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I am the Gamelin Postdoctoral Fellow at the Simons Laufer Mathematical Sciences Institute (formerly MSRI), where I am with the Mathematics and Computer Science of Market and Mechanism Design program, and at UC Berkeley, where I am hosted by Michael JordanIn the summer 2023, I obtained my Ph.D. in the Electrical Engineering and Computer Science from MIT, where I worked with Asu Ozdaglar and Daron Acemoglu. 

My research interests lie in the span of machine learning theory, game theory, algorithmic market design and mechanism design, optimization, and privacy. In particular, I work toward understanding different aspects and challenges in deploying machine learning algorithms, from convergence and performance guarantees to their interactions with strategic users and potential societal considerations.

My Ph.D was generously supported by the Apple Scholars in AI/ML PhD fellowship, the MathWorks Engineering Fellowship, and the Siebel Scholarship. I spent summer 2020 as a research intern at Apple ML Privacy team. Before coming to MIT, I earned a dual B.Sc. degree in Electrical Engineering and Mathematics from Sharif University of Technology. 

Here are links to my Google Scholar and my CV.

Recent News

  • July 2023: Presented our work on privacy guarantees and platforms behavior at the Graduating Bits workshop at EC 2023 in London!
  • July 2023: Presented two papers, one on bridging central and local differentially private mechanisms for data acquisition and the other one on privacy guarantees and platforms behavior, at the INFORMS Revenue Management and Pricing (RM&P) at the Imperial College Business School, London!
  • June 2023: I passed my Ph.D. thesis defense! Huge thanks to my committee members Asu, Daron, and Costis!
  • December 2022: Attended the NeurIPS 2022 conference to present our paper on Bridging Central and Local Differential Privacy in Data Acquisition Mechanisms!
  • October 2022: I presented the following two works at 2022 INFORMS Annual Meeting:
    • Privacy Costs Of Strategic Data Sharing: Implications Of Shuffling
      • with Daron Acemoglu, Ali Makhdoumi, Azarakhsh Malekian, and Asuman Ozdaglar.
    • Optimal And Differentially Private Data Acquisition: Central And Local Mechanisms
      • with Ali Makhdoumi, Azarakhsh Malekian, and Asuman Ozdaglar.
  • September 2022: Attended the Allerton Conference to present the following two works:
    • Optimal Private Data Acquisition: Central and Local Differential Privacy
    • Personalized Federated Learning: A Meta-learning Approach
  • September 2022: Our paper on Bridging Central and Local Differential Privacy in Data Acquisition Mechanisms just got accepted to NeurIPS 2022!
  • June 2022: Received a minor revision decision from the Operations Research journal for our paper titled “Optimal and Differentially Private Data Acquisition: Central and Local Mechanisms”.