I am an Assistant Research Professor in the Department of Biostatistics & Bioinformatics at Duke University. Prior to that, I spent more than four years as a Research Scientist in the Tech industry, followed by a two-year postdoctoral research position at the University of Pennsylvania. I received my Ph.D. in Electrical Engineering from the University of Southern California in 2016 and my M.Sc. in Electrical and Computer Engineering from Cornell University in 2014, both under Prof. Salman Avestimehr. I spent Summer 2015 as a Research Intern at Bell Labs under Prof. Mohammad Ali Maddah-Ali. I also received my B.Sc. in Electrical Engineering from Sharif University of Technology in 2011 under Prof. S. Jamaloddin Golestani.
My current research interests span the foundations of machine learning, artificial intelligence, and signal processing, and their applications in developing novel methods to analyze biological and clinical data for advancing basic and health sciences. I am especially interested in novel protein language model (PLM) and graph neural network (GNN) architectures for learning over protein sequence and structure data, as well as efficient and transparent foundation models in biology and healthcare. I have also extensively worked on research problems in wireless communication networks, including fundamental information-theoretic limits and learning-based resource allocation algorithms.
What’s New
Apr. 2025: Our paper, Black-box Optimization of CT Acquisition and Reconstruction Parameters: A Reinforcement Learning Approach, has been accepted to SPIE Medical Imaging.
Mar. 2025: Our paper, Aggregating Residue-Level Protein Language Model Embeddings with Optimal Transport, has been accepted to Bioinformatics Advances.
Mar. 2025: New preprint: Opportunistic Routing in Wireless Communications via Learnable State-Augmented Policies.
Feb. 2025: New preprint: ESPFormer: Doubly-Stochastic Attention with Expected Sliced Transport Plans.
Feb. 2025: Together with colleagues from Johns Hopkins University and the University of Pennsylvania, we presented a tutorial on “Graph Neural Networks: Architectures, Fundamental Properties and Applications” at AAAI 2025.
Dec. 2024: Our paper, State-augmented Opportunistic Routing in Wireless Communication Systems with Graph Neural Networks, has been accepted to IEEE ICASSP 2025.
