Research Output
Source code for these papers is not public. For access, email me.
CRSP-FL: Efficiently Scaling Private Frequency Estimation to Resolve Data Heterogeneity in Federated Learning
SHARP-FL: Single-Round Global Frequency Reweighting for Private Federated Learning
SESAME: Communication-Optimal Secure Aggregation for Sign-Based Federated Learning
Maliciously Secure Federated U-Statistics with Non-Interactive Verifiable Noise
TAPAS: Efficient Two-Server Asymmetric Private Aggregation Beyond Prio(+)
Contributed talk: TPMPC 2026
Regulatable ML @NeurIPS2025, NeurIPS 2025 Workshop on GenAI in Finance
AgentCrypt: Advancing Privacy and Secure Computation in AI Agent Collaboration
Regulatable ML @NeurIPS2025,NeurIPS 2025 Workshop on Scaling Environments for Agents
Armadillo: Robust Secure Aggregation for Federated Learning with Input Validation
Early version: NeurIPS 2024 AIM-FM Workshop
Early versions: TPMPC ’25 · NeurIPS ’24 Workshop · AAAI ’25 Workshop
Method and System for Private Decentralized Forward-Secure Confidential Transactions
Published as US20240177150A1 (May 2024) · Granted Feb 2026