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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, and Computational 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.



Ashton Anderson
Postdoc Researcher

Research Area: Computational Social Science


Ashton Anderson is a postdoc researcher at Microsoft Research NYC. His research is driven by a desire to better understand the increasingly digital social systems that shape our society. He studies a wide array of computational social science problems ranging from mapping the structure of how information and products spread through social networks to investigating how to use badges to incentivize people online. Prior to joining Microsoft Research, he received his Master's and PhD in Computer Science from Stanford University, where he was supported by a Google PhD Fellowship in Social Computing.


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.



Hussam Abu-Libdeh 
Postdoc Researcher

Research Area: Computer Systems 


Hussam is a distributed systems researcher. His previous work focused on data storage, data center networking, replication protocols, data consistency, distributed factor graph processing, and group communication primitives. He received a PhD in computer science from Cornell University, under the tutelage of Robbert van Renesse.


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.



Markus Cozowicz, Research Software Design Engineer (RSDE) II
Markus received an M.Sc. in Computer Science from Vienna University of Technology and an M.Sc. in Statistics from University of Vienna.

He joined Microsoft Research Advanced Technology Labs Europe in Munich where he worked on time series anomaly detection in streaming systems such as Azure Stream Analytics. He is now a senior research engineer at Microsoft Research New York and works on systems for machine learning such as Multi World Testing (contextual bandits).


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
Senior 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
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: Computational Social Science


Dan Goldstein works at the intersection of behavioral economics and computer science. 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's Behavioral Insights Team (aka Britain’s “nudge unit”). He was elected President of the Society for Judgment and Decision Making for the year 2015-2016. 



Etan Green
Postdoc Researcher
Research Area: Economics


Etan joined Microsoft Research after doctoral studies at Stanford University’s Graduate School of Business. He uses field data to examine decision making by experts and crowds. Subjects of his studies include umpires in Major League Baseball, referees in the National Basketball Association, kickoff returners in the National Football League, pundits at ESPN, sports bettors, airline pilots, and grant reviewers at the National Institutes of Health. His work has been featured on National Public Radio, Businessweek, FiveThirtyEight, and other outlets. 


Shawndra Hill

Research Area: Machine Learning, Computational Social Science


Shawndra Hill studies data mining and machine learning and their alignment with business problems. Specifically, she researches the value to companies of mining data on how consumers interact with each other on social media – usually for targeted marketing, advertising, public health and fraud detection. Her current research focuses on the interactions between TV content and Social Media. Shawndra holds a B.S. in Mathematics from Spelman College, a B.E.E. from the Georgia Institute of Technology and a Ph.D. in Information Systems from NYU's Stern School of Business.


Jake Hofman
Senior 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. 



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.


Akshay Krishnamurthy

Postdoc Researcher

Research Area: Machine Learning 


Akshay recently received his PhD from Carnegie Mellon University where he was advised by Aarti Singh. His research interests are broadly in machine learning and statistics, with a focus on the design and analysis of interactive learning algorithms. He will join the Department of Computer Science at UMass Amherst in Fall 2016.


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. 



Kati London 

Senior Researcher
Research Area: Social Computing


Kati is currently a Senior Researcher at Microsoft Research, FUSE (Future User Social Experiences) [Microsoft Research] / [FUSE]. Previously, she was Director of Product for Zynga New York and Vice President and Senior Producer at Area/Code (acquired by Zynga). In 2012 she became Innovator-in-Residence at USC's Annenberg School, where she led workshops in Design Patterns for Autonomous Objects.


Andrew Mao
Postdoc Researcher
Research Area: Computational Social Science


Andrew Mao's work focuses studying the collective behavior of people connected by the Internet, such as teamwork in online communities and effectiveness of crowdsourcing systems. He is particularly interested in using novel experimental and computational techniques to bring theory closer to the real world, and specializes in gathering data from real-time, interactive behavioral experiments. Andrew received his PhD from Harvard University in 2015, where he was advised by Yiling Chen. He received a Yahoo! Key Scientific Challenges award in 2011, and obtained a B.S.E. in Computer Science and a B.S. in Economics from the University of Pennsylvania in 2009. 


Dan Melamed
Principal Applied Scientist
Research Area: Machine Learning


Dan loves both research and engineering. He has held positions at various start-ups, at AT&T Labs-Research, and on the CS faculty of New York University, where he received an NSF CAREER award. Most of his academic publications have been on natural language processing, ranging from the highly theoretical (formal language theory) to the purely practical (machine translation). His engineering work has resulted in numerous software packages, in areas such as syntactic parsing, machine learning, plagiarism detection, computational advertising, anomaly detection, and trainable virtual agents. He holds a PhD in Computer and Information Science from the University of Pennsylvania.


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
Senior Researcher
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 is an economist at Microsoft Research. 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, 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 been utilized by Bing, Cortana, and Xbox. And, he correctly predicted 50 of 51 Electoral College outcomes in February of 2012, average of 20 of 24 Oscars from 2013-5, and 15 of 15 knockout games in the 2014 World Cup.



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. In 2002, he became a Professor of Computer Science at Princeton University. He joined Microsoft Research in 2014. 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.



Siddhartha Sen

Research Area: Computer Systems, Algorithms and Theory


Siddhartha Sen is a researcher at MSR NYC (previously at the MSR SVC lab until its closure in 2014). He designs and builds distributed systems that are provably scalable and robust, by tackling theoretical questions that arise during system creation. He is passionate about data structures and algorithms: several of his systems are centered around them, and his data structures have been incorporated into undergraduate textbooks and curricula. Lately, he has been collaborating with machine learning experts to build a platform for large-scale decision making, and with computational social science and prediction markets experts to build a platform for real-time social media analysis. Siddhartha received his BS degrees in computer science and mathematics and his MEng degree in Computer Science from MIT. From 2004-2007 he worked as a developer at Microsoft, building a network load balancer for Windows Server. He holds several patents for this work. He received his PhD from Princeton University in 2013, spending his final year as a junior research scientist at New York University. Siddhartha received the first Google Fellowship in Fault-Tolerant Computing in 2009 and the best student paper award at PODC in 2012.



Amit Sharma

Postdoc Researcher
Research Area: Computational Social Science


Amit joined Microsoft Research after completing his Ph.D. in computer science at Cornell University. His research focuses on understanding the underlying decision processes that shape people's activities online, with a particular emphasis on the effect of recommendation systems and social influence. More generally, his work contributes to methods for causal inference on online systems, combining both experimental and observational methods of inquiry. He received the 2012 Yahoo! Key Scientific Challenges Award.


Chinmay Singh

Senior Research Software Design Engineer (RSDE)

Chinmay Singh received B.Tech. in Computer Science from Indian Institute of Technology Kharagpur in 2005 and worked for Oracle, Disney and few startups before joining Microsoft IDC in 2013. He worked on MSN Prime and Windows 10 releases in India and Canada prior to joining MSR in 2015. At Microsoft Research Lab in New York City, his first project is with the computational social science team.


Alex Slivkins
Senior Researcher
Research Area: Algorithms and Theory, Algorithmic Economics, Machine Learning


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: Computational Social Science


Siddharth “Sid” Suri is a computational social scientist. His research interests lie at the intersection of computer science, behavioral economics and crowdsourcing. Sid’s early work analyzed the relationship between network topology and human behavior. More recently, 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 and was nominated for a best paper award in WWW 2015. 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 associate 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, Machine Learning 


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
Senior Researcher
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 earning, 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 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 Social Science


Duncan Watts is a principal researcher at Microsoft Research and an AD White Professor at Large at Cornell University. Prior to joining MSR in 2012, he was from 2000-2007 a professor of Sociology at Columbia University, and then a principal research scientist at Yahoo! Research, where he directed the Human Social Dynamics group. 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, and has been recognized by the 2009 German Physical Society Young Scientist Award for Socio and Econophysics, the 2013 Lagrange-CRT Foundation Prize for Complexity Science, and the 2014 Everett Rogers Prize. He is also the author of three books: Six Degrees: The Science of a Connected Age (W.W. Norton, 2003); Small Worlds: The Dynamics of Networks between Order and Randomness (Princeton University Press, 1999); 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
Senior Researcher
Research Area: Algorithmic Economics, Machine Learning


Jenn Wortman Vaughan is a Researcher at Microsoft Research, New York City. The goal of her research is to develop mathematically rigorous, empirically grounded frameworks to understand and design algorithms for eliciting and aggregating information, preferences, and beliefs. Her research draws on ideas from economics, machine learning, probability theory, optimization, and beyond. For several years, her research has centered on elicitation and aggregation using prediction markets, wagering mechanisms, and other crowdsourcing approaches. Jenn came to MSR in 2012 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. 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 (PECASE), and a handful of paper awards at conferences including COLT, UAI, and EC. In her “spare” time, Jenn is involved in a variety of efforts to support 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 at this time


    • Zhiwei (Steven) Wu, Penn (March 14, 2016 - June 03, 2016)
    • Rishabh Mehrotra, University College London (March 28, 2016 - June 17, 2016)
    • Adrian Benton, John Hopkins University (March 28, 2016 - June 17. 2016)
    • Abigail Jacobs, CU Boulder (April 04, 2016 - July 22, 2016)
    • Jia Liu, Columbia (May 02, 2016 - July 29, 2016)
    • Rupert Freeman, Duke (May 16, 2016 - August 19, 2016)
    • Michael Zhao, MIT (May 16, 2016 - August 19, 2016)
    • Ashudeep Singh, Cornell (May 23, 2016 - August 12, 2016)
    • Yu-Xiang Wang, CMU (May 23, 2016 - September 9, 2016)

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