I am a Ph.D. candidate 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 broadly interested in machine learning theory, statistics, and optimization. My current focuses are deep learning theory (optimization and generalization) and causality.
Prior to MIT, I eared 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.
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.