Patrick Rebeschini

Patrick Rebeschini is a Lecturer in the Department of Statistics at the University of Oxford. Before joining Oxford, he held positions at Yale University, including Lecturer in Computer Science and Postdoctoral Associate at the Yale Institute for Network Science. He earned his Ph.D. in Operations Research and Financial Engineering from Princeton University, supervised by Ramon van Handel. His research spans high-dimensional probability, statistics, and optimization, focusing on designing computationally efficient and statistically optimal algorithms for machine learning and artificial intelligence.

His recent activities include organizing workshops on Online Learning and Game-Theoretic Statistics (2025), serving as Senior Area Chair for ICML 2025, and chairing sessions at the IMS Annual Meeting (2022). He co-organizes the joint Maths-Stats colloquium series at Oxford and contributes to doctoral training as a Co-Investigator for the Imperial-Oxford StatML Centre for Doctoral Training (CDT) and the Fundamentals of AI Erlangen Hub.

Scientific Awards:

• 2019 Oxford MPLS Teaching Award
• Excellence in Teaching Award from Princeton Engineering Council (2013)

Grants & Collaborations:

• Co-Investigator for StatML CDT and Fundamentals of AI Erlangen Hub
• Member of Bernoulli Society, Institute of Mathematical Statistics, and ELLIS

Teaching:

• Organized reading groups on learning theory (2017–2025)
• Module Leader for doctoral courses on Online Learning and Reinforcement Learning (2023)
• Supervisor for UNIQ+ DeepMind summer interns (2022–present)