Multiple papers accepted at ITCS'26, FOCS'25, RANDOM'25, COLT'25, TQC'25, AISTATS'25; published in the Journal of Privacy and Confidentiality (JPC); involved in several research projects such as 'Boosting Algorithm Performance with Imperfect Advice' funded by the Australian Research Council; joined the Program Committee for ACM-SIAM Symposium on Discrete Algorithms (SODA 2026); co-chair of the Program Committee for IEEE Information Theory Workshop (ITW 2025) and General Chair for the 66th IEEE Symposium on Foundations of Computer Science (FOCS 2025).
Research Experience
Postdoc at the Stanford Theory Group and IBM Research Almaden, currently a Senior Lecturer at the University of Sydney and a member of the Sydney Algorithms and Computing Theory (SACT) group.
Education
Ph.D. from the Computer Science department of Columbia University, advised by Prof. Rocco Servedio; M.Sc. in Computer Science from the Parisian Master of Research in Computer Science; engineering degree from École Centrale Paris.
Background
Currently a Senior Lecturer in the School of Computer Science at the University of Sydney, with research interests in distribution testing, learning theory, and more broadly, randomized algorithms and the theory of machine learning. Focuses on understanding the computational aspects of learning and statistical inference under various resource or information constraints, as well as reliable and rigorous approaches to data privacy, specifically differential privacy.