ConferencePresented as Oral
Offline Learning of Nash Stable Coalition Structures with Possibly Overlapping Coalitions
AAMAS 2026
Postdoctoral Research Associate · University of Oxford
I study how agents make decisions and learn when they interact strategically, drawing on game theory, computational social choice and machine learning. My work ranges from coalition formation and fair allocation to large language models and AI safety.
01 About
I am a Postdoctoral Research Associate in the Department of Computer Science at the University of Oxford, hosted by Prof. Michael Wooldridge.
My research is about multi-agent systems, in which several agents with their own goals make decisions that affect one another. I approach them mainly through game theory and computational social choice, and through machine learning. Many of the problems I study are sequential; agents or resources arrive over time, and decisions have to be made before the future is known.
One strand of my work concerns how agents form coalitions and how goods and resources can be allocated fairly, often in these online settings. Another concerns learning: how agents can learn their preferences and stable outcomes from feedback, and how learning models such as graph neural networks can be given provable guarantees. Most recently, I have been working on the safety of large language models, studying self-play red teaming as a game between an attacker and a defender.
I completed my Ph.D. at Bar-Ilan University with Prof. Noa Agmon, with a thesis on coalition formation in sequential decision-making under uncertainty.

02 Research
Game theory · Computational social choice
How self-interested agents group together, and which guarantees of stability, welfare and fairness survive when groups form online, one arrival at a time.
Computational social choice · Online algorithms
Dividing goods, houses and resources that arrive sequentially and must be assigned irrevocably or with limited recourse, and how far subsidies, resource augmentation or predictions can restore fairness.
Online learning · Reinforcement learning · Multi-agent systems
Agents that learn their preferences and strategies from feedback, from online and offline learning of stable partitions to coordination in multi-agent reinforcement learning and robot swarms.
Machine learning · Large language models · AI safety
Learning architectures with provable guarantees, such as convexified graph neural networks, and game-theoretic approaches to the safety of large language models.
03 Publications
One recent paper from each research theme. 19 published or accepted papers, 3 working papers and 4 under review.
All publicationsCoalitions & collective decisions
ConferencePresented as Oral
AAMAS 2026
Fair allocation over time
Working PaperarXiv 2026-09
Learning in multi-agent systems
ConferencePresented as Oral
ECAI 2025
Principled & safe AI
ConferencePresented as Oral
NeurIPS 2026 [To Appear]
04 Recognition & service
05 Contact