Postdoctoral Research Associate · University of Oxford

SaarCohen

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

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.

Now
Postdoctoral Research Associate, Department of Computer Science, University of OxfordHosted by Prof. Michael Wooldridge
2021 – 2025
Ph.D. in Computer Science, Bar-Ilan University
2018 – 2021
M.Sc. in Computer Science, Bar-Ilan University
2013 – 2017
B.Sc. in Mathematics, Tel Aviv University
Portrait of Saar Cohen

02 Research

What I work on

  1. Coalitions & collective decisions

    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.

  2. Fair allocation over 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.

  3. Learning in multi-agent systems

    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.

  4. Principled & safe AI

    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

Current work

One recent paper from each research theme. 19 published or accepted papers, 3 working papers and 4 under review.

All publications

Coalitions & collective decisions

Fair allocation over time

Learning in multi-agent systems

Principled & safe AI

04 Recognition & service

Recognition and service

Awards & scholarships

  • 2025IAAI Best PhD Thesis Award
  • 2024CS@BIU Nadav Scholarship for Excellent PhD Students
  • 2023The President’s Scholarship Program for Outstanding Doctoral StudentsOn behalf of the president of Bar-Ilan University
  • 2022CS@BIU Nadav Scholarship for Excellent M.Sc. Students

Service

  • 2026Organizer, 3rd Workshop on Social Choice and Learning Algorithms (SCaLA’26)Held at IJCAI’26, Bremen, Germany
  • 2022–2026Program committee and reviewing: AAAI, AAMAS, IJCAI, ECAI, ICRA, IEEE Transactions on RoboticsDistinguished PC member: AAMAS’25, IJCAI’23