Lorenzo Croissant

Lorenzo Croissant is a Researcher affiliated with CREST at ENSAE Paris. He is a member of the FairPlay Inria team (joint with CREST and Criteo AI Lab). His research focuses on decision theory under uncertainty, bridging probability theory, control theory, and machine learning.

His work spans

• Reinforcement Learning
• Bandit Algorithms
• Optimal Transport
• Learning-Augmented Algorithms
• Auction Theory

He explores stochastic processes, optimal control, and online decision-making frameworks, with recent publications in journals like Stochastic Processes and their Applications and conferences including ICML and COLT.

At ENSAE, he contributes to teaching and research, leveraging collaborations with Criteo AI Lab and Inria. His methodological innovations connect infinite-dimensional linear bandits, dynamic programming, and algorithmic fairness in economic systems.