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 Jordan. In 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

• November 2023: New paper out: Contract Design With Safety Inspections

• October 2023: Just presented our work on bridging central and local differentially private mechanisms for data acquisition in INFORMS 2023 Annual Meeting in Phoenix, Arizona!

• October 2023: I just gave a talk at the BLISS seminar at UC Berkeley on our work “Optimal and Differentially Private Data Acquisition: Central and Local Mechanisms“!

• October 2023: Presented our paper “How Good Are Privacy Guarantees? Platform Architecture and Violation of User Privacy” at SLMath/MSRI Seminar on Market and Mechanism Design [video].

• September 2023: The conference version of our paper “How Good Are Privacy Guarantees? Platform Architecture and Violation of User Privacy” just got accepted to the Conference on Web and Internet Economics (WINE 2023)!

• August 2023: Our paper “Optimal and Differentially Private Data Acquisition: Central and Local Mechanisms” just got accepted for publication in the Operations Research journal!

• 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!

• May 2023: Presented our work on privacy guarantees and platforms behavior at the Marketplace Innovation Workshop.

• January 2023: New paper out: How Good Are Privacy Guarantees? Data Sharing, Privacy Preservation, and Platform Behavior

• 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.

• October 2022: I will present my work on privacy mechanisms for data markets at the Cornell ORIE Young Researchers Workshop.

• 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!

• July 2022: Our paper on robust accelerated gradient methods for distributed optimization is accepted for publication in the Journal of Machine Learning Research (JMLR)!

• July 2022: Presented our work on differentially private data acquisition at two more workshops:
  • ICML workshop on Theory and Practice of Differential Privacy (TPDP), Baltimore, USA.
  • The workshop on Differential Privacy and Statistical Data Analysis, The Fields Institute for Research in Mathematical Sciences, University of Toronto.

• July 2022: Attended the Economics and Computation (EC’22) conference in Boulder, Colorado, to present our accepted paper on differentially private data acquisition which will appear as an extended abstract [Video].

• 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”.

• June 2022: Presented our work on differentially private data acquisition at the INFORMS Revenue Management and Pricing (RM&P) Section Conference & Manufacturing and Service Operations Management Conference (MSOM).

• May 2022: Presented our work on differentially private data acquisition at the seventh Marketplace Innovation Workshop.