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 for analyzing biological data and health data in general. 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 democratized foundation models in the realm of biology. I have also extensively worked on, and maintain my interest in, research problems in wireless communication networks, including fundamental information-theoretic limits and learning-based resource allocation algorithms.
What’s New
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May 2024: New preprint: Learning to Slice Wi-Fi Networks: A State-Augmented Primal-Dual Approach.
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Apr. 2024: Paper submissions are open for our ICML 2024 AccMLBio Workshop on Accessible and Efficient Foundation Models for Biological Discovery. Extended Deadline: May 28, 2024.
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Apr. 2024: New preprint: Decentralized Learning Strategies for Estimation Error Minimization with Graph Neural Networks.
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Mar. 2024: Our proposed workshop “Accessible and Efficient Foundation Models for Biological Discovery” has been accepted to ICML 2024.
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Feb. 2024: Gave an invited talk on my recent work on resilient wireless resource management at the Center of Excellence on Agile Waveform Design for Communication Networks in Contested Environments.
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Jan. 2024: New preprint: Aggregating Residue-Level Protein Language Model Embeddings with Optimal Transport.
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Dec. 2023: New preprint: Robust Stochastically-Descending Unrolled Networks.
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Dec. 2023: Our paper “State-Augmented Information Routing in Communication Systems With Graph Neural Networks” has been accepted to IEEE ICASSP 2024.
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Dec. 2023: Gave an invited talk on my recent work on machine learning with constraints at the FIU School of Computing and Information Sciences.
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Oct. 2023: New preprint: Primal-Dual Continual Learning: Stability and Plasticity through Lagrange Multipliers.
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Oct. 2023: New preprint: Learning State-Augmented Policies for Information Routing in Communication Networks.
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Aug. 2023: Excited to join the faculty of the Department of Biostatistics & Bioinformatics at Duke University.
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May 2023: New preprint: Stochastic Unrolled Federated Learning.
