About me

I’m an incoming assistant professor at IIT Madras, starting in April 2024. My group will study the theory and practice of ML & AI, focusing on privacy-preserving, federated, and robust learning, with applications to LLMs and generative AI.

Currently, I’m a visiting researcher (postdoc) at Google Research in the Federated Learning team. I obtained my PhD from the Paul G. Allen School of Computer Science & Engineering at the University of Washington, where I was fortunate to be advised by Zaid Harchaoui and Sham Kakade. Before that, I worked with Nina Balcan for my Master’s at Carnegie Mellon University and received an undergraduate degree from IIT Bombay.

My research has been recognized by a NeurIPS outstanding paper award and I was a 2019-20 J.P. Morgan PhD Fellow.

Contact me at [my-last-name] @ cs [dot] washington [dot] edu.

Research highlights

Some highlights from my previous research include:

  • evaluating synthetic data generation by LLMs and generative AI [NeurIPS ‘21 Outstanding Paper Award, JMLR 2023]
  • differentially private learning, including optimization [ICLR ‘24], auditing [NeurIPS ‘23], datasets [NeurIPS ‘23], and LLM privacy attacks [ArXiv ‘23]
  • robust aggregation for federated learning [FL-ICML ‘20 Long Oral, TSP ‘22]
  • differentially private superquantile aggregation for fair federated learning [DistShift-NeurIPS ‘22 Spotlight, MLJ ‘23].

News

  • [Mar. 2024] Paper on robust federated learning has been identified as one of IEEE Signal Processing Society’s top 25 downloaded articles from Sept. 2022 - Sept. 2023!
  • [Feb. 2024] Concluded my time as a visiting researcher (postdoc) at Google Research where I worked on various aspects of differential privacy at the user-level [final slides, Papers: ICLR ‘24, NeurIPS ‘23, NeurIPS D&B ‘23, ArXiv ‘23]. I’m grateful to the team for such a wonderful experience!
  • [Dec. 2023] Paper on MAUVE Scores for Generative AI accepted to JMLR!
  • [Jan. 2023] Paper on Differentially Private Superquantile Aggregation for Federated Learning accepted to the Machine Learning Journal! See the Project Page