Research projects include improving the trade-offs between privacy and accuracy, advancing differentially-private synthetic data generation techniques, and exploring the relationship between differential privacy and other characteristics.
Research Experience
Leading the ParkLabML lab, which is dedicated to creating and improving machine learning tools that preserve privacy for a better world.
Background
Research Interests: Improving and developing methods in privacy-preserving machine learning, aiming to facilitate data analyses without sacrificing privacy. Areas of expertise include improving the trade-offs between privacy and accuracy, advancing differentially-private synthetic data generation techniques, and studying the interplay between differential privacy and other emerging characteristics such as interpretability, fairness, and causality. About: Our lab focuses on enhancing and developing privacy-preserving machine learning methods, especially for applications in healthcare.
Miscellany
Personal interests: A wholehearted approach to life.