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
Research Area: Machine Learning


Alekh is a researcher at MSR NYC, where he was previously a postdoctoral researcher. 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, learning feature representations and learning algorithms for agents that actively interact with their environment.



Sarah Bird
Postdoc Researcher
Research Area: Computer Systems, Machine Learning


Sarah is postdoc at Microsoft Research NYC. Her research interests include mobile and cloud computing, machine learning, dynamic optimization, energy efficiency, parallel computer architecture, operating systems, and user experience. Her current research focuses on problems at the intersection of systems and machine learning, particularly on designing systems that can be controlled and optimized with learning algorithms. Sarah did her Ph.D. work in computer science at UC Berkeley’s Parallel Computing Laboratory (ParLab) advised by Krste Asanovic and David Patterson at Berkeley and Burton Smith at Microsoft Research. She has B.S. in Electrical Engineering (Computer Engineering) from the University of Texas at Austin. 



danah boyd
Principal 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, the "big data" phenomenon, and unintended outcomes of social technologies. She is the author of "It's Complicated: The Social Lives of Networked Teens". danah is also a Research Assistant Professor at New York University and the founder and executive director of the Data & Society Research Institute, a NYC-based think/do tank. 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 distinguished scientist and 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, and for ten years before this, she was Professor of Mathematics at UCLA. Chayes’ 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 over 125 scientific papers and the co-inventor of more than 30 patents. Chayes is the recipient of many awards and honors: she is a Fellow of the Association of Computing Machinery, the American Mathematical Society, the Fields Institute, the American Association for the Advancement of Science, as well as a National Associate of the National Academies, an elected Member of the American Academy of Arts and Sciences, and the recipient of the Women of Vision Leadership Award of the Anita Borg Institute.



Byron Cook

Principal Researcher
Research Area: Software Development, Programming Principles, Tools, and Languages


Byron Cook comes to MSR-NYC from MSR-Cambridge (UK), where he spent the last 10 years. Byron’s research interests include automatic program verification, constraint solving, logic and theorem proving, biological systems. In recent years Byron has been active in making automatic program termination provers (yes, yes, the halting problem is undecidable) as well as building tools for the analysis of biological models. Previously Byron was actively involved with the Windows OS Static Driver Verifier tool. In fact, Byron spent two years as a developer in the Windows operating systems group to help make this transfer of MSR technology a possibility.



Kate Crawford
Principal Researcher

Research Area: Social Media


Kate Crawford researches the social, political and cultural contexts of data and networked technologies. She is a Visiting Professor at the MIT Center for Civic Media, and a Senior Fellow at NYU's Information Law Institute. She has conducted large-scale studies of mobile and social media use at sites around the world, including India and Australia, and has been awarded both the AAH Medal and the Manning Clark Cultural Award. Her current projects include the political and ethical implications of data science, and the uses of social data during crisis events. She recently received a Rockefeller Foundation Bellagio Fellowship for work on data and communities. She is on the editorial boards of Fibreculture and Big Data and Society. 



Fernando Diaz
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
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. 



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
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. 



Tzu-Kuo Huang

Postdoc Researcher

Research Area: Machine Learning


Tzu-Kuo (TK) is a postdoc researcher at MSR NYC. Previously he was a postdoc at Carnegie Mellon University, after receiving his Ph.D. in machine learning from the same institution. His research interests include interactive machine learning, learning dynamic models, and recently large-scale learning. He has been working on developing interactive algorithms that help users quickly find relevant information from large data repositories, and methods for learning dynamic models from data without explicit temporal information.


Sébastien Lahaie
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
Principal 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 a widely read blog on machine learning (, was co-chair of the 2012 International Conference on Machine Learning, and has published around 100 papers with >10K citations. 



David Pennock
Assistant Managing Director

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. 



Justin Rao
Research Area: Economics


Justin M. Rao is a researcher at Microsoft Research in New York City. He previously was at Yahoo! Research in Santa Clara, CA for two years after receiving his Ph.D. in Economics from UCSD in 2010. He is an empirical economist with a focus on e-commerce, decision making and writing about himself in the 3rd person.



David Rothschild
Research Area: Economics


David Rothschild 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, social media and online data, 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 2012 he has been 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, and 21 of 24 Oscars in 2014.



Rob Schapire

Principal Researcher
Research Area: Machine Learning


Rob Schapire is a Principal Researcher at Microsoft Research in New York City. He received his PhD from MIT in 1991. After a short post-doc at Harvard, he joined the technical staff at AT&T Labs (formerly AT&T Bell Laboratories) in 1991. Since 2002, he has been with the Computer Science Department at Princeton University. He joined MSR in 2014 (on leave from Princeton). His awards include the 1991 ACM Doctoral Dissertation Award, the 2003 Gödel Prize, and the 2004 Kanelakkis Theory and Practice Award (both of the last two with Yoav Freund). He is a fellow of the AAAI, and a
member of the National Academy of Engineering. His main research interest is in theoretical and applied machine learning, with particular focus on boosting, online learning, game theory, and maximum entropy.



Alex Slivkins
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
Senior Researcher
Research Area: Experimental and Behavioral Social Science


Siddharth "Sid" Suri works at the intersection of computer science and behavioral economics. His work analyzes the relationship between social network topology and behavior using a variety of techniques including behavioral experiments, massive data analysis and theoretical modeling. Moreover, Sid has become one of the leaders in designing, building, and conducting "virtual lab" experiments using Amazon's Mechanical Turk. His work has appeared in Science, PNAS, as well as top computer science venues. He won the Best paper award and a Top 10% paper award in ACM EC 2012.
Sid earned his Ph.D. in computer and information science from the University of Pennsylvania in 2007 under the supervision of Michael Kearns. After that he was a postdoctoral researcher working with Jon Kleinberg in the computer science department at Cornell University. Then he moved to the Human & Social Dynamics group at Yahoo! Research led by Duncan Watts. Currently, Sid is one of the founding members of Microsoft Research, New York City. 



Vasilis Syrgkanis
Postdoc Researcher
Research Area: Algorithmic Economics, Theory


Vasilis comes to MSR from Cornell University where he just completed his PhD in Computer Science under the supervision of Prof. Eva Tardos. His research interests include algorithms, game theory, auction theory, mechanism design, crowdsourcing and computational complexity. Driven from electronic market applications such as ad auctions, his research focuses on the design and analysis of approximately efficient mechanisms with guaranteed good properties even when players participate in many mechanisms simultaneously or sequentially and even if they use learning algorithms to identify how to play the game and have incomplete information about the competition. More broadly he is interested in quantifying the inefficiency of systems with strategic users.


Hanna Wallach
Research Area: Machine Learning, Computational Social Science


Hanna Wallach is a researcher at Microsoft Research in New York City
and an assistant professor at the University of Massachusetts
Amherst's School of Computer Science, where she is one of five core
faculty members involved in UMass's recently formed Computational
Social Science Initiative. Hanna develops new machine learning methods
for analyzing the structure, content, and dynamics of complex social
processes, such as the US political system, the US patent system, and
software development communities. Her research contributes to machine
learning, Bayesian statistics, and, in collaboration with social
scientists, to the nascent field of computational social science. Her
work on infinite belief networks won the best paper award at AISTATS
2010. Hanna has organized several workshops on Bayesian latent
variable modeling and computational social science. She also
co-founded the annual Women in Machine Learning Workshop. Hanna holds
a B.A. in Computer Science from the University of Cambridge, an
M.S. in Cognitive Science and Machine Learning from the University of
Edinburgh, and a Ph.D. in Physics from the University of Cambridge.



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. 



Jennifer Wortman Vaughan
Research Area: Algorithmic Economics, 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. 




Visiting Researchers

  • None currently



  • Douwe Kiela, U of Cambridge
  • Akshay Krishnamurthy, CMU
  • Ioannis Paparrizos, Columbia
  • Daniel Barowy, U Mass
  • Kai-Wei Chang, University of Illinois at Urbana-Champaign (UIUC)
  • Aaron Schein, Umass Amherst
  • Luke Stark, New York U
  • Adam Obeng, Columbia
  • Amit Sharma , Cornell University
  • Qiushi (Andrew) Mao, Harvard
  • Sara Kingsley, U Mass
  • Pablo Barrio, Columbia
  • Ran He, Columbia
  • Hoda Heidari, U Penn
  • Erik Zawadzki, CMU
  • Juergen Brandstetter, U of Canterbury
  • Chien-Ju Ho, UCLA
  • Noah Liebman, Northwestern
  • Andrey Simonov, U Chicago

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