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I am a Ph.D. student in the Electrical Engineering and Computer Science Department at MIT. I am very fortunate to be advised by Professor Patrick Jaillet in the Laboratory for Information & Decision Systems (MIT LIDS). I am also advised by Professor Gabriele Farina. My research interests broadly encompass machine learning theory, statistics, and optimization. Currently, I am focused on computational-statistical trade-offs, the understanding of overparameterized models (including non-convex optimization and generalization), learning dynamics in games and causality.
Prior to MIT, I earned a B.Sc. degree in Computer Engineering from Sharif University of Technology. During my undergrad, I was a research assistant at Max Planck Institute for Intelligent Systems (MPI-IS) under supervision of Professor Bernhard Schölkopf and Professor Stefan Bauer. Besides, I spent a couple of months working with Professor Martin Jaggi at EPFL.
Here is a link to my google scholar.
Recent News
Co-organized ICLR 2022 workshop on "Machine Learning for Drug Discovery (MLDD)".
We are organizing "GSK.ai GeneDisco Challenge" on active learning for drug discovery!
Our paper on "Physical Derivatives: Computing policy gradients by physical forward-propagation" is on arXiv.
Our paper "GeneDisco: A Benchmark for Experimental Design in Drug Discovery" is accepted for publication in International Conference on Learning Representations (ICLR) 2022!
Our workshop proposal on "Machine Learning for Drug Discovery (MLDD)" is accepted to ICLR!
Our paper "GeneDisco: A Benchmark for Experimental Design in Drug Discovery" is now on arXiv.
I have started my Ph.D. at MIT EECS.