I am a Principal Researcher at MSR New York City. Previously I was a researcher at MSR Silicon Valley, after receiving my Ph.D. in Computer Science from Cornell and a postdoc at Brown.
My research interests are in algorithms and theoretical computer science, spanning learning theory, algorithmic economics, and networks. I am particularly interested in online machine learning and exploration-exploitation tradeoff, and their manifestations in socioeconomic environments. Much of my earlier research was on the analysis of Internet and social networks, metric embeddings, and distance/routing data structures. My work has been recognized with the best paper award at ACM EC 2010, the best paper nomination at WWW 2015, and the best student paper award at ACM PODC 2005.
Full publication list (with abstracts, by year and by topic).
My book, Introduction to Multi-Armed Bandits, has been published with Foundations and Trends in Machine Learning in November 2019. It is based on this class at UMD, and partially on that class at Columbia, providing a textbook-like treatment of the subject. A plain-formatted draft can also be found on arxiv. Here’s a related post on MSR blog.
Relevant project/group pages at MSR
Economics and Computation
Real-world Reinforcement Learning
Multiworld Testing: a methodology and a system for contextual bandits.
Explore-exploit learning at MSR-NYC
Multi-armed bandits at MSR-SV (inactive since the closure of MSR-SV)
Former interns: Mahsa Derakhshan (2019), Karthik Abinav Sankararaman (2018), Jieming Mao (2018), Manish Raghavan (2017), Mathias Lecuyer (2017), Steven Wu (2015, 2016), Chien-Ju Ho (2013, 2014), Ashwinkumar Badanidiyuru (2012, 2013), Sigal Oren (2011), Shiri Chechik (2010), Yogeshwer Sharma (2008).