Gabriele Dragotto is a Data X Postdoctoral Fellow at Princeton's Center for Statistics and Machine Learning and a Postdoctoral Research Associate at Princeton's Department of Operations Research and Financial Engineering. He holds a Ph.D. in Mathematics (2022) from Polytechnique Montréal, where he worked at the Canada Excellence Research Chair in Data Science for Real-Time Decision-Making on his thesis "Mathematical Programming Games". He received a B.Sc. in Engineering and Management (2018) from Politecnico di Torino, as part of the project Young Talents.
His research is at the interface of Mathematical Optimization, in particular Discrete Optimization, (Algorithmic) Game Theory, and Machine Learning. His main research interests are on nonconvex games, i.e., decision-making among a set of selfish and mutually-interacting agents that decide by solving complex optimization problems. Gabriele combines rigorous optimization tools with algorithmic game theory to design data-driven algorithms and theoretical insights to guide decision-makers toward efficient and socially-beneficial outcomes.