We study learning and control in multi-agent systems—where humans and AI make decisions together. Our goal: principled methods to analyze, steer, and optimize socio-technical systems. Therefore, at the GAIN Lab,
- We focus on developing a foundational understanding of learning and autonomy in complex, dynamic, and multi-agent systems.
- We develop new methodologies for analyzing, controlling, and optimizing socio-technical systems.
- We apply these methodologies to specific problems in urban mobility, robotics, and the digital economy.
See the following posters and the video recordings as examples of our recent research projects:
We are looking for new team members. Please get in touch with us if you are interested in!
Recent News
- [Oct. 2025] Dr. Sayin is Technical Program Co-Chair of GameSec 2025 – Conference on Game Theory and AI for Security (Athens, October 13–15). The conference will feature keynote speakers Marta Kwiatkowska (Oxford), Milind Tambe (Harvard & Google Deepmind), Michael I. Jordan (Inria & UC Berkeley), and Lorenzo Cavallaro (UCL).
- [Sep. 2025] New team members at GAIN! We are pleased to welcome Arda Koşay and Ozan Cem Baş to the GAIN Lab.
- [Jun. 2025] Our paper “Logit-Q dynamics for efficient learning in stochastic teams” got accepted to the SIAM Journal on Control and Optimization.
- [May 2025] Our paper “Convergence of heterogeneous learning dynamics in zero-sum stochastic games” got accepted to the IEEE Transactions on Automatic Control.
- [May 2025] Our joint work “Dynamic feedback strategies for duopolies over partially observed consumer networks” with Dr. Saeed Ahmed from the University of Groningen got accepted to the Dynamic Games and Applications.
- [Mar. 2025] Dr. Sayin gave an invited talk titled “Multi-team reinforcement learning” at the Algorithmic Learning in Games Seminar (ALIGS) Series.