I am a final-year PhD candidate in Electrical and Computer Engineering at the University of California, San Diego (UCSD), advised by Prof. Bill Lin. I hold an M.S. from UCSD and a B.S. from Amirkabir University of Technology, both in Electrical and Computer Engineering.

My current research focuses on the intersection of Artificial Intelligence, Healthcare, and Business, with broad interests in decentralized AI, generative AI, human-centered AI, federated learning/collaborative AI, information systems, operations management, and economics. My work has resulted in publications at top-tier AI conferences, including NeurIPS and AAAI, as well as journals like IEEE Transactions on Artificial Intelligence. I have also served as a reviewer for several top-tier AI conferences such as ICLR, and AISTAT and journals such as IEEE TMC, and SIAM Journal on Optimization.

In addition to my academic experience, I have completed AI research internships at Tesla Autopilot and Qualcomm AI Research.

News

[December 2024] Our paper “Federated Learning Client Pruning for Noisy Labels” Accepted to ACM Transactions on Modeling and Performance Evaluation of Computing Systems [Paper, Code].

[October 2024] INFORMS Annual Meeting I will present our work “Deep Causal Inequalities: Demand Estimation in the Differentiated Products Markets” at INFORMS 2024 on Sunday, October 20, 2:15 PM - 3:30 PM, summit - 338.

[September 2024] Our paper “Towards Diverse Device Heterogeneous Federated Learning via Task Arithmetic Knowledge Integration” Accepted to NeurIPS 2024 [Paper, Project Website, Code].

[August 2024] Our paper “Large Scale Delocalized Federated Learning Over a Huge Diversity of Devices in Emerging Next-Generation Edge Intelligence Environments” Accepted to ICCAD 2024 as invited talk.