Game Theory and Machine Learning for Addressing Societal Challenges: From Theory to Real-World Impact

11/21/24 | 4:15pm | E51-149

Fei Fang

Associate Professor of Computer Science
Carnegie Mellon University

Abstract: Societal challenges involve complex decision-making by multiple self-interested agents. In our research, we delve into the development of game theory and machine learning-based methodologies and tools to tackle these challenges, with a strong focus on contributing to the social good. In this talk, I will introduce our work that has led to successful applications in environmental conservation and food rescue. Moreover, I will cover our foundational research in inverse game theory, scalable game solving, and interpretable multi-agent reinforcement learning. These advancements are motivated by the real-world problems we have been working on and enable us to tackle more complex decision-making scenarios in the future.

Bio: Fei Fang is an Associate Professor at the Software and Societal Systems Department in the School of Computer Science at Carnegie Mellon University. Before joining CMU, she was a Postdoctoral Fellow at the Center for Research on Computation and Society (CRCS) at Harvard University, hosted by David Parkes and Barbara Grosz. She received her Ph.D. from the Department of Computer Science at the University of Southern California advised by Milind Tambe. Her research lies in the field of artificial intelligence and multi-agent systems, focusing on integrating machine learning with game theory. Her work has been motivated by and applied to security, sustainability, and mobility domains, contributing to the theme of AI for Social Good. She is the recipient of the Allen Newell Award for Research Excellence 2023, 2022 Sloan Research Fellowship, and IJCAI-21 Computers and Thought Award. She was named to IEEE Intelligent Systems’ “AI’s 10 to Watch” list for 2020. She received an NSF CAREER Award in 2021.