I am an Assistant Professor at The Elmore Family School of Electrical and Computer Engineering at Purdue University and the director of MINDS Group. Previously, I was a Postdoctoral scholar at the Oden Institute for Computational Engineering and Sciences at the University of Texas at Austin. I received my PhD and MSE degrees in Electrical and Computer Engineering department at UT Austin in 2020 and 2016, and my BS degree in Electrical Engineering from Sharif University of Technology in 2014.
MINDS Group has an opening for a postdoctoral postion on optimization for distributed machine learning. Please see here for more details.
Watch my talk on Privacy-Preserving Federated Learning at Federated Learning One World (FLOW) Seminar
My research interests include:
The goal of my research is to enhance the performance and capabilities of learning systems characterized by limited communication budget and data scarcity. In doing so, I design efficient algorithms with mathematical guarantees to render practical deployment of such systems possible in applications including medical imaging, bioinformatics, and dynamical systems (see the Research tab for more details and the Publication tab for a complete list of my papers).
For the full list of news please check the News tab.
Faster Non-Convex Federated Learning via Global and Local Momentum, The 2022 Conference on Uncertainty in Artificial Intelligence (UAI), 2022.
Robust Training in High Dimensions via Block Coordinate Geometric Median Descent, The 25th International Conference on Artificial Intelligence and Statistics (AISTATS), 2022.
Communication-Efficient Variance-Reduced Decentralized Stochastic Optimization over Time-Varying Directed Graphs, IEEE Transactions on Automatic Control, 2022.
No-Regret Learning with High-Probability in Adversarial Markov Decision Processes, Conference on Uncertainty in Artificial Intelligence (UAI), July 2021.
Randomized greedy sensor selection: Leveraging weak submodularity, IEEE Transactions on Automatic Control, Jan. 2021
Submodular Observation Selection and Information Gathering for Quadratic Models, International Conference on Machine Learning (ICML), June 2019.
For the full list of publications please check the Publication tab.