New York City Lab Members' Bios

The Microsoft Research New York City lab was founded in May of 2012. Our interdisciplinary group includes computer scientists, sociologists, mathematicians, and economists, who work together and have a history of creating practical, commercial technologies as well as basic research. Our areas of interest include Large-Scale Machine Learning, Interactive Machine Learning, Information Retrieval, Algorithmic Economics and Market Design, Behavioral and Empirical Economics, Prediction Engines, Computational Social Science, and Online Experimental Social Science.

 

 

 

Alekh Agarwal
Postdoc Researcher
Research Area: Machine Learning

 

Alekh is a postdoctoral researcher at MSR NYC. Prior to that, he obtained his PhD in computer science from UC Berkeley in 2012 which was supported in part by a MSR PhD fellowship and a Google PhD fellowship. Alekh’s research encompasses many theoretical and practical aspects of large-scale machine learning, with particular emphasis on the design of computationally budgeted algorithms, large-scale convex optimization, high-dimensional statistical inference and learning algorithms for agents that actively interact with their environment. 

 

 

danah boyd
Senior Researcher
Research Area: Social Media

 

danah boyd's research centers on the intersection of people, social practices, and technology. She is interested in how mediated environments alter the structural conditions in which people operate and how people navigate and repurpose these environments for their own needs. Her current work investigates how youth culture, privacy, online safety, and unintended outcomes of social technologies. danah is also a Research Scholar at New York University's Media, Culture, and Communication program, a Visiting Researcher at Harvard Law School, and an Adjunct Associate Professor at the University of New South Wales. When bored or frustrated, danah is known to blow off steam by ranting on her blog.

   

 

Ceren Budak
Postdoc Researcher
Research Area: Computational Social Science

 

Ceren received her PhD in computer science from University of California, Santa Barbara in 2012. She is interested in building data-driven solutions to address challenges that arise in complex systems, with a particular focus on social systems. This goal has driven Ceren to focus on diverse areas of research such as data mining, theoretical computer science, statistics and databases. She has been applying these techniques to tackle problems such as modeling the diffusion of information, trend detection and optimization problems in the context of online social networks. 

 

Jennifer Chayes
Managing Director
Research Area: Theory

 

Jennifer Tour Chayes is managing director of Microsoft Research New York City as well as the newly opened 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.

 

 

Fernando Diaz
Researcher
Research Area: Information Retrieval

 

Fernando Diaz's research concerns all levels of information retrieval system deployment, including algorithm design, implementation, and evaluation. The generalizability of these approaches has been supported in domains such as core web search, news search, enterprise search, medical informatics, federated search, and cross-lingual retrieval. His work on federated search received best paper awards at the SIGIR 2010 and WSDM 2010 conferences. Fernando's current research focuses on information access during crisis events such as natural disasters. In this area, he co-organized the SIGIR 2011 Workshop on Social Media Under Crisis and is co-organizing the TREC 2013 Temporal Summarization Track. His research studying the spatiotemporal aspects of query behavior during crisis events received a best paper nomination at SIGIR 2011. Fernando received his PhD from the University of Massachusetts Amherst in 2008. 

 

 

Miroslav Dudik
Researcher
Research Area: Machine Learning

 

Miroslav Dudík’s research combines theoretical and applied aspects of machine learning, statistics, convex optimization and algorithms. Two main themes in his theoretical work have been the use of constrained optimization in statistical estimation, and learning from biased or partially observed data (contextual bandits). His applied work includes species habitat modeling, image classification, and modeling of user response to web content. Most recently he has been applying convex optimization to the design of “prediction engines” that aggregate user opinion using the mechanism of prediction markets. He received his PhD from Princeton in 2007. He is a co-creator of the MaxEnt package for modeling species distributions, which is used by thousands of?biologists around the world to design national parks, model impacts of climate change, and discover new species. 

 

 

Rafael Frongillo
Postdoc Researcher
Research Area: Algorithmic Economics

 

Rafael Frongillo completed his Ph.D. at UC Berkeley, advised by Christos Papadimitriou and supported by the NDSEG fellowship. His research lies broadly in algorithmic economics, drawing techniques from game theory, convex analysis, machine learning, and dynamical systems. Currently, he is focused on conceptual problems in elicitation (i.e., scoring rules, mechanism design, prediction markets).

 

 

Sharad Goel
Senior Researcher
Research Area: Computational Social Science

 

Sharad Goel’s primary research area is computational social science, an emerging discipline at the intersection of computer science, statistics, and the social sciences. He is particularly interested in large-scale empirical analyzes that address questions motivated by sociology and economics. Prior to joining Microsoft Research, Sharad worked in the Microeconomics and Social Systems group at Yahoo! Research. He holds a BS in mathematics from the University of Chicago, a PhD in applied mathematics from Cornell, and completed postdoctoral fellowships in the math departments at Stanford and the University of Southern California.

 

 

 

Dan Goldstein
Principal Researcher
Research Area: Experimental and Behavioral Social Science

 

Dan Goldstein works at the intersection of behavioral economics and computer science. Research topics include: judgment and decision making (e.g., lexicographic “fast and frugal” heuristics), choice architecture (e.g., the effect of opt-out defaults on organ donation), behavioral finance (e.g., the Distribution Builder methodology for investment preference elicitation), inter-temporal choice (e.g., using digitally-aged photos of faces to affect retirement savings), and online marketing (e.g., the economic impact of annoying ads, time-based display advertising, social network targeting, and tracking URL diffusion). Prior to joining Microsoft, Dan was a Principal Research Scientist at Yahoo Research and a marketing professor at London Business School. He received his Ph.D. at The University of Chicago and has taught and researched at Columbia, Harvard, Stanford and Max Planck Institute in Germany, where he was awarded the Otto Hahn Medal in 1997. His academic writings have appeared in journals from Science to Psychological Review. Dan is a member of the Academic Advisory Board of the UK Government's Behavioral Insights Unit (aka Britain’s “nudge unit”), the Advisory Board of Allianz Global Investors’ Center for Behavioral Finance, and the Executive Board of the Society for Judgment and Decision Making. He edits Decision Science News. 

 

 

Jake Hofman
Researcher
Research Area: Computational Social Science

 

Jake Hofman is a Researcher at Microsoft Research in New York City, where his work in computational social science involves applications of statistics and machine learning to large-scale social data. Prior to joining Microsoft, he was a member of the Microeconomics and Social Systems group at Yahoo! Research. Jake is also an Adjunct Assistant Professor of Applied Mathematics at Columbia University, where he has designed and taught classes on a number of topics ranging from biological physics to applied machine learning. He holds a B.S. in Electrical Engineering from Boston University and a Ph.D. in Physics from Columbia University. 

 

 

Sébastien Lahaie
Researcher
Research Area: Algorithmic Economics

 

Sébastien Lahaie received his PhD in Computer Science from Harvard University in 2007 and was previously a senior research scientist at Yahoo. His research focuses on computational aspects of marketplace design, including sponsored search and display advertising. He is interested in designing market algorithms that scale well and properly anticipate user behavior. Other interests include preference modeling and elicitation, reputation systems, and prediction markets. He serves as a co-editor for Economic Inquiry and was previously a program chair for AMMA. He regularly serves on the program committee of conferences such as EC, IJCAI, WWW, and AAMAS. 

 

 

John Langford
Senior Researcher
Research Area: Machine Learning

 

John Langford has a unique expertise over all aspects of machine learning. He has framed important new settings (such as contextual bandit learning) and mastered them to create many useful algorithms for learning with user feedback, created new forms of analysis (such as learning reductions theory), architected terascale parallel learning algorithms, and leads the Vowpal Wabbit software project. His research also spans Game theory, Steganography, and Captchas. He is the main author of the open-source software Vowpal Wabbit which is currently the fastest (generalized) linear predictor anywhere. He is also the author of the most widely read blog on machine learning (hunch.net). He is a co-chair of the 2012 International Conference on Machine Learning and has published around 100 papers with >8000 citations, most-recently coauthoring the book Scaling Up Machine Learning. 

 

 

David Pennock
Principal Researcher
Research Area: Algorithmic Economics

 

David Pennock has over sixty academic publications relating to computational issues in electronic commerce and the web, including papers in PNAS, Science, IEEE Computer, Theoretical Computer Science, Algorithmica, Electronic Commerce Research, Electronic Markets, AAAI, EC, WWW, KDD, UAI, SIGIR, ICML, NIPS, INFOCOM, SAINT, ACM SIGCSE, and VLDB. He has authored two patents and ten patent applications. One of his primary areas of expertise is the design and analysis of prediction markets. In 2005, he was named to MIT Technology Review's list of 35 top technology innovators under age 35. Prior to his current position, he was a Principal Research Scientist at Yahoo!, a research scientist at NEC Laboratories America, a research intern at Microsoft Research, and in 2001 served as an adjunct professor at Pennsylvania State University. He received a Ph.D. in Computer Science from the University of Michigan, an M.S. in Computer Science from Duke University, and a B.S. in Physics from Duke. Dr. Pennock's work has been featured in Discover Magazine, New Scientist, CNN, the New York Times, the Economist, Surowiecki’s "The Wisdom of Crowds", and several other publications. 

 

 

David Rothschild
Economist
Research Area: Economics

 

David Rothschild is an economist at MSR-NYC. He has a Ph.D. in applied economics from the Wharton School of Business at the University of Pennsylvania. His primary body of work is on forecasting, and understanding public interest and sentiment. Related work examines how the public absorbs information. He has written extensively, in both the academic and popular press, on polling, prediction markets, and predictions of upcoming events; most of his popular work has focused on predicting elections and an economist take on public policy. After joining Microsoft in May he has been busy building prediction and sentiment models, and organizing novel/experimental polling and prediction games; this work has appeared on both Bing and Xbox. And, he correctly predicted 50 of 51 Electoral College outcomes in February of 2012. 

 

 

Justin Rao
Researcher
Research Area: Economics

 

Justin Rao completed his Ph.D. in economics at UC San Diego under the guidance of Jim Andreoni. From there, he joined Yahoo! Research where he conducted pure research in behavioral economics and industrial organization as well as applied research in dynamic and variable pricing, quantifying user experience and personnel incentives. His work has received press coverage in the Wall Street Journal, Business Week, ESPN.com, ESPN The Magazine (Paper awarded "ESPN Fan Choice" for best paper in the conference). Co-hosted ESPN "NBA Today" Podcast. 

 

Matthew Salganik
Senior Researcher
Research Area: Computational Social Science

 

Matt comes to MSR NYC from Princeton University where he was a Professor of Sociology. His research interests include social networks and computational social science. One main area of his research has focused on developing network-based statistical methods for studying hard-to-reach populations such as the groups most at-risk for HIV/AIDS. A second main area of work has focused on using the World Wide Web to collect and analyze social data in innovative ways. For more information and links to papers, you can check out his homepage: http://www.princeton.edu/~mjs3

 

Alex Slivkins
Researcher
Research Area: Theory, Machine Learning, Economics and Computation

 

Alex’s research interests are in algorithms and theoretical computer science, spanning machine learning theory, social network analysis, and algorithmic economics. He has also worked on metric embeddings and algorithms for Internet and peer-to-peer networks. Across various domains, Alex is drawn to algorithmic problems with informational constraints. He is particularly interested in sequential decision-making and its applications to web search, mechanism design, and crowdsourcing markets. His work has received the best paper award at ACM EC 2010 and the best student paper award at ACM PODC 2005.

 

Before joining MSR New York in 2013, Alex Slivkins was a member of MSR Silicon Valley since 2007. Alex received his Ph.D. in Computer Science from Cornell University in 2006, under the supervision of Jon Kleinberg. In 2006-2007 he was a postdoc at Brown University with Eli Upfal. 

 

Siddharth Suri
Researcher
Research Area: Experimental and Behavioral Social Science

 

Siddharth Suri's research spans computer science and behavioral economics. He has coauthored a series of papers modeling and analyzing algorithms in the MapReduce programming paradigm. He also coauthored a paper which pioneered the field of networked human subjects experiments. Moreover, Sid has become one of the leaders in designing, building, and conducting "virtual lab" experiments using Amazon's Mechanical Turk. This work includes a series of static and dynamic network cooperation games and a series of experiments on psychologically informed pricing schemes for display ads.

 

Sid is one of the founding members of Microsoft Research, New York City. Before that he was a member of the Human & Social Dynamics group at Yahoo! Research led by Duncan Watts from 2008 to 2012. He was a postdoctoral associate working with Jon Kleinberg in the computer science department at Cornell University from 2007 to 2008. Sid earned his Ph.D. in computer and information science from the University of Pennsylvania in 2007 under the supervision of Michael Kearns. 

 

 

Jennifer Wortman Vaughan
Researcher
Research Area: Algorithmic Economics and Machine Learning

 

Jenn Wortman Vaughan came to MSR New York from UCLA, where she was an assistant professor in the computer science department. She completed her Ph.D. at the University of Pennsylvania in 2009, and subsequently spent a year as a Computing Innovation Fellow at Harvard. Her research interests are in algorithmic economics and market design, machine learning, and social computing, all of which she studies using techniques from theoretical computer science. She is the recipient of Penn's 2009 Rubinoff dissertation award for innovative applications of computer technology, a National Science Foundation CAREER award, a Presidential Early Career Award for Scientists and Engineers, and best paper or best student paper awards at COLT, ACM EC, and UAI. In her “spare” time, Jenn is involved in a variety of efforts to provide support for women in computer science; most notably, she co-founded the Annual Workshop for Women in Machine Learning, which has been held each year since 2006. 

 

Duncan Watts
Principal Researcher
Research Area: Computational and Experimental Social Science

 

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. He has also served on the external faculty of the Santa Fe Institute and Nuffield College, Oxford. 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. His paper “Collective Dynamics of Small World Networks,” published in Nature in 1998, was named one of top 10 most cited papers in Physics in the decade following, and is considered a seminal contribution to network science. He is also the author of three books; “Small Worlds: The Dynamics of Networks Between Order and Randomness (Princeton, 1999); “Six Degrees: The Science of A Connected Age” (Norton, 2003), and most recently “Everything is Obvious (Once You Know The Answer)” (Crown Business, 2011). He holds a B.Sc. in Physics from the Australian Defence Force Academy, from which he also received his officer’s commission in the Royal Australian Navy, and a Ph.D. in Theoretical and Applied Mechanics from Cornell University. 

 

Visiting researchers

  • Yiling Chen (July - December 2013), Harvard School of Engineering and Applied Sciences
  • Robert Schapire (September - January 2014), Princeton University Department of Computer Science

 

Interns

  • None currently

 

Careers at Microsoft Research

We are always looking for exceptional researchers, post-docs, and interns. For more information about a career at Microsoft Research New York City, see:

 

Meet the Researchers