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 Algorithmic Game Theory and Mathematical Optimization, and it focuses on multi-agent decision-making in systems where selfish and mutually-interacting agents solve complex optimization problems (e.g., non-convex 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.