Asuman Ozdaglar
EECS Department Head; MIT Schwarzman College of Computing Deputy Dean of Academics; MathWorks Professor
Studies optimization, game theory, and machine learning, including distributed optimization, game-theoretic learning, and multi-agent reinforcement learning.
Asuman Ozdaglar's lab at MIT works on learning theory & optimization, reasoning & neuro-symbolic AI and planning, control & multi-robot systems. 31 papers since 2023, 25 at top venues, mostly ICLR and NeurIPS. 21 alumni. Applications close .
At a glance
- papers, 2023–2026
- 8/ year
- at T1 venues
- 81%
- PhD students
- 0
- MS students
- 0
- alumni listed
- 21
Papers by yearfrom DBLP, 2023–2026
- 7 papers2026 · T1 6 · collecting
- 9 papers2025 · T1 7
- 8 papers2024 · T1 5
- 7 papers2023 · T1 7
T1 venuesOtherVenues: ICLR, NeurIPS, ICML, AISTATS, COLT
Research focuspaper titles that use each topic's words
- Learning theory & optimization12 papers
- Planning, control & multi-robot systems5 papers
- multi-agent reinforcement1 paper
- Reasoning & neuro-symbolic AI0 papers
- distributed optimization0 papers
Paper list
From DBLP.
20267 papers
- ICLR (T1)Beyond RLHF and NLHF: Population-Proportional Alignment under an Axiomatic Framework (opens in a new tab)
- ICLR (T1)Computing Equilibrium beyond Unilateral Deviation (opens in a new tab)
- ICLR (T1)Differentially Private Equilibrium Finding in Polymatrix Games (opens in a new tab)
- COLT (T1)Learning Decision-Sufficient Representations for Linear Optimization (opens in a new tab)
- ICLR (T1)Online Learning and Equilibrium Computation with Ranking Feedback (opens in a new tab)
- ACCOptimism as Risk-Seeking in Multi-Agent Reinforcement Learning (opens in a new tab)
- COLT (T1)Regret Minimization with Adaptive Opponents in Repeated Games (opens in a new tab)
20259 papers
- ICLR (T1)A Policy-Gradient Approach to Solving Imperfect-Information Games with Best-Iterate Convergence (opens in a new tab)
- ICML (T1)Contextual Optimization Under Model Misspecification: A Tractable and Generalizable Approach (opens in a new tab)
- Math. Oper. Res.Convergence and Stability of Coupled Belief-Strategy Learning Dynamics in Continuous Games (opens in a new tab)
- ICLR (T1)Do LLM Agents Have Regret? A Case Study in Online Learning and Games (opens in a new tab)
- ACL (T1)MAPoRL: Multi-Agent Post-Co-Training for Collaborative Large Language Models with Reinforcement Learning (opens in a new tab)
- CDCOptimal interventions in opinion dynamics on large-scale, time-varying, random networks (opens in a new tab)
- NeurIPS (T1)UFT: Unifying Supervised and Reinforcement Fine-Tuning (opens in a new tab)
- NeurIPS (T1)What Data Enables Optimal Decisions? An Exact Characterization for Linear Optimization (opens in a new tab)
- WWW (T1)Wikipedia Contributions in the Wake of ChatGPT (opens in a new tab)
20248 papers
- ICML (T1)A Unified Linear Programming Framework for Offline Reward Learning from Human Demonstrations and Feedback (opens in a new tab)
- AISTATS (T1)EM for Mixture of Linear Regression with Clustered Data (opens in a new tab)
- EMNLP (T1)MisinfoEval: Generative AI in the Era of "Alternative Facts" (opens in a new tab)
- Autom.Optimal adaptive testing for epidemic control: Combining molecular and serology tests (opens in a new tab)
- Oper. Res.Optimal and Differentially Private Data Acquisition: Central and Local Mechanisms (opens in a new tab)
- Oper. Res.Testing, Voluntary Social Distancing, and the Spread of an Infection (opens in a new tab)
- EC (T1)Two-Timescale Q-Learning with Function Approximation in Zero-Sum Stochastic Games (opens in a new tab)
- ICML (T1)Uniformly Stable Algorithms for Adversarial Training and Beyond (opens in a new tab)
20237 papers
- NeurIPS (T1)A Finite-Sample Analysis of Payoff-Based Independent Learning in Zero-Sum Stochastic Games (opens in a new tab)
- NeurIPS (T1)Multi-Player Zero-Sum Markov Games with Networked Separable Interactions (opens in a new tab)
- TAC (T1)Optimal Intervention in Transportation Networks (opens in a new tab)
- ICML (T1)Revisiting the Linear-Programming Framework for Offline RL with General Function Approximation (opens in a new tab)
- AISTATS (T1)Symmetric (Optimistic) Natural Policy Gradient for Multi-Agent Learning with Parameter Convergence (opens in a new tab)
- ICLR (T1)The Power of Regularization in Solving Extensive-Form Games (opens in a new tab)
- NeurIPS (T1)Time-Reversed Dissipation Induces Duality Between Minimizing Gradient Norm and Function Value (opens in a new tab)
Where alumni went21 listed
Apply to MITFall 2027
MIT EECS Computer Science PhD
- Round
- Single round
- Opens
- —
- GRE
- See program page
- English
- TOEFL/IELTS
- Fee
- USD 90
Confirmed on the program page.
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