Fei Fang
Associate Professor
Combines machine learning and game theory for multi-agent decision-making, applied to security, sustainability and mobility under the AI for social good theme.
Fei Fang's lab at CMU works on reasoning & neuro-symbolic AI, reinforcement learning and planning, control & multi-robot systems. 38 papers since 2023, 25 at top venues, mostly AAAI and AAMAS. 4 PhD students. 10 alumni. Applications close .
At a glance
- papers, 2023–2026
- 10/ year
- at T1 venues
- 66%
- PhD students
- 4
- MS students
- 0
- alumni listed
- 10
Papers by yearfrom the lab homepage, 2023–2026
- 4 papers2026 · T1 2 · collecting
- 13 papers2025 · T1 8
- 13 papers2024 · T1 10
- 8 papers2023 · T1 5
T1 venuesOtherVenues: AAAI, AAMAS, ICLR, NeurIPS, AAAI/ACM AIES
Research focuspaper titles that use each topic's words
- Reinforcement learning15 papers
- Planning, control & multi-robot systems11 papers
- Reasoning & neuro-symbolic AI1 paper
- social good0 papers
- combines machine0 papers
Paper list
From the lab homepage.
20264 papers
- IAAIPeerCoPilot: A Language Model-Powered Assistant for Behavioral Health Organizations
- IAAIRescueLens: LLM-Powered Triage and Action on Volunteer Feedback for Food Rescue
- AAAI (T1)Safe Multi-Agent Reinforcement Learning with Natural Language Constraints
- ICLR (T1)Strategic Planning and Rationalizing on Trees Make LLMs Better Debaters
202513 papers
- NAACL (T1)CBT-Bench: Evaluating Large Language Models on Assisting Cognitive Behavior Therapy
- AAAI (T1)Deviate or Not: Learning Coalition Structures with Multiple-bit Observations in Games
- AAAI (T1)Improving Community-Participated Patrol for Anti-Poaching
- AAAI/ACM AIESIncorporating Human Preferences into Interpretable Reinforcement Learning with Tree Policies
- ICLR (T1)LICORICE: Label-Efficient Concept-Based Interpretable Reinforcement Learning
- ICML (T1)M3HF: Multi-agent Reinforcement Learning from Multi-phase Human Feedback of Mixed Quality
- AAAI/ACM AIESMaking Teams and Influencing Agents: Efficiently Coordinating Decision Trees for Interpretable Multi-Agent Reinforcement Learning
- FAccTMissing Pieces: How Framing Uncertainty Impacts Longitudinal Trust in AI Decision Aids — A Gig Driver Case Study
- The Winter Simulation Conference (WSC), DecemberOut of the Past: An AI-Enabled Pipeline for Traffic Simulation from Noisy, Multimodal Detector Data and Stakeholder Feedback
- ACL (T1)REALM: A Dataset of Real-World LLM Use Cases
- AAMAS (T1)Teamwork Makes the Defense Work: Defense Resource Allocation with Composable Targets
- International Conference on Human-Computer InteractionTraining Users Against Human and GPT-4 Generated Social Engineering Attacks
202413 papers
- AAAI (T1)Accelerate Multi-Agent Reinforcement Learning in Zero-Sum Games with Subgame Curriculum Learning
- NeurIPS (T1)Global Rewards in Restless Multi-Armed Bandits
- ICML (T1)Language Agents with Reinforcement Learning for Strategic Play in the Werewolf Game
- AAAI (T1)Learning Coalition Structures with Games
- CoNLLLeveraging a Cognitive Model to Measure Subjective Similarity of Human and GPT-4 Written Content
- AAMAS (T1)MABL: Bi-Level Latent-Variable World Model for Sample-Efficient Multi-Agent Reinforcement Learning
- AAMAS (T1)MESA: Cooperative Meta-Exploration in Multi-Agent Learning through Exploiting State-Action Space Structure
- NeurIPS (T1)Multi-Agent Imitation Learning: Value is Easy, Regret is Hard
- AAAI (T1)NewsPanda: Media Monitoring for Timely Conservation Action
- EMNLP (T1)PATIENT-Ψ: Using Large Language Models to Simulate Patients for Training Mental Health Professionals
- JCSSRELand: Risk Estimation of Landmines via Interpretable Invariant Risk Minimization
- PloS one 19 (11), e0301111Testing for Reviewer Anchoring in Peer Review: A Randomized Controlled Trial
20238 papers
- WWW (T1)A Dataset on Malicious Paper Bidding in Peer Review
- NeurIPS (T1)A One-Size-Fits-All Approach to Improving Randomness in Paper Assignment
- ACM Computing Surveys, doi:10.1145/3616864A Survey of Explainable Reinforcement Learning
- ICRA (T1)Explainable Action Advising for Multi-Agent Reinforcement Learning
- AAAI (T1)Function Approximation for Solving Stackelberg Equilibrium in Large Perfect Information Games
- GameSecMulti-defender Security Games with Schedules
- CHI (T1)Navigates Like Me: Understanding How People Evaluate Human-Like AI in Video Games
- WSCPurpose in the Machine: Do Traffic Simulators Produce Distributionally Equivalent Outcomes for Reinforcement Learning Applications?
Where alumni went10 listed
Apply to CMUFall 2027
CMU SCS Computer Science PhD
- Round
- Single round
- Opens
- GRE
- See program page
- English
- TOEFL / IELTS / Duolingo (F-1/J-1 non-native speakers)
- Fee
- USD 100 (USD 80 by early deadline)
Confirmed on the program page.
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