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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.

Recent News

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April 2022
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Co-organized ICLR 2022 workshop on "Machine Learning for Drug Discovery (MLDD)".

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February 2022
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We are organizing "GSK.ai GeneDisco Challenge" on active learning for drug discovery!

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January 2022
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Our paper on "Physical Derivatives: Computing policy gradients by physical forward-propagation" is on arXiv.

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January 2022
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Our paper "GeneDisco: A Benchmark for Experimental Design in Drug Discovery" is accepted for publication in International Conference on Learning Representations (ICLR) 2022!

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December 2021
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Our workshop proposal on "Machine Learning for Drug Discovery (MLDD)" is accepted to ICLR!

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October 2021
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Our paper "GeneDisco: A Benchmark for Experimental Design in Drug Discovery" is now on arXiv.

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September 2021
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I have started my Ph.D. at MIT EECS.