Mingxun Zhou

Mingxun Zhou is an Assistant Professor in the Department of Computer Science and Engineering at the Hong Kong University of Science and Technology. He received his PhD in Computer Science from Carnegie Mellon University in 2025 and his Bachelor's degree from Peking University in 2020. His research interests include applied cryptography, differential privacy, and security and privacy in machine learning. His primary goal is to design practical algorithms with theoretical principles that achieve a careful balance between utility and provable security and privacy guarantees in real-world applications. He also works on related topics including blockchain and information retrieval.

Mingxun Zhou is a researcher specializing in Security and Privacy. His work focuses on developing efficient and secure data retrieval and analysis techniques, with an emphasis on private information retrieval (PIR), differential privacy, and blockchain security. Zhou's research includes innovations such as 'Piano,' which offers sublinear server computation for PIR, and 'Squirrl,' which automates attack analysis on blockchain incentive mechanisms using deep reinforcement learning. He also explores privacy-preserving data aggregation, watermarking, and secure data joining protocols. His contributions aim to enhance privacy, security, and efficiency in data systems and distributed environments.