Published numerous high-impact papers on differential privacy, including topics like the shuffle model, distributed mean estimation, histogram release, and stochastic convex optimization.
Key works include: 'Differentially Private Histograms in the Shuffle Model from Fake Users' (IEEE S&P 2022), 'The Limits of Pan Privacy and Shuffle Privacy for Learning and Estimation' (STOC 2021), and 'Distributed Differential Privacy via Shuffling' (EUROCRYPT 2019).
Publications in top-tier venues such as STOC, SODA, ICLR, EUROCRYPT, IEEE S&P, ITCS, and ICML.
May 2024: Co-authored a submission to TPDP'24.
April 2024: Co-authored a Google whitepaper on Confidential Federated Computations.