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.
I am an organizer of the 28th Annual MIT LIDS Student Conference! I will also give a talk about doubly robust causal feature selection.
I gave a talk at "Causality x AI/ML" about our work "Causal Feature Selection via Orthogonal Search".
Our workshop proposal "Machine Learning for Drug Discovery (MLDD)" has been accepted to ICLR 2023!
Our paper "Causal Feature Selection via Orthogonal Search" is accepted to Transactions on Machine Learning Research (TMLR) 2022.
Our paper "Pyfectious: A probabilistic hierarchical simulator of infectious diseases and fine-grained control measures" will be published in PLOS Computational Biology.
Our work "GeneDisco: A Benchmark for Experimental Design in Drug Discovery" is accepted for spotlight presentation at ReALML @ ICML.
Our work on "Federated Learning in Multi-Center Critical Care Research: A Systematic Case Study using the eICU Database" is placed on arXiv.