id=\"label-external-link\" class=\"sr-only\" aria-hidden=\"true\">Opens in a new tab<\/span><\/p>\n\t\t\t<\/div>\n\t\t<\/div>\n\t<\/li>\n\t\t<li class=\"m-0\" data-wp-context='{\"id\":\"accordion-content-6244\"}' data-wp-init=\"callbacks.init\">\n\t\t<div class=\"accordion-header\">\n\t\t\t<button\n\t\t\t\taria-controls=\"accordion-content-6244\"\n\t\t\t\tclass=\"btn btn-collapse\"\n\t\t\t\tdata-wp-bind--aria-expanded=\"state.isExpanded\"\n\t\t\t\tdata-wp-on--click=\"actions.onClick\"\n\t\t\t\tid=\"accordion-button-6243\"\n\t\t\t\ttype=\"button\"\n\t\t\t>\n\t\t\t\tApril 24, 2014\t\t\t<\/button>\n\t\t<\/div>\n\t\t<div\n\t\t\taria-labelledby=\"accordion-button-6243\"\n\t\t\tclass=\"msr-accordion__content\"\n\t\t\tdata-wp-bind--inert=\"!state.isExpanded\"\n\t\t\tdata-wp-run=\"callbacks.run\"\n\t\t\tid=\"accordion-content-6244\"\n\t\t>\n\t\t\t<div class=\"msr-accordion__body\">\n\t\t\t\t<h2>Opening Event<\/h2>\n<div class=\"conM \">\n<p>You can view the talks and the panel discussion <a class=\"invalidLink\" href=\"https:\/\/www.microsoft.com\/en-us\/research\/video\/msr-nyc-data-science-seminar-series\/\" target=\"_self\">here<\/a>.<\/p>\n<hr \/>\n<h2>Opening Remarks<\/h2>\n<p>Speaker: <a class=\"invalidLink\" href=\"https:\/\/www.microsoft.com\/en-us\/research\/people\/duncan\/\" target=\"_self\">Duncan Watts<\/a><\/p>\n<p><strong>Bio:<\/strong> Prior to joining Microsoft, Duncan Watts was a Senior Principal Research Scientist at Yahoo! Research, where he directed the Human Social Dynamics group. Prior to joining Yahoo!, he was a full professor of Sociology at Columbia University, where he taught from 2000-2007. His research on social networks and collective dynamics has appeared in a wide range of journals, from Nature, Science, and Physical Review Letters to the American Journal of Sociology and Harvard Business Review. He is also the author of three books, most recently Everything is Obvious (Once You Know The Answer) (Crown Business, 2011). He holds a B.Sc. in Physics from the Australian Defense Force Academy, and a Ph.D. in Theoretical and Applied Mechanics from Cornell University.<\/p>\n<hr \/>\n<h2>Technical Talks<\/h2>\n<p><a class=\"msr-external-link glyph-append glyph-append-open-in-new-tab glyph-append-xsmall\" rel=\"noopener noreferrer\" href=\"http:\/\/yann.lecun.com\/\" target=\"_blank\">Yann LeCun<span class=\"sr-only\"> (opens in new tab)<\/span><\/a>, NYU and Facebook<\/p>\n<p><strong>Title & Abstract: Yann presents a demo on deep learning and vision.\u00a0<\/strong><\/p>\n<p><strong>Bio:<\/strong> Yann is Director of AI Research at Facebook, and Silver Professor of Dara Science, Computer Science, Neural Science, and Electrical Engineering at New York University, affiliated with the NYU Center for Data Science, the Courant Institute of Mathematical Science, the Center for Neural Science, and the Electrical and Computer Engineering Department. He received the Electrical Engineer Diploma from Ecole Superieure d&#8217;Ingenieurs en Electrotechnique et Electronique (ESIEE), Paris in 1983, and a PhD in Computer Science from Universite Pierre et Marie Curie (Paris) in 1987. He is the lead faculty at NYU for the Moore-Sloan Data Science Environment, a $36M initiative in collaboration with UC Berkeley and University of Washington to develop data-driven methods in the sciences. He is the recipient of the 2014 IEEE Neural Network Pioneer Award.<\/p>\n<hr \/>\n<p><a class=\"msr-external-link glyph-append glyph-append-open-in-new-tab glyph-append-xsmall\" rel=\"noopener noreferrer\" href=\"http:\/\/mornaaman.com\/\" target=\"_blank\">Mor Naaman<span class=\"sr-only\"> (opens in new tab)<\/span><\/a>, Cornell Tech<\/p>\n<p><b>Title: Data and People in Connective Media<\/b><\/p>\n<p><b>Abstract: <\/b>In five minutes or less, I will talk about how we use methods from social science, people-centered design, data science and machine learning to understand social media data large and small, and build new applications that help us make sense of the city from (public) social media data. I&#8217;ll also say a word about Cornell Tech and our Connective Media hub. OK, six minutes may be needed to squeeze it all in.<\/p>\n<p><b>Bio:<\/b> Naaman is an associate professor at Cornell Tech&#8217;s Jacobs Institute. He is also a co-founder and Chief Scientist at Seen.co, a startup founded to make sense of the real-time web and social media. Mor&#8217;s research applies multidisciplinary methods to gain new insights about people and society from social media data, and to develop novel tools to make this data more accessible and usable in various settings. He gets awards, too, including the NSF Early Faculty CAREER Award, research awards from Google, Yahoo!, and Nokia, and three best paper awards.<\/p>\n<hr \/>\n<p><a class=\"msr-external-link glyph-append glyph-append-open-in-new-tab glyph-append-xsmall\" rel=\"noopener noreferrer\" href=\"http:\/\/www1.cs.columbia.edu\/~jebara\/\" target=\"_blank\">Tony Jebara<span class=\"sr-only\"> (opens in new tab)<\/span><\/a>, Columbia University<\/p>\n<p><strong>Title:\u00a0Learning From Network Connectivity and Mobile Phone Data<\/strong><\/p>\n<p><strong>Abstract: <\/strong>Many real-world networks are described by both connectivity information as well as features for every node. While most network growth models are based on link analysis, we explore how an individual&#8217;s data profile without any connectivity information can be used to infer their connectivity with other users. For example, in a class of incoming freshmen students with no known friendship connections, can we predict which pairs will become friends at the end of the year using only their profile information? Similarly, can we using co-location to predict communication? In other words, by observing only the mobile location data from users, can we predict what pairs of users are likely to communicate? To learn how to reconstruct these networks, we present structure-preserving metric learning and apply it to Facebook data, Wikipedia data, FourSquare data and mobile phone call detail records,<\/p>\n<p><strong>Bio:<\/strong> Tony is Associate Professor of Computer Science at Columbia University. He chairs the Center on Foundations of Data Science as well as directs the Columbia Machine Learning Laboratory. His research intersects computer science and statistics to develop new frameworks for learning from data with applications in social networks, spatio-temporal data, vision and text. Jebara has founded or advised startups including Sense Networks (acquired by yp.com), AchieveMint, Agolo, and Bookt (acquired by RealPage NASDAQ:RP). He is the author of the book Machine Learning: Discriminative and Generative. In 2004, Jebara was the recipient of the Career award from the National Science Foundation.<\/p>\n<hr \/>\n<h2>Panel Discussion<\/h2>\n<p><strong>Panel Topic:<\/strong> <strong>Opportunities and Challenges in Data Science Research<\/strong><\/p>\n<p><strong>Panel Moderator:<\/strong> <a class=\"invalidLink\" href=\"https:\/\/www.microsoft.com\/en-us\/research\/people\/jchayes\/\" target=\"_self\">Jennifer Chayes<\/a>, Managing Director, MSR New York City<\/p>\n<p><strong>Bio:<\/strong> Jennifer Tour Chayes is Managing Director of Microsoft Research New York City as well as the Microsoft Research New England lab in Cambridge. Before this, she was research area manager for Mathematics, Theoretical Computer Science and Cryptography at Microsoft Research Redmond. Chayes joined Microsoft Research in 1997, when she co-founded the Theory Group. Her research areas include phase transitions in discrete mathematics and computer science, structural and dynamical properties of self-engineered networks, and algorithmic game theory. She is the co-author of almost 100 scientific papers and the co-inventor of more than 20 patents.<\/p>\n<p><strong>Panel Members:<\/strong> Yann LeCun, Mor Naaman, Tony Jebara<\/p>\n<p><span id=\"label-external-link\" class=\"sr-only\" aria-hidden=\"true\">Opens in a new tab<\/span><\/p>\n\t\t\t<\/div>\n\t\t<\/div>\n\t<\/li>\n\t<\/div\t\t\t\t\t<\/ul>\n\t<\/div>\n\t<\/p>\n<\/div>\n<p><span id=\"label-external-link\" class=\"sr-only\" aria-hidden=\"true\">Opens in a new tab<\/span><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Venue: Microsoft Research New York (641 Avenue of the Americas, New York (opens in new tab)) Host: Ceren Budak, Post Doc ResearcherOpens in a new tab This seminar series is bringing data science researchers from Columbia University, NYU, Cornell Tech and Microsoft Research together. Our goal is to increase interactions within the broader New York [&hellip;]<\/p>\n","protected":false},"featured_media":0,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"_classifai_error":"","msr_startdate":"2014-04-24","msr_enddate":"2016-04-24","msr_location":"New York, NY, U.S.","msr_expirationdate":"","msr_event_recording_link":"","msr_event_link":"","msr_event_link_redirect":false,"msr_event_time":"","msr_hide_region":false,"msr_private_event":true,"msr_hide_image_in_river":0,"footnotes":""},"research-area":[13556,13563,13548,13555,13559],"msr-region":[197900],"msr-event-type":[197941],"msr-video-type":[],"msr-locale":[268875],"msr-program-audience":[],"msr-post-option":[],"msr-impact-theme":[],"class_list":["post-199989","msr-event","type-msr-event","status-publish","hentry","msr-research-area-artificial-intelligence","msr-research-area-data-platform-analytics","msr-research-area-economics","msr-research-area-search-information-retrieval","msr-research-area-social-sciences","msr-region-north-america","msr-event-type-conferences","msr-locale-en_us"],"msr_about":"<!-- wp:msr\/event-details {\"title\":\"MSR NYC Data Science Seminar Series\",\"backgroundColor\":\"grey\"} \/-->\n\n<!-- wp:msr\/content-tabs --><!-- wp:msr\/content-tab {\"title\":\"Summary\"} --><!-- wp:freeform --><p><strong>Venue:<\/strong> Microsoft Research New York (<a class=\"msr-external-link glyph-append glyph-append-open-in-new-tab glyph-append-xsmall\" href=\"http:\/\/www.bing.com\/maps\/#Y3A9NDcuNjgwMDk5fi0xMjIuMTIwNTk4Jmx2bD00JnN0eT1yJnE9NjQxJTIwQXZlbnVlJTIwb2YlMjB0aGUlMjBBbWVyaWNhcyUyQyUyME5ldyUyMFlvcmslMkMlMjBVbml0ZWQlMjBTdGF0ZXM=\" target=\"_blank\">641 Avenue of the Americas, New York<span class=\"sr-only\"> (opens in new tab)<\/span><\/a>)<\/p>\n<p><strong>Host:<\/strong> Ceren Budak, Post Doc Researcher<span id=\"label-external-link\" class=\"sr-only\" aria-hidden=\"true\">Opens in a new tab<\/span><\/p>\n<p>This seminar series is bringing data science researchers from Columbia University, NYU, Cornell Tech and Microsoft Research together. Our goal is to increase interactions within the broader New York data science community, and to provide a new forum for discussions on data science research.<\/p>\n<h2>Format<\/h2>\n<p>The events in this series start with a formal talk session (45 minutes). Invited speakers present short talks, providing their views on the opportunities and challenges in data science research. The second part of the event (2 hours) is a wine and cheese social designed to enable researchers to exchange ideas in a relaxed setting.<span id=\"label-external-link\" class=\"sr-only\" aria-hidden=\"true\">Opens in a new tab<\/span><\/p>\n<!-- \/wp:freeform --><!-- \/wp:msr\/content-tab --><!-- wp:msr\/content-tab {\"title\":\"Speakers\"} --><!-- wp:freeform --><h3>Microsoft<\/h3>\n<p>Ceren Budak, Post Doc Researcher<\/p>\n<p><a href=\"https:\/\/www.microsoft.com\/en-us\/research\/people\/duncan\/\">Duncan Watts<span class=\"sr-only\"> (opens in new tab)<\/span><\/a>, Principal Researcher<\/p>\n<p><a href=\"https:\/\/www.microsoft.com\/en-us\/research\/people\/jchayes\/\">Jennifer Chayes<span class=\"sr-only\"> (opens in new tab)<\/span><\/a>, Distinguished Scientist \/ Managing Director, Microsoft Research New England &amp; New York City<\/p>\n<h3>Other<\/h3>\n<p><a class=\"msr-external-link glyph-append glyph-append-open-in-new-tab glyph-append-xsmall\" target=\"_blank\" href=\"http:\/\/www.cis.upenn.edu\/~mkearns\/\">Michael Kearns<span class=\"sr-only\"> (opens in new tab)<\/span><\/a>, University of Pennsylvania<\/p>\n<p><a class=\"msr-external-link glyph-append glyph-append-open-in-new-tab glyph-append-xsmall\" target=\"_blank\" href=\"http:\/\/www.soc.cornell.edu\/faculty\/macy.html\">Michael Macy<span class=\"sr-only\"> (opens in new tab)<\/span><\/a>, Cornell University<\/p>\n<p><a class=\"msr-external-link glyph-append glyph-append-open-in-new-tab glyph-append-xsmall\" target=\"_blank\" href=\"http:\/\/people.stern.nyu.edu\/cperlich\/\">Claudia Perlich<span class=\"sr-only\"> (opens in new tab)<\/span><\/a>, Dstillery; NYU Stern<\/p>\n<p><a class=\"msr-external-link glyph-append glyph-append-open-in-new-tab glyph-append-xsmall\" target=\"_blank\" href=\"http:\/\/www.cs.columbia.edu\/~blei\/\">David Blei<span class=\"sr-only\"> (opens in new tab)<\/span><\/a>, Columbia University<\/p>\n<p><a class=\"msr-external-link glyph-append glyph-append-open-in-new-tab glyph-append-xsmall\" target=\"_blank\" href=\"http:\/\/tech.cornell.edu\/deborah-estrin\/\">Deborah Estrin<span class=\"sr-only\"> (opens in new tab)<\/span><\/a>, Cornell Tech<\/p>\n<p><a class=\"msr-external-link glyph-append glyph-append-open-in-new-tab glyph-append-xsmall\" target=\"_blank\" href=\"http:\/\/yann.lecun.com\/\">Yann LeCun<span class=\"sr-only\"> (opens in new tab)<\/span><\/a>, NYU and Facebook<\/p>\n<p><a class=\"msr-external-link glyph-append glyph-append-open-in-new-tab glyph-append-xsmall\" target=\"_blank\" href=\"http:\/\/mornaaman.com\/\">Mor Naaman<span class=\"sr-only\"> (opens in new tab)<\/span><\/a>, Cornell Tech<\/p>\n<p><a class=\"msr-external-link glyph-append glyph-append-open-in-new-tab glyph-append-xsmall\" target=\"_blank\" href=\"http:\/\/www1.cs.columbia.edu\/~jebara\/\">Tony Jebara<span class=\"sr-only\"> (opens in new tab)<\/span><\/a>, Columbia University<span id=\"label-external-link\" class=\"sr-only\" aria-hidden=\"true\">Opens in a new tab<\/span><\/p>\n<!-- \/wp:freeform --><!-- \/wp:msr\/content-tab --><!-- wp:msr\/content-tab {\"title\":\"Videos\"} --><!-- wp:freeform --><p><a href=\"https:\/\/www.microsoft.com\/en-us\/research\/video\/msr-nyc-data-science-seminar-series\/\">MSR NYC Data Science Seminar Series<\/a><\/p>\n<p><a href=\"https:\/\/www.microsoft.com\/en-us\/research\/video\/msr-nyc-data-science-seminar-series-2\/\">MSR NYC Data Science Seminar Series #2<\/a><\/p>\n<p><a href=\"https:\/\/www.microsoft.com\/en-us\/research\/video\/msr-nyc-data-science-seminar-series-3-topic-models-and-user-behavior\/\">MSR NYC Data Science Seminar Series #3 &#8211; Topic Models and User Behavior<\/a><\/p>\n<p><a href=\"https:\/\/www.microsoft.com\/en-us\/research\/video\/msr-nyc-data-science-seminar-series-4-what-makes-us-human-machine-learning-challenges-in-digital-advertising\/\">MSR NYC Data Science Seminar Series #4 &#8211; What Makes us Human? Machine Learning Challenges in Digital Advertising<\/a><span id=\"label-external-link\" class=\"sr-only\" aria-hidden=\"true\">Opens in a new tab<\/span><\/p>\n<!-- \/wp:freeform --><!-- \/wp:msr\/content-tab --><!-- wp:msr\/content-tab {\"title\":\"Previous Events\"} --><!-- wp:freeform --><div class=\"conM \">\n<p>\t<div data-wp-context='{\"items\":[]}' data-wp-interactive=\"msr\/accordion\">\n\t\t\t\t\t<div class=\"clearfix\">\n\t\t\t\t<div\n\t\t\t\t\tclass=\"btn-group align-items-center mb-g float-sm-right\"\n\t\t\t\t\tdata-bi-aN=\"accordion-collapse-controls\"\n\t\t\t\t>\n\t\t\t\t\t<button\n\t\t\t\t\t\tclass=\"btn btn-link m-0\"\n\t\t\t\t\t\tdata-bi-cN=\"Expand all\"\n\t\t\t\t\t\tdata-wp-bind--aria-controls=\"state.ariaControls\"\n\t\t\t\t\t\tdata-wp-bind--aria-expanded=\"state.ariaExpanded\"\n\t\t\t\t\t\tdata-wp-bind--disabled=\"state.isAllExpanded\"\n\t\t\t\t\t\tdata-wp-class--inactive=\"state.isAllExpanded\"\n\t\t\t\t\t\tdata-wp-on--click=\"actions.onExpandAll\"\n\t\t\t\t\t\ttype=\"button\"\n\t\t\t\t\t>\n\t\t\t\t\t\tExpand all\t\t\t\t\t<\/button>\n\t\t\t\t\t<span aria-hidden=\"true\"> | <\/span>\n\t\t\t\t\t<button\n\t\t\t\t\t\tclass=\"btn btn-link m-0\"\n\t\t\t\t\t\tdata-bi-cN=\"Collapse all\"\n\t\t\t\t\t\tdata-wp-bind--aria-controls=\"state.ariaControls\"\n\t\t\t\t\t\tdata-wp-bind--aria-expanded=\"state.ariaExpanded\"\n\t\t\t\t\t\tdata-wp-bind--disabled=\"state.isAllCollapsed\"\n\t\t\t\t\t\tdata-wp-class--inactive=\"state.isAllCollapsed\"\n\t\t\t\t\t\tdata-wp-on--click=\"actions.onCollapseAll\"\n\t\t\t\t\t\ttype=\"button\"\n\t\t\t\t\t>\n\t\t\t\t\t\tCollapse all\t\t\t\t\t<\/button>\n\t\t\t\t<\/div>\n\t\t\t<\/div>\n\t\t\t\t<ul class=\"msr-accordion\">\n\t\t\t\t\t\t\t\t<li class=\"m-0\" data-wp-context='{\"id\":\"accordion-content-6234\"}' data-wp-init=\"callbacks.init\">\n\t\t<div class=\"accordion-header\">\n\t\t\t<button\n\t\t\t\taria-controls=\"accordion-content-6234\"\n\t\t\t\tclass=\"btn btn-collapse\"\n\t\t\t\tdata-wp-bind--aria-expanded=\"state.isExpanded\"\n\t\t\t\tdata-wp-on--click=\"actions.onClick\"\n\t\t\t\tid=\"accordion-button-6233\"\n\t\t\t\ttype=\"button\"\n\t\t\t>\n\t\t\t\tJune 25, 2015\t\t\t<\/button>\n\t\t<\/div>\n\t\t<div\n\t\t\taria-labelledby=\"accordion-button-6233\"\n\t\t\tclass=\"msr-accordion__content\"\n\t\t\tdata-wp-bind--inert=\"!state.isExpanded\"\n\t\t\tdata-wp-run=\"callbacks.run\"\n\t\t\tid=\"accordion-content-6234\"\n\t\t>\n\t\t\t<div class=\"msr-accordion__body\">\n\t\t\t\t<p><strong>Speaker:<\/strong> <a class=\"msr-external-link glyph-append glyph-append-open-in-new-tab glyph-append-xsmall\" target=\"_blank\" href=\"http:\/\/www.cis.upenn.edu\/~mkearns\/\">Michael Kearns<\/a>, University of Pennsylvania<\/p>\n<p><b>Title:<\/b> <strong>From \u201cIn\u201d to \u201cOver\u201d: Behavioral Experiments on Whole-Network Computation<\/strong><\/p>\n<p><b>Abstract:<\/b>\u00a0 We report on a series of behavioral experiments in human computation on three different tasks over networks: graph coloring, community detection (or graph clustering), and competitive contagion. While these tasks share similar action spaces and interfaces, they capture a diversity of computational challenges: graph coloring is a search problem, clustering is an optimization problem, and competitive contagion is a game-theoretic problem. In contrast with much of the recent literature on human-subject experiments in networks, in which collectives of subjects are embedded \u201cin\u201d the network, and have only local information and interactions, here individual subjects have a global (or \u201cover\u201d) view and must solve \u201cwhole network\u201d problems alone. Our primary findings are that subject performance is impressive across all three problem types; that subjects find diverse and novel strategies for solving each task; and that collective performance can often be strongly correlated with known algorithms.<\/p>\n<p>Joint work with Lili Dworkin.<\/p>\n<p><b>Bio:<\/b> Michael Kearns is a professor in the Computer and Information Science department at the University of Pennsylvania, where he holds the National Center Chair and has joint appointments in the Wharton School. He is founding director of Penn\u2019s Networked and Social Systems Engineering (NETS) program (<a class=\"msr-external-link glyph-append glyph-append-open-in-new-tab glyph-append-xsmall\" target=\"_blank\" href=\"http:\/\/www.nets.upenn.edu\/\">www.nets.upenn.edu<\/a>), and founding director of Penn&#8217;s Warren Center for Network and Data Sciences (<a class=\"msr-external-link glyph-append glyph-append-open-in-new-tab glyph-append-xsmall\" target=\"_blank\" href=\"http:\/\/www.warrencenter.upenn.edu\/\">www.warrencenter.upenn.edu<\/a>). His research interests include topics in machine learning, algorithmic game theory, social networks, and computational finance. He has worked and consulted extensively in the technology and finance industries. He is a fellow of the American Academy of Arts and Sciences, the Association for Computing Machinery, and the Association for the Advancement of Artificial Intelligence.<\/p>\n<p><span id=\"label-external-link\" class=\"sr-only\" aria-hidden=\"true\">Opens in a new tab<\/span><\/p>\n\t\t\t<\/div>\n\t\t<\/div>\n\t<\/li>\n\t\t<li class=\"m-0\" data-wp-context='{\"id\":\"accordion-content-6236\"}' data-wp-init=\"callbacks.init\">\n\t\t<div class=\"accordion-header\">\n\t\t\t<button\n\t\t\t\taria-controls=\"accordion-content-6236\"\n\t\t\t\tclass=\"btn btn-collapse\"\n\t\t\t\tdata-wp-bind--aria-expanded=\"state.isExpanded\"\n\t\t\t\tdata-wp-on--click=\"actions.onClick\"\n\t\t\t\tid=\"accordion-button-6235\"\n\t\t\t\ttype=\"button\"\n\t\t\t>\n\t\t\t\tApril 16, 2015\t\t\t<\/button>\n\t\t<\/div>\n\t\t<div\n\t\t\taria-labelledby=\"accordion-button-6235\"\n\t\t\tclass=\"msr-accordion__content\"\n\t\t\tdata-wp-bind--inert=\"!state.isExpanded\"\n\t\t\tdata-wp-run=\"callbacks.run\"\n\t\t\tid=\"accordion-content-6236\"\n\t\t>\n\t\t\t<div class=\"msr-accordion__body\">\n\t\t\t\t<p><strong>Speaker:<\/strong> <a class=\"msr-external-link glyph-append glyph-append-open-in-new-tab glyph-append-xsmall\" target=\"_blank\" href=\"http:\/\/www.soc.cornell.edu\/faculty\/macy.html\">Michael Macy<\/a>, Cornell University<\/p>\n<p><b>Title:<\/b> <strong>On A Scale From 1 to 5, How Much Confidence Do You Have in Survey Results?<\/strong><\/p>\n<p><b>Abstract:<\/b> From astronomy to neuroscience to particle physics, scientific knowledge depends heavily on the available tools for observation. Since the introduction of stratified sampling in 1934, the survey has been the single most important observational tool for social science. During this time, impressive advances have taken place in our ability to reduce sampling error (e.g. Respondent Driven Sampling), measurement error (e.g. SEM), specification error (e.g. linear mixed models), and inferential error (e.g. Propensity Score Matching). Nevertheless, increasing confidence in survey technology has paradoxically reinforced a debilitating theoretical blinder that has compromised the ability of social science to elicit confidence in predictions. What is worse, this blinder has largely escaped notice through a combination of ideological bias and reluctance to pull back the covers on problems for which we have no solution. The good news is that a solution is finally on the horizon, with the potential for social science to begin to close the gap with the physical and life sciences in predictive ability. The bad news is \u2026 (to be continued).<\/p>\n<p><b>Bio:<\/b> Michael Macy earned his B.A. and Ph.D from Harvard, along with an M.A. from Stanford. He is currently Goldwin Smith Professor of Arts and Sciences and Director of the Social Dynamics Laboratory at Cornell, with a dual appointment in the Departments of Sociology and Information Science. With support from the National Science Foundation, the Department of Defense, and Google, his research team has used computational models, online laboratory experiments, and digital traces of device-mediated interaction to explore familiar but enigmatic social patterns, such as circadian rhythms, the emergence and collapse of fads, the spread of self-destructive behaviors, cooperation in social dilemmas, the critical mass in collective action, the spread of high-threshold contagions on small-world networks, the polarization of opinion, segregation of neighborhoods, and assimilation of minority cultures. Recent research uses 509 million Twitter messages to track diurnal and seasonal mood changes in 54 countries, telephone logs for 12B calls in the UK to measure the economic correlates of network structure, and hundreds of millions of Yahoo! email logs in 90 countries to test Huntington\u2019s theory of the &#8220;clash of civilizations.&#8221; His research has been published in leading journals, including Science, PNAS, American Journal of Sociology, American Sociological Review, and Annual Review of Sociology.<\/p>\n<p><span id=\"label-external-link\" class=\"sr-only\" aria-hidden=\"true\">Opens in a new tab<\/span><\/p>\n\t\t\t<\/div>\n\t\t<\/div>\n\t<\/li>\n\t\t<li class=\"m-0\" data-wp-context='{\"id\":\"accordion-content-6238\"}' data-wp-init=\"callbacks.init\">\n\t\t<div class=\"accordion-header\">\n\t\t\t<button\n\t\t\t\taria-controls=\"accordion-content-6238\"\n\t\t\t\tclass=\"btn btn-collapse\"\n\t\t\t\tdata-wp-bind--aria-expanded=\"state.isExpanded\"\n\t\t\t\tdata-wp-on--click=\"actions.onClick\"\n\t\t\t\tid=\"accordion-button-6237\"\n\t\t\t\ttype=\"button\"\n\t\t\t>\n\t\t\t\tFebruary 5, 2015\t\t\t<\/button>\n\t\t<\/div>\n\t\t<div\n\t\t\taria-labelledby=\"accordion-button-6237\"\n\t\t\tclass=\"msr-accordion__content\"\n\t\t\tdata-wp-bind--inert=\"!state.isExpanded\"\n\t\t\tdata-wp-run=\"callbacks.run\"\n\t\t\tid=\"accordion-content-6238\"\n\t\t>\n\t\t\t<div class=\"msr-accordion__body\">\n\t\t\t\t<p>You can view the recording of the talk <a class=\"invalidLink\" href=\"https:\/\/www.microsoft.com\/en-us\/research\/video\/msr-nyc-data-science-seminar-series-4-what-makes-us-human-machine-learning-challenges-in-digital-advertising\/\" target=\"_self\">here<\/a><\/p>\n<p><strong>Speaker:<\/strong> <a href=\"http:\/\/people.stern.nyu.edu\/cperlich\/\">Claudia Perlich<\/a>, Dstillery &amp; NYU Stern<\/p>\n<p><b>Title:<\/b> <strong>What makes us human? Machine learning challenges in digital advertising<\/strong><\/p>\n<p><b>Abstract:<\/b> Digital advertising is one of the largest and open playgrounds for machine learning, data mining and related analytic approaches. This talk will touch on a number of challenges which arise in this environment: 1) high volume data streams of around 30 Billion daily consumer touch points, 2) low latency requirements on scoring and automated bidding decisioning within 100ms and 3) adversarial modeling in the light of advertising fraud and bots. Specifically, we will discuss an automated learning system implemented at Dstillery, that uses privacy friendly data representation to build sparse targeting models for thousands of products in Millions of dimensions. The solution incorporates ideas from transfer learning, Bayesian priors, stochastic gradient descent, hashing and learning rate estimation. On the sidelines, but of no less importance, are topics on bid optimization, data reliability, cross-device identification and observational methods for causal inference. Finally, I will touch on a few higher-level lessons around incentive misalignments\/measurement issues in the advertising industry and pose the paradox of big data and predictive modeling: You never have the data you need.<\/p>\n<p><b>Bio:<\/b> Claudia Perlich currently acts as Chief Scientist at Dstillery and designs, develops, analyzes and optimizes the machine learning that drives digital advertising. An active industry speaker and frequent contributor to academic and industry publications, Claudia was recently named winner of the Advertising Research Foundation\u2019s (ARF) Grand Innovation Award, was selected as member of the Crain\u2019s NY annual 40 Under 40 list, WIRED\u2019s Smart List, and FastCompany\u2019s 100 Most Creative People. She has published over 50 scientific articles, and holds multiple patents in machine learning. Claudia has a PhD in Information Systems from NYU and worked in the Predictive Modeling Group at IBM\u2019s Watson Research Center, concentrating on data analytics and machine learning for real-world applications. She also teaches in the NYU Stern MBA program.<\/p>\n<p><span id=\"label-external-link\" class=\"sr-only\" aria-hidden=\"true\">Opens in a new tab<\/span><\/p>\n\t\t\t<\/div>\n\t\t<\/div>\n\t<\/li>\n\t\t<li class=\"m-0\" data-wp-context='{\"id\":\"accordion-content-6240\"}' data-wp-init=\"callbacks.init\">\n\t\t<div class=\"accordion-header\">\n\t\t\t<button\n\t\t\t\taria-controls=\"accordion-content-6240\"\n\t\t\t\tclass=\"btn btn-collapse\"\n\t\t\t\tdata-wp-bind--aria-expanded=\"state.isExpanded\"\n\t\t\t\tdata-wp-on--click=\"actions.onClick\"\n\t\t\t\tid=\"accordion-button-6239\"\n\t\t\t\ttype=\"button\"\n\t\t\t>\n\t\t\t\tDecember 4th, 2014\t\t\t<\/button>\n\t\t<\/div>\n\t\t<div\n\t\t\taria-labelledby=\"accordion-button-6239\"\n\t\t\tclass=\"msr-accordion__content\"\n\t\t\tdata-wp-bind--inert=\"!state.isExpanded\"\n\t\t\tdata-wp-run=\"callbacks.run\"\n\t\t\tid=\"accordion-content-6240\"\n\t\t>\n\t\t\t<div class=\"msr-accordion__body\">\n\t\t\t\t<p>You can view the recording of the talk <a class=\"invalidLink\" href=\"https:\/\/www.microsoft.com\/en-us\/research\/video\/msr-nyc-data-science-seminar-series-3-topic-models-and-user-behavior\/\" target=\"_self\">here<\/a><\/p>\n<p><strong>Speaker:\u00a0<\/strong><a href=\"http:\/\/www.cs.columbia.edu\/~blei\/\" target=\"_self\">David Blei<\/a>, Columbia University<\/p>\n<p><strong>Title:<\/strong> <strong>Topic Models and User Behavior<\/strong><\/p>\n<p><strong>Abstract:<\/strong> Probabilistic topic models provide a suite of tools for analyzinglarge document collections. Topic modeling algorithms discover thelatent themes that underlie the documents and identify how eachdocument exhibits those themes. Topic modeling can be used to helpexplore, summarize, and form predictions about documents. Topicmodeling ideas have been adapted to many domains, including images,music, networks, genomics, and neuroscience.<\/p>\n<p>Traditional topic modeling algorithms analyze a document collectionand estimate its latent thematic structure. However, many collectionscontain an additional type of data: how people use the documents. Forexample, readers click on articles in a newspaper website, scientistsplace articles in their personal libraries, and lawmakers vote on acollection of bills. Behavior data is essential both for makingpredictions about users (such as for a recommendation system) and forunderstanding how a collection and its users are organized.<\/p>\n<p>In this talk, I will review the basics of topic modeling and describeour recent research on collaborative topic models, models thatsimultaneously analyze a collection of texts and its correspondinguser behavior. We studied collaborative topic models on 80,000scientists&#8217; libraries from Mendeley and 100,000 users&#8217; click data fromthe arXiv. Collaborative topic models enable interpretablerecommendation systems, capturing scientists&#8217; preferences and pointingthem to articles of interest. Further, these models can organize thearticles according to the discovered patterns of readership. Forexample, we can identify articles that are important within a fieldand articles that transcend disciplinary boundaries.<\/p>\n<p>More broadly, topic modeling is a case study in the large field ofapplied probabilistic modeling. Finally, I will survey some recentadvances in this field. I will show how modern probabilistic modelinggives data scientists a rich language for expressing statisticalassumptions and scalable algorithms for uncovering hidden patterns inmassive data.<\/p>\n<p><strong>Bio:<\/strong> David Blei is a Professor of Statistics and Computer Science atColumbia University. His research is in statistical machine learning,involving probabilistic topic models, Bayesian nonparametric methods,and approximate posterior inference. He works on a variety ofapplications, including text, images, music, social networks, userbehavior, and scientific data.David earned his Bachelor&#8217;s degree in Computer Science and Mathematics from Brown University (1997) and his PhD in Computer Science from theUniversity of California, Berkeley (2004). Before arriving toColumbia, he was an Associate Professor of Computer Science atPrinceton University. He has received several awards for hisresearch, including a Sloan Fellowship (2010), Office of NavalResearch Young Investigator Award (2011), Presidential Early CareerAward for Scientists and Engineers (2011), Blavatnik Faculty Award(2013), and ACM-Infosys Foundation Award (2013).<\/p>\n<p><span id=\"label-external-link\" class=\"sr-only\" aria-hidden=\"true\">Opens in a new tab<\/span><\/p>\n\t\t\t<\/div>\n\t\t<\/div>\n\t<\/li>\n\t\t<li class=\"m-0\" data-wp-context='{\"id\":\"accordion-content-6242\"}' data-wp-init=\"callbacks.init\">\n\t\t<div class=\"accordion-header\">\n\t\t\t<button\n\t\t\t\taria-controls=\"accordion-content-6242\"\n\t\t\t\tclass=\"btn btn-collapse\"\n\t\t\t\tdata-wp-bind--aria-expanded=\"state.isExpanded\"\n\t\t\t\tdata-wp-on--click=\"actions.onClick\"\n\t\t\t\tid=\"accordion-button-6241\"\n\t\t\t\ttype=\"button\"\n\t\t\t>\n\t\t\t\tSeptember 23, 2014\t\t\t<\/button>\n\t\t<\/div>\n\t\t<div\n\t\t\taria-labelledby=\"accordion-button-6241\"\n\t\t\tclass=\"msr-accordion__content\"\n\t\t\tdata-wp-bind--inert=\"!state.isExpanded\"\n\t\t\tdata-wp-run=\"callbacks.run\"\n\t\t\tid=\"accordion-content-6242\"\n\t\t>\n\t\t\t<div class=\"msr-accordion__body\">\n\t\t\t\t<p>You can view the recording of the talk <a class=\"invalidLink\" title=\"\" href=\"https:\/\/www.microsoft.com\/en-us\/research\/video\/msr-nyc-data-science-seminar-series-2\/\" target=\"_self\">here<\/a><\/p>\n<p><strong>Speaker:<\/strong> <a href=\"http:\/\/tech.cornell.edu\/deborah-estrin\/\">Deborah Estrin<\/a>, Cornell Tech<\/p>\n<p><strong>Title: Small, n=me, data<\/strong><\/p>\n<p><strong>Abstract:<\/strong> Consider a new kind of cloud-based app that would create a picture of an individuals life over time by continuously, securely, and privately analyzing the digital traces they generate 24&#215;7. The social networks, search engines, mobile operators, online games, and e-commerce sites that they access every hour of most every day extensively use these digital traces to tailor service offerings and to improve system performance and in some cases to target advertisements. Our premise is that these diverse and messy, but highly personalized, data can be analyzed to draw powerful inferences about an individual, and for that individual. Use of applications that are fueled by these traces could enhance, and even transform, our experiences as consumers, patients, passengers, customers, family members, as well as users of online media. This talk will discuss precedents for small data in mobile health, and the opportunities and challenges of broadening the scope of small data capture, storage, and use.<\/p>\n<p><strong>Bio:<\/strong> Deborah Estrin (PhD, MIT (1985); BS, UCB (1980)) is a Professor of Computer Science at Cornell Tech in New York City (<a title=\"\" href=\"http:\/\/tech.cornell.edu\/deborah-estrin\" target=\"_self\">http:\/\/tech.cornell.edu\/deborah-estrin<\/a>) and a Professor of Health Policy and Research at Weill Cornell Medical College. She is a co-founder of Open mHealth (<a href=\"http:\/\/openmhealth.org\/\">http:\/\/openmhealth.org<\/a>\/). Her current focus is on mobile health and small data, leveraging the pervasiveness of mobile devices and digital interactions for health and life management (TEDMED <a href=\"https:\/\/smalldata.tech.cornell.edu\/narrative.php\">https:\/\/smalldata.tech.cornell.edu\/<\/a>). Estrin was the founding director of the NSF-funded Science and Technology Center for Embedded Networked Sensing (CENS) at UCLA (2002-12). Awards include: ACM Athena Lecturer (2006) and Anita Borg Institute&#8217;s Women of Vision Award for Innovation (2007). She is an elected member of the American Academy of Arts and Sciences (2007) and National Academy of Engineering (2009).<\/p>\n<p><span id=\"label-external-link\" class=\"sr-only\" aria-hidden=\"true\">Opens in a new tab<\/span><\/p>\n\t\t\t<\/div>\n\t\t<\/div>\n\t<\/li>\n\t\t<li class=\"m-0\" data-wp-context='{\"id\":\"accordion-content-6244\"}' data-wp-init=\"callbacks.init\">\n\t\t<div class=\"accordion-header\">\n\t\t\t<button\n\t\t\t\taria-controls=\"accordion-content-6244\"\n\t\t\t\tclass=\"btn btn-collapse\"\n\t\t\t\tdata-wp-bind--aria-expanded=\"state.isExpanded\"\n\t\t\t\tdata-wp-on--click=\"actions.onClick\"\n\t\t\t\tid=\"accordion-button-6243\"\n\t\t\t\ttype=\"button\"\n\t\t\t>\n\t\t\t\tApril 24, 2014\t\t\t<\/button>\n\t\t<\/div>\n\t\t<div\n\t\t\taria-labelledby=\"accordion-button-6243\"\n\t\t\tclass=\"msr-accordion__content\"\n\t\t\tdata-wp-bind--inert=\"!state.isExpanded\"\n\t\t\tdata-wp-run=\"callbacks.run\"\n\t\t\tid=\"accordion-content-6244\"\n\t\t>\n\t\t\t<div class=\"msr-accordion__body\">\n\t\t\t\t<h2>Opening Event<\/h2>\n<div class=\"conM \">\n<p>You can view the talks and the panel discussion <a class=\"invalidLink\" href=\"https:\/\/www.microsoft.com\/en-us\/research\/video\/msr-nyc-data-science-seminar-series\/\" target=\"_self\">here<\/a>.<\/p>\n<hr \/>\n<h2>Opening Remarks<\/h2>\n<p>Speaker: <a class=\"invalidLink\" href=\"https:\/\/www.microsoft.com\/en-us\/research\/people\/duncan\/\" target=\"_self\">Duncan Watts<\/a><\/p>\n<p><strong>Bio:<\/strong> Prior to joining Microsoft, Duncan Watts was a Senior Principal Research Scientist at Yahoo! Research, where he directed the Human Social Dynamics group. Prior to joining Yahoo!, he was a full professor of Sociology at Columbia University, where he taught from 2000-2007. His research on social networks and collective dynamics has appeared in a wide range of journals, from Nature, Science, and Physical Review Letters to the American Journal of Sociology and Harvard Business Review. He is also the author of three books, most recently Everything is Obvious (Once You Know The Answer) (Crown Business, 2011). He holds a B.Sc. in Physics from the Australian Defense Force Academy, and a Ph.D. in Theoretical and Applied Mechanics from Cornell University.<\/p>\n<hr \/>\n<h2>Technical Talks<\/h2>\n<p><a href=\"http:\/\/yann.lecun.com\/\" target=\"_self\">Yann LeCun<\/a>, NYU and Facebook<\/p>\n<p><strong>Title &amp; Abstract: Yann presents a demo on deep learning and vision.\u00a0<\/strong><\/p>\n<p><strong>Bio:<\/strong> Yann is Director of AI Research at Facebook, and Silver Professor of Dara Science, Computer Science, Neural Science, and Electrical Engineering at New York University, affiliated with the NYU Center for Data Science, the Courant Institute of Mathematical Science, the Center for Neural Science, and the Electrical and Computer Engineering Department. He received the Electrical Engineer Diploma from Ecole Superieure d&#8217;Ingenieurs en Electrotechnique et Electronique (ESIEE), Paris in 1983, and a PhD in Computer Science from Universite Pierre et Marie Curie (Paris) in 1987. He is the lead faculty at NYU for the Moore-Sloan Data Science Environment, a $36M initiative in collaboration with UC Berkeley and University of Washington to develop data-driven methods in the sciences. He is the recipient of the 2014 IEEE Neural Network Pioneer Award.<\/p>\n<hr \/>\n<p><a href=\"http:\/\/mornaaman.com\/\" target=\"_self\">Mor Naaman<\/a>, Cornell Tech<\/p>\n<p><b>Title: Data and People in Connective Media<\/b><\/p>\n<p><b>Abstract: <\/b>In five minutes or less, I will talk about how we use methods from social science, people-centered design, data science and machine learning to understand social media data large and small, and build new applications that help us make sense of the city from (public) social media data. I&#8217;ll also say a word about Cornell Tech and our Connective Media hub. OK, six minutes may be needed to squeeze it all in.<\/p>\n<p><b>Bio:<\/b> Naaman is an associate professor at Cornell Tech&#8217;s Jacobs Institute. He is also a co-founder and Chief Scientist at Seen.co, a startup founded to make sense of the real-time web and social media. Mor&#8217;s research applies multidisciplinary methods to gain new insights about people and society from social media data, and to develop novel tools to make this data more accessible and usable in various settings. He gets awards, too, including the NSF Early Faculty CAREER Award, research awards from Google, Yahoo!, and Nokia, and three best paper awards.<\/p>\n<hr \/>\n<p><a href=\"http:\/\/www1.cs.columbia.edu\/~jebara\/\" target=\"_self\">Tony Jebara<\/a>, Columbia University<\/p>\n<p><strong>Title:\u00a0Learning From Network Connectivity and Mobile Phone Data<\/strong><\/p>\n<p><strong>Abstract: <\/strong>Many real-world networks are described by both connectivity information as well as features for every node. While most network growth models are based on link analysis, we explore how an individual&#8217;s data profile without any connectivity information can be used to infer their connectivity with other users. For example, in a class of incoming freshmen students with no known friendship connections, can we predict which pairs will become friends at the end of the year using only their profile information? Similarly, can we using co-location to predict communication? In other words, by observing only the mobile location data from users, can we predict what pairs of users are likely to communicate? To learn how to reconstruct these networks, we present structure-preserving metric learning and apply it to Facebook data, Wikipedia data, FourSquare data and mobile phone call detail records,<\/p>\n<p><strong>Bio:<\/strong> Tony is Associate Professor of Computer Science at Columbia University. He chairs the Center on Foundations of Data Science as well as directs the Columbia Machine Learning Laboratory. His research intersects computer science and statistics to develop new frameworks for learning from data with applications in social networks, spatio-temporal data, vision and text. Jebara has founded or advised startups including Sense Networks (acquired by yp.com), AchieveMint, Agolo, and Bookt (acquired by RealPage NASDAQ:RP). He is the author of the book Machine Learning: Discriminative and Generative. In 2004, Jebara was the recipient of the Career award from the National Science Foundation.<\/p>\n<hr \/>\n<h2>Panel Discussion<\/h2>\n<p><strong>Panel Topic:<\/strong> <strong>Opportunities and Challenges in Data Science Research<\/strong><\/p>\n<p><strong>Panel Moderator:<\/strong> <a class=\"invalidLink\" href=\"https:\/\/www.microsoft.com\/en-us\/research\/people\/jchayes\/\" target=\"_self\">Jennifer Chayes<\/a>, Managing Director, MSR New York City<\/p>\n<p><strong>Bio:<\/strong> Jennifer Tour Chayes is Managing Director of Microsoft Research New York City as well as the Microsoft Research New England lab in Cambridge. Before this, she was research area manager for Mathematics, Theoretical Computer Science and Cryptography at Microsoft Research Redmond. Chayes joined Microsoft Research in 1997, when she co-founded the Theory Group. Her research areas include phase transitions in discrete mathematics and computer science, structural and dynamical properties of self-engineered networks, and algorithmic game theory. She is the co-author of almost 100 scientific papers and the co-inventor of more than 20 patents.<\/p>\n<p><strong>Panel Members:<\/strong> Yann LeCun, Mor Naaman, Tony Jebara<\/p>\n<p><span id=\"label-external-link\" class=\"sr-only\" aria-hidden=\"true\">Opens in a new tab<\/span><\/p>\n\t\t\t<\/div>\n\t\t<\/div>\n\t<\/li>\n\t<\/div\t\t\t\t\t<\/ul>\n\t<\/div>\n\t<\/p>\n<\/div>\n<p><span id=\"label-external-link\" class=\"sr-only\" aria-hidden=\"true\">Opens in a new tab<\/span><\/p>\n<!-- \/wp:freeform --><!-- \/wp:msr\/content-tab --><!-- \/wp:msr\/content-tabs -->","tab-content":[{"id":0,"name":"Summary","content":"This seminar series is bringing data science researchers from Columbia University, NYU, Cornell Tech and Microsoft Research together. Our goal is to increase interactions within the broader New York data science community, and to provide a new forum for discussions on data science research.\r\n<h2>Format<\/h2>\r\nThe events in this series start with a formal talk session (45 minutes). Invited speakers present short talks, providing their views on the opportunities and challenges in data science research. The second part of the event (2 hours) is a wine and cheese social designed to enable researchers to exchange ideas in a relaxed setting."},{"id":1,"name":"Speakers","content":"<h3>Microsoft<\/h3>\r\nCeren Budak, Post Doc Researcher\r\n\r\n<a href=\"https:\/\/www.microsoft.com\/en-us\/research\/people\/duncan\/\">Duncan Watts<\/a>, Principal Researcher\r\n\r\n<a href=\"https:\/\/www.microsoft.com\/en-us\/research\/people\/jchayes\/\">Jennifer Chayes<\/a>, Distinguished Scientist \/ Managing Director, Microsoft Research New England &amp; New York City\r\n<h3>Other<\/h3>\r\n<a href=\"http:\/\/www.cis.upenn.edu\/~mkearns\/\">Michael Kearns<\/a>, University of Pennsylvania\r\n\r\n<a href=\"http:\/\/www.soc.cornell.edu\/faculty\/macy.html\">Michael Macy<\/a>, Cornell University\r\n\r\n<a href=\"http:\/\/people.stern.nyu.edu\/cperlich\/\">Claudia Perlich<\/a>, Dstillery; NYU Stern\r\n\r\n<a href=\"http:\/\/www.cs.columbia.edu\/~blei\/\">David Blei<\/a>, Columbia University\r\n\r\n<a href=\"http:\/\/tech.cornell.edu\/deborah-estrin\/\">Deborah Estrin<\/a>, Cornell Tech\r\n\r\n<a href=\"http:\/\/yann.lecun.com\/\">Yann LeCun<\/a>, NYU and Facebook\r\n\r\n<a href=\"http:\/\/mornaaman.com\/\">Mor Naaman<\/a>, Cornell Tech\r\n\r\n<a href=\"http:\/\/www1.cs.columbia.edu\/~jebara\/\">Tony Jebara<\/a>, Columbia University"},{"id":2,"name":"Videos","content":"<a href=\"https:\/\/www.microsoft.com\/en-us\/research\/video\/msr-nyc-data-science-seminar-series\/\">MSR NYC Data Science Seminar Series<\/a>\r\n\r\n<a href=\"https:\/\/www.microsoft.com\/en-us\/research\/video\/msr-nyc-data-science-seminar-series-2\/\">MSR NYC Data Science Seminar Series #2<\/a>\r\n\r\n<a href=\"https:\/\/www.microsoft.com\/en-us\/research\/video\/msr-nyc-data-science-seminar-series-3-topic-models-and-user-behavior\/\">MSR NYC Data Science Seminar Series #3 - Topic Models and User Behavior<\/a>\r\n\r\n<a href=\"https:\/\/www.microsoft.com\/en-us\/research\/video\/msr-nyc-data-science-seminar-series-4-what-makes-us-human-machine-learning-challenges-in-digital-advertising\/\">MSR NYC Data Science Seminar Series #4 - What Makes us Human? Machine Learning Challenges in Digital Advertising<\/a>"},{"id":3,"name":"Previous Events","content":"<div class=\"conM \">\r\n\r\n[accordion]\r\n\r\n[panel header=\"June 25, 2015\"]\r\n\r\n<strong>Speaker:<\/strong> <a href=\"http:\/\/www.cis.upenn.edu\/~mkearns\/\">Michael Kearns<\/a>, University of Pennsylvania\r\n\r\n<b>Title:<\/b> <strong>From \u201cIn\u201d to \u201cOver\u201d: Behavioral Experiments on Whole-Network Computation<\/strong>\r\n\r\n<b>Abstract:<\/b>\u00a0 We report on a series of behavioral experiments in human computation on three different tasks over networks: graph coloring, community detection (or graph clustering), and competitive contagion. While these tasks share similar action spaces and interfaces, they capture a diversity of computational challenges: graph coloring is a search problem, clustering is an optimization problem, and competitive contagion is a game-theoretic problem. In contrast with much of the recent literature on human-subject experiments in networks, in which collectives of subjects are embedded \u201cin\u201d the network, and have only local information and interactions, here individual subjects have a global (or \u201cover\u201d) view and must solve \u201cwhole network\u201d problems alone. Our primary findings are that subject performance is impressive across all three problem types; that subjects find diverse and novel strategies for solving each task; and that collective performance can often be strongly correlated with known algorithms.\r\n\r\nJoint work with Lili Dworkin.\r\n\r\n<b>Bio:<\/b> Michael Kearns is a professor in the Computer and Information Science department at the University of Pennsylvania, where he holds the National Center Chair and has joint appointments in the Wharton School. He is founding director of Penn\u2019s Networked and Social Systems Engineering (NETS) program (<a href=\"http:\/\/www.nets.upenn.edu\/\">www.nets.upenn.edu<\/a>), and founding director of Penn's Warren Center for Network and Data Sciences (<a href=\"http:\/\/www.warrencenter.upenn.edu\/\">www.warrencenter.upenn.edu<\/a>). His research interests include topics in machine learning, algorithmic game theory, social networks, and computational finance. He has worked and consulted extensively in the technology and finance industries. He is a fellow of the American Academy of Arts and Sciences, the Association for Computing Machinery, and the Association for the Advancement of Artificial Intelligence.\r\n\r\n[\/panel]\r\n\r\n[panel header=\"April 16, 2015\"]\r\n\r\n<\/div>\r\n<strong>Speaker:<\/strong> <a href=\"http:\/\/www.soc.cornell.edu\/faculty\/macy.html\">Michael Macy<\/a>, Cornell University\r\n\r\n<b>Title:<\/b> <strong>On A Scale From 1 to 5, How Much Confidence Do You Have in Survey Results?<\/strong>\r\n\r\n<b>Abstract:<\/b> From astronomy to neuroscience to particle physics, scientific knowledge depends heavily on the available tools for observation. Since the introduction of stratified sampling in 1934, the survey has been the single most important observational tool for social science. During this time, impressive advances have taken place in our ability to reduce sampling error (e.g. Respondent Driven Sampling), measurement error (e.g. SEM), specification error (e.g. linear mixed models), and inferential error (e.g. Propensity Score Matching). Nevertheless, increasing confidence in survey technology has paradoxically reinforced a debilitating theoretical blinder that has compromised the ability of social science to elicit confidence in predictions. What is worse, this blinder has largely escaped notice through a combination of ideological bias and reluctance to pull back the covers on problems for which we have no solution. The good news is that a solution is finally on the horizon, with the potential for social science to begin to close the gap with the physical and life sciences in predictive ability. The bad news is \u2026 (to be continued).\r\n\r\n<b>Bio:<\/b> Michael Macy earned his B.A. and Ph.D from Harvard, along with an M.A. from Stanford. He is currently Goldwin Smith Professor of Arts and Sciences and Director of the Social Dynamics Laboratory at Cornell, with a dual appointment in the Departments of Sociology and Information Science. With support from the National Science Foundation, the Department of Defense, and Google, his research team has used computational models, online laboratory experiments, and digital traces of device-mediated interaction to explore familiar but enigmatic social patterns, such as circadian rhythms, the emergence and collapse of fads, the spread of self-destructive behaviors, cooperation in social dilemmas, the critical mass in collective action, the spread of high-threshold contagions on small-world networks, the polarization of opinion, segregation of neighborhoods, and assimilation of minority cultures. Recent research uses 509 million Twitter messages to track diurnal and seasonal mood changes in 54 countries, telephone logs for 12B calls in the UK to measure the economic correlates of network structure, and hundreds of millions of Yahoo! email logs in 90 countries to test Huntington\u2019s theory of the \"clash of civilizations.\" His research has been published in leading journals, including Science, PNAS, American Journal of Sociology, American Sociological Review, and Annual Review of Sociology.\r\n\r\n[\/panel]\r\n\r\n[panel header=\"February 5, 2015\"]\r\n\r\nYou can view the recording of the talk <a class=\"invalidLink\" href=\"https:\/\/www.microsoft.com\/en-us\/research\/video\/msr-nyc-data-science-seminar-series-4-what-makes-us-human-machine-learning-challenges-in-digital-advertising\/\" target=\"_self\">here<\/a>\r\n\r\n<strong>Speaker:<\/strong> <a href=\"http:\/\/people.stern.nyu.edu\/cperlich\/\">Claudia Perlich<\/a>, Dstillery &amp; NYU Stern\r\n\r\n<b>Title:<\/b> <strong>What makes us human? Machine learning challenges in digital advertising<\/strong>\r\n\r\n<b>Abstract:<\/b> Digital advertising is one of the largest and open playgrounds for machine learning, data mining and related analytic approaches. This talk will touch on a number of challenges which arise in this environment: 1) high volume data streams of around 30 Billion daily consumer touch points, 2) low latency requirements on scoring and automated bidding decisioning within 100ms and 3) adversarial modeling in the light of advertising fraud and bots. Specifically, we will discuss an automated learning system implemented at Dstillery, that uses privacy friendly data representation to build sparse targeting models for thousands of products in Millions of dimensions. The solution incorporates ideas from transfer learning, Bayesian priors, stochastic gradient descent, hashing and learning rate estimation. On the sidelines, but of no less importance, are topics on bid optimization, data reliability, cross-device identification and observational methods for causal inference. Finally, I will touch on a few higher-level lessons around incentive misalignments\/measurement issues in the advertising industry and pose the paradox of big data and predictive modeling: You never have the data you need.\r\n\r\n<b>Bio:<\/b> Claudia Perlich currently acts as Chief Scientist at Dstillery and designs, develops, analyzes and optimizes the machine learning that drives digital advertising. An active industry speaker and frequent contributor to academic and industry publications, Claudia was recently named winner of the Advertising Research Foundation\u2019s (ARF) Grand Innovation Award, was selected as member of the Crain\u2019s NY annual 40 Under 40 list, WIRED\u2019s Smart List, and FastCompany\u2019s 100 Most Creative People. She has published over 50 scientific articles, and holds multiple patents in machine learning. Claudia has a PhD in Information Systems from NYU and worked in the Predictive Modeling Group at IBM\u2019s Watson Research Center, concentrating on data analytics and machine learning for real-world applications. She also teaches in the NYU Stern MBA program.\r\n\r\n[\/panel]\r\n\r\n[panel header=\"December 4th, 2014\"]\r\n\r\nYou can view the recording of the talk <a class=\"invalidLink\" href=\"https:\/\/www.microsoft.com\/en-us\/research\/video\/msr-nyc-data-science-seminar-series-3-topic-models-and-user-behavior\/\" target=\"_self\">here<\/a>\r\n\r\n<strong>Speaker:\u00a0<\/strong><a href=\"http:\/\/www.cs.columbia.edu\/~blei\/\" target=\"_self\">David Blei<\/a>, Columbia University\r\n\r\n<strong>Title:<\/strong> <strong>Topic Models and User Behavior<\/strong>\r\n\r\n<strong>Abstract:<\/strong> Probabilistic topic models provide a suite of tools for analyzing\r\nlarge document collections. Topic modeling algorithms discover the\r\nlatent themes that underlie the documents and identify how each\r\ndocument exhibits those themes. Topic modeling can be used to help\r\nexplore, summarize, and form predictions about documents. Topic\r\nmodeling ideas have been adapted to many domains, including images,\r\nmusic, networks, genomics, and neuroscience.\r\n\r\nTraditional topic modeling algorithms analyze a document collection\r\nand estimate its latent thematic structure. However, many collections\r\ncontain an additional type of data: how people use the documents. For\r\nexample, readers click on articles in a newspaper website, scientists\r\nplace articles in their personal libraries, and lawmakers vote on a\r\ncollection of bills. Behavior data is essential both for making\r\npredictions about users (such as for a recommendation system) and for\r\nunderstanding how a collection and its users are organized.\r\n\r\nIn this talk, I will review the basics of topic modeling and describe\r\nour recent research on collaborative topic models, models that\r\nsimultaneously analyze a collection of texts and its corresponding\r\nuser behavior. We studied collaborative topic models on 80,000\r\nscientists' libraries from Mendeley and 100,000 users' click data from\r\nthe arXiv. Collaborative topic models enable interpretable\r\nrecommendation systems, capturing scientists' preferences and pointing\r\nthem to articles of interest. Further, these models can organize the\r\narticles according to the discovered patterns of readership. For\r\nexample, we can identify articles that are important within a field\r\nand articles that transcend disciplinary boundaries.\r\n\r\nMore broadly, topic modeling is a case study in the large field of\r\napplied probabilistic modeling. Finally, I will survey some recent\r\nadvances in this field. I will show how modern probabilistic modeling\r\ngives data scientists a rich language for expressing statistical\r\nassumptions and scalable algorithms for uncovering hidden patterns in\r\nmassive data.\r\n\r\n<strong>Bio:<\/strong> David Blei is a Professor of Statistics and Computer Science at\r\nColumbia University. His research is in statistical machine learning,\r\ninvolving probabilistic topic models, Bayesian nonparametric methods,\r\nand approximate posterior inference. He works on a variety of\r\napplications, including text, images, music, social networks, user\r\nbehavior, and scientific data.David earned his Bachelor's degree in Computer Science and Mathematics from Brown University (1997) and his PhD in Computer Science from the\r\nUniversity of California, Berkeley (2004). Before arriving to\r\nColumbia, he was an Associate Professor of Computer Science at\r\nPrinceton University. He has received several awards for his\r\nresearch, including a Sloan Fellowship (2010), Office of Naval\r\nResearch Young Investigator Award (2011), Presidential Early Career\r\nAward for Scientists and Engineers (2011), Blavatnik Faculty Award\r\n(2013), and ACM-Infosys Foundation Award (2013).\r\n\r\n[\/panel]\r\n\r\n[panel header=\"September 23, 2014\"]\r\n\r\nYou can view the recording of the talk <a class=\"invalidLink\" title=\"\" href=\"https:\/\/www.microsoft.com\/en-us\/research\/video\/msr-nyc-data-science-seminar-series-2\/\" target=\"_self\">here<\/a>\r\n\r\n<strong>Speaker:<\/strong> <a href=\"http:\/\/tech.cornell.edu\/deborah-estrin\/\">Deborah Estrin<\/a>, Cornell Tech\r\n\r\n<strong>Title: Small, n=me, data<\/strong>\r\n\r\n<strong>Abstract:<\/strong> Consider a new kind of cloud-based app that would create a picture of an individuals life over time by continuously, securely, and privately analyzing the digital traces they generate 24x7. The social networks, search engines, mobile operators, online games, and e-commerce sites that they access every hour of most every day extensively use these digital traces to tailor service offerings and to improve system performance and in some cases to target advertisements. Our premise is that these diverse and messy, but highly personalized, data can be analyzed to draw powerful inferences about an individual, and for that individual. Use of applications that are fueled by these traces could enhance, and even transform, our experiences as consumers, patients, passengers, customers, family members, as well as users of online media. This talk will discuss precedents for small data in mobile health, and the opportunities and challenges of broadening the scope of small data capture, storage, and use.\r\n\r\n<strong>Bio:<\/strong> Deborah Estrin (PhD, MIT (1985); BS, UCB (1980)) is a Professor of Computer Science at Cornell Tech in New York City (<a title=\"\" href=\"http:\/\/tech.cornell.edu\/deborah-estrin\" target=\"_self\">http:\/\/tech.cornell.edu\/deborah-estrin<\/a>) and a Professor of Health Policy and Research at Weill Cornell Medical College. She is a co-founder of Open mHealth (<a href=\"http:\/\/openmhealth.org\/\">http:\/\/openmhealth.org<\/a>\/). Her current focus is on mobile health and small data, leveraging the pervasiveness of mobile devices and digital interactions for health and life management (TEDMED <a href=\"https:\/\/smalldata.tech.cornell.edu\/narrative.php\">https:\/\/smalldata.tech.cornell.edu\/<\/a>). Estrin was the founding director of the NSF-funded Science and Technology Center for Embedded Networked Sensing (CENS) at UCLA (2002-12). Awards include: ACM Athena Lecturer (2006) and Anita Borg Institute's Women of Vision Award for Innovation (2007). She is an elected member of the American Academy of Arts and Sciences (2007) and National Academy of Engineering (2009).\r\n\r\n[\/panel]\r\n\r\n[panel header=\"April 24, 2014\"]\r\n<h2>Opening Event<\/h2>\r\n<div class=\"conM \">\r\n\r\nYou can view the talks and the panel discussion <a class=\"invalidLink\" href=\"https:\/\/www.microsoft.com\/en-us\/research\/video\/msr-nyc-data-science-seminar-series\/\" target=\"_self\">here<\/a>.\r\n\r\n<hr \/>\r\n\r\n<h2>Opening Remarks<\/h2>\r\nSpeaker: <a class=\"invalidLink\" href=\"https:\/\/www.microsoft.com\/en-us\/research\/people\/duncan\/\" target=\"_self\">Duncan Watts<\/a>\r\n\r\n<strong>Bio:<\/strong> Prior to joining Microsoft, Duncan Watts was a Senior Principal Research Scientist at Yahoo! Research, where he directed the Human Social Dynamics group. Prior to joining Yahoo!, he was a full professor of Sociology at Columbia University, where he taught from 2000-2007. His research on social networks and collective dynamics has appeared in a wide range of journals, from Nature, Science, and Physical Review Letters to the American Journal of Sociology and Harvard Business Review. He is also the author of three books, most recently Everything is Obvious (Once You Know The Answer) (Crown Business, 2011). He holds a B.Sc. in Physics from the Australian Defense Force Academy, and a Ph.D. in Theoretical and Applied Mechanics from Cornell University.\r\n\r\n<hr \/>\r\n\r\n<h2>Technical Talks<\/h2>\r\n<a href=\"http:\/\/yann.lecun.com\/\" target=\"_self\">Yann LeCun<\/a>, NYU and Facebook\r\n\r\n<strong>Title &amp; Abstract: Yann presents a demo on deep learning and vision.\u00a0<\/strong>\r\n\r\n<strong>Bio:<\/strong> Yann is Director of AI Research at Facebook, and Silver Professor of Dara Science, Computer Science, Neural Science, and Electrical Engineering at New York University, affiliated with the NYU Center for Data Science, the Courant Institute of Mathematical Science, the Center for Neural Science, and the Electrical and Computer Engineering Department. He received the Electrical Engineer Diploma from Ecole Superieure d'Ingenieurs en Electrotechnique et Electronique (ESIEE), Paris in 1983, and a PhD in Computer Science from Universite Pierre et Marie Curie (Paris) in 1987. He is the lead faculty at NYU for the Moore-Sloan Data Science Environment, a $36M initiative in collaboration with UC Berkeley and University of Washington to develop data-driven methods in the sciences. He is the recipient of the 2014 IEEE Neural Network Pioneer Award.\r\n\r\n<hr \/>\r\n\r\n<a href=\"http:\/\/mornaaman.com\/\" target=\"_self\">Mor Naaman<\/a>, Cornell Tech\r\n\r\n<b>Title: Data and People in Connective Media<\/b>\r\n\r\n<b>Abstract: <\/b>In five minutes or less, I will talk about how we use methods from social science, people-centered design, data science and machine learning to understand social media data large and small, and build new applications that help us make sense of the city from (public) social media data. I'll also say a word about Cornell Tech and our Connective Media hub. OK, six minutes may be needed to squeeze it all in.\r\n\r\n<b>Bio:<\/b> Naaman is an associate professor at Cornell Tech's Jacobs Institute. He is also a co-founder and Chief Scientist at Seen.co, a startup founded to make sense of the real-time web and social media. Mor's research applies multidisciplinary methods to gain new insights about people and society from social media data, and to develop novel tools to make this data more accessible and usable in various settings. He gets awards, too, including the NSF Early Faculty CAREER Award, research awards from Google, Yahoo!, and Nokia, and three best paper awards.\r\n\r\n<hr \/>\r\n\r\n<a href=\"http:\/\/www1.cs.columbia.edu\/~jebara\/\" target=\"_self\">Tony Jebara<\/a>, Columbia University\r\n\r\n<strong>Title:\u00a0Learning From Network Connectivity and Mobile Phone Data<\/strong>\r\n\r\n<strong>Abstract: <\/strong>Many real-world networks are described by both connectivity information as well as features for every node. While most network growth models are based on link analysis, we explore how an individual's data profile without any connectivity information can be used to infer their connectivity with other users. For example, in a class of incoming freshmen students with no known friendship connections, can we predict which pairs will become friends at the end of the year using only their profile information? Similarly, can we using co-location to predict communication? In other words, by observing only the mobile location data from users, can we predict what pairs of users are likely to communicate? To learn how to reconstruct these networks, we present structure-preserving metric learning and apply it to Facebook data, Wikipedia data, FourSquare data and mobile phone call detail records,\r\n\r\n<strong>Bio:<\/strong> Tony is Associate Professor of Computer Science at Columbia University. He chairs the Center on Foundations of Data Science as well as directs the Columbia Machine Learning Laboratory. His research intersects computer science and statistics to develop new frameworks for learning from data with applications in social networks, spatio-temporal data, vision and text. Jebara has founded or advised startups including Sense Networks (acquired by yp.com), AchieveMint, Agolo, and Bookt (acquired by RealPage NASDAQ:RP). He is the author of the book Machine Learning: Discriminative and Generative. In 2004, Jebara was the recipient of the Career award from the National Science Foundation.\r\n\r\n<hr \/>\r\n\r\n<h2>Panel Discussion<\/h2>\r\n<strong>Panel Topic:<\/strong> <strong>Opportunities and Challenges in Data Science Research<\/strong>\r\n\r\n<strong>Panel Moderator:<\/strong> <a class=\"invalidLink\" href=\"https:\/\/www.microsoft.com\/en-us\/research\/people\/jchayes\/\" target=\"_self\">Jennifer Chayes<\/a>, Managing Director, MSR New York City\r\n\r\n<strong>Bio:<\/strong> Jennifer Tour Chayes is Managing Director of Microsoft Research New York City as well as the Microsoft Research New England lab in Cambridge. Before this, she was research area manager for Mathematics, Theoretical Computer Science and Cryptography at Microsoft Research Redmond. Chayes joined Microsoft Research in 1997, when she co-founded the Theory Group. Her research areas include phase transitions in discrete mathematics and computer science, structural and dynamical properties of self-engineered networks, and algorithmic game theory. She is the co-author of almost 100 scientific papers and the co-inventor of more than 20 patents.\r\n\r\n<strong>Panel Members:<\/strong> Yann LeCun, Mor Naaman, Tony Jebara\r\n\r\n[\/panel]\r\n\r\n[\/accordion]\r\n\r\n<\/div>"}],"msr_startdate":"2014-04-24","msr_enddate":"2016-04-24","msr_event_time":"","msr_location":"New York, NY, U.S.","msr_event_link":"","msr_event_recording_link":"","msr_startdate_formatted":"April 24, 2014","msr_register_text":"Watch now","msr_cta_link":"","msr_cta_text":"","msr_cta_bi_name":"","featured_image_thumbnail":null,"event_excerpt":"This seminar series is bringing data science researchers from Columbia University, NYU, Cornell Tech and Microsoft Research together. Our goal is to increase interactions within the broader New York data science community, and to provide a new forum for discussions on data science research. Format The events in this series start with a formal talk session (45 minutes). Invited speakers present short talks, providing their views on the opportunities and challenges in data science research.&hellip;","msr_research_lab":[199571],"related-researchers":[{"type":"user_nicename","value":"jchayes","display_name":"Jennifer Chayes","author_link":"<a href=\"https:\/\/www.microsoft.com\/en-us\/research\/people\/jchayes\/\" aria-label=\"Visit the profile page for Jennifer Chayes\">Jennifer Chayes<\/a>","is_active":false,"user_id":32200,"last_first":"Chayes, Jennifer","people_section":0,"alias":"jchayes"}],"msr_impact_theme":[],"related-academic-programs":[],"related-groups":[],"related-projects":[],"related-opportunities":[],"related-publications":[],"related-videos":[],"related-posts":[],"_links":{"self":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-event\/199989","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-event"}],"about":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/types\/msr-event"}],"version-history":[{"count":3,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-event\/199989\/revisions"}],"predecessor-version":[{"id":1147381,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-event\/199989\/revisions\/1147381"}],"wp:attachment":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/media?parent=199989"}],"wp:term":[{"taxonomy":"msr-research-area","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/research-area?post=199989"},{"taxonomy":"msr-region","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-region?post=199989"},{"taxonomy":"msr-event-type","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-event-type?post=199989"},{"taxonomy":"msr-video-type","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-video-type?post=199989"},{"taxonomy":"msr-locale","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-locale?post=199989"},{"taxonomy":"msr-program-audience","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-program-audience?post=199989"},{"taxonomy":"msr-post-option","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-post-option?post=199989"},{"taxonomy":"msr-impact-theme","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-impact-theme?post=199989"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}