Congratulations, Dr. Patris!

I participated today in the successful Ph.D. defense of Nikolas Patris, a student of my newly-tenured colleague Ioannis Panageas. Nikolas has been working on problems of learning Nash equilibria of games and more generally finding saddle points of smooth functions, a crossover area between theoretical computer science and machine learning. His results are theoretical but his papers on them are in machine learning conferences: “Exponential Lower Bounds for Fictitious Play in Potential Games”, NeurIPS 2023, arXiv:2310.02387 “Computing Nash Equilibria in Potential Games with Private Uncoupled Constraints”, AAAI 2024, arXiv:2402.07797 “Learning Nash Equilibria in Rank-1 Games”, ICLR 2024 “Improved Bounds for Online Facility Location with Predictions”, AAAI 2025 “(Doubly) Exponential Lower Bounds for Follow the Regularized Leader in Potential Games”, ICML 2026, arXiv:2601.23248 The main theme of his thesis and defense was that games can have a nice structure and still not be efficiently…