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 research interests lie at the intersection of artificial intelligence, generative AI, human-centered AI, federated learning/collaborative AI, and privacy with applications in healthcare, business analytics. My works have been published at top-tier AI conferences, including NeurIPS and AAAI, as well as journals including IEEE Transactions on Artificial Intelligence, and ACM. 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.