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I'm starting as an assistant professor at CMU in Fall 2017 with a 50/50 appointment in the ML and CS departments.

I am a PhD candidate in the EECS department at UC Berkeley, working with Martin Wainwright and Kannan Ramchandran. I am working in the areas of statistical learning theory and game theory, with a focus on problems in crowdsourcing. My thesis committee members are Martin Wainwright, Kannan Ramchandran, Christos Papadimitriou, and Tom Griffiths. In the summers of 2013 and 2014, I interned at Microsoft Research, Redmond working with Denny Zhou in the machine learning group.

I have been awarded the Microsoft Research PhD Fellowship for 2014-16 (thanks, MSR!), and previously the Berkeley Fellowship for 2011-13.

Recent news

My research interests lie in the areas of statistics, machine learning, information theory and game theory, with a focus on the application to crowdsourcing and human-centered systems. Here is a brief description of some of my work.
Statistical inference from crowdsourced data

There is a long and rich literature on learning from data from people. However, the models assumed therein make quite restrictive parameter-based assumptions---they assume that the behavior of any entity (a user, item, etc.) is governed by a single parameter. Although there are algorithms that work well when these assumptions are satisfied, such algorithms fail under violations of these limiting assumptions, which occurs frequently in practice. In my research, I propose a departure from this conventional approach, towards a paradigm of far richer permutation-based models. Under this new approach, I design provably optimal and robust algorithms for estimation, prediction and ranking in human-centered applications. Moreover, I prove that as compared to the parameter-based algorithms, my algorithms enjoy a surprising win-win in the statistical bias-variance tradeoff.

Selected papers:

"Unique" mechanisms for obtaining high-quality data from crowdsourcing

My research also targets robust collection of data via game-theoretic incentives. I have established a framework towards a novel principled approach for the design and selection of incentive mechanisms. I employ this framework to construct incentive mechanisms, and prove that my proposed multiplicative mechanisms are the one and only possible incentive mechanisms in various applications.

Selected papers:

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      At UC Berkeley:
          Statistical Learning TheoryAdvanced Topics in Learning and Decision Making
          Theoretical StatisticsAdvanced Theoretical Statistics
          Convex OptimizationRandom Processes in Systems
          Computational Biology

      At IISc:
          Detection And Estimation TheoryGraph Theory and Combinatorics
          Communication NetworksInformation Theory
          Error Control CodesAlgebra
          Wireless CommunicationWireless Networks
          Space-Time CodingDigital Communication