Research Area: Trustworthy Machine Learning and Systems Security, with a focus on building secure and privacy-preserving Machine Learning systems, watermarking for intellectual property protection and detection of AI-generated content, and adversarial robustness
Thesis: Recovering Utility in LDP Schemes by Training with Noise^2 - Invented methods to improve model utility under local differential privacy constraints while preserving strong privacy guarantees