I am currently a CSoI Postdoctoral Fellow with worksite at Stanford University and UC Berkeley. Starting next summer, I will be an Assistant Professor at USC Marshall in the department of Data Sciences and Operations (Statistics group).
I completed my Ph.D. in Electrical Engineering at Stanford University, where I worked with Andrea Montanari. Before joining Stanford in 2009, I received B.S. degrees in Electrical Engineering and Mathematics from Sharif University of Technology.
I am broadly interested in the design and analysis of algorithms that reveal latent structures in data sets. My focus is to exploit such structures to develop both computationally and statistically efficient procedures for inference. In my research, I use and improve on techniques from various fields such as optimization, graphical models, statistics and probability.
For the complete list of publications, check here.
Confidence Intervals and
Hypothesis Testing for High-Dimensional Regression [Website]
Hypothesis Testing in High-Dimensional Regression under the Gaussian Random Design Model: Asymptotic Theory
Information-Theoretically Optimal Compressed Sensing via Spatial Coupling and Approximate Message Passing
Learning Topic Models and Latent Bayesian Networks Under Expansion Constraints
Efficient Reinforcement Learning for High Dimensional Linear Quadratic Systems
Localization from Incomplete Noisy Distance Measurements