Reza Shokri is a NUS Presidential Young Professor of Computer Science. His research focuses on data privacy and trustworthy machine learning. He is a recipient of the IEEE Security and Privacy (S&P) Test-of-Time Award 2021, for his paper on quantifying location privacy. He received the Caspar Bowden Award for Outstanding Research in Privacy Enhancing Technologies in 2018, for his work on analyzing the privacy risks of machine learning models. Last years, Reza received the NUS Early Career Research Award 2019, VMWare Early Career Faculty Award 2021, and Intel Faculty Research Award (Private AI Collaborative Research Institute) 2021. He obtained his PhD from EPFL.
Reza is interested in designing methods to quantitatively measure the privacy risks of data processing algorithms, and build scalable schemes for generalizable machine learning models that are also privacy-preserving, robust, interpretable, and fair. The research is on analyzing the trade-offs between different pillars of trust in machine learning for practical scenarios, and on resolving such conflicts with rigorous mathematical guarantees. His team is currently working on many interesting problems in this domain, including trustworthy federated learning, differential privacy for machine learning, fairness versus privacy in machine learning, privacy-aware model explanations, privacy-preserving data synthesis, and quantifying privacy risks of data analytics.