Fei Fang
Assistant Professor | Carnegie Mellon University
Pittsburgh, PA, UNITED STATES
Fei Fang's research lies in the field of artificial intelligence and multi-agent systems, focusing on computational game theory.
Biography
Fei Fang is an Assistant Professor at the Software and Societal Systems Department 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, advised by Prof. David Parkes and Prof. Barbara Grosz. She received her Ph.D. from the Department of Computer Science at the University of Southern California in June 2016, advised by Prof. Milind Tambe. She received her bachelor degree from the Department of Electronic Engineering, Tsinghua University in July 2011.
Her research lies in the field of artificial intelligence and multi-agent systems, focusing on computational game theory with applications to security and sustainability domains. Her dissertation is selected as the runner-up for IFAAMAS-16 Victor Lesser Distinguished Dissertation Award. Her work has won the Innovative Application Award at Innovative Applications of Artificial Intelligence (IAAI’16), the Outstanding Paper Award in Computational Sustainability Track at the International Joint Conferences on Artificial Intelligence (IJCAI’15). Her work on “Protecting Moving Targets with Mobile Resources” has been deployed by the US Coast Guard for protecting the Staten Island Ferry in New York City since April 2013. Her work on designing patrol strategies to combat illegal poaching has lead to the deployment of PAWS application in a conservation area in Southeast Asia for protecting tigers.
Areas of Expertise (5)
Multi-Agent Systems
Optimization
Artificial Intelligence
Game Theory
Computer Science
Media Appearances (2)
Trash talk hurts, even when it comes from a robot
EurekAlert! online
2019-11-19
"This is one of the first studies of human-robot interaction in an environment where they are not cooperating," said co-author Fei Fang, an assistant professor in the Institute for Software Research. It has enormous implications for a world where the number of robots and internet of things (IoT) devices with artificial intelligence capabilities is expected to grow exponentially. "We can expect home assistants to be cooperative," she said, "but in situations such as online shopping, they may not have the same goals as we do."
How AI could help solve some of society’s toughest problems
MIT Technology Review online
2018-09-12
Fei Fang has saved lives. But she isn’t a lifeguard, medical doctor, or superhero. She’s an assistant professor at Carnegie Mellon University, specializing in artificial intelligence for societal challenges.
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Industry Expertise (3)
Research
Education/Learning
Computer Software
Accomplishments (2)
Innovative Application Award (professional)
Innovative Applications of Artificial Intelligence (IAAI’16)
Outstanding Paper Award in Computational Sustainability Track (professional)
International Joint Conferences on Artificial Intelligence (IJCAI’15)
Education (2)
Tsinghua University: B.Eng., Electronic Engineering 2011
University of Southern California: Ph.D., Computer Science 2016
Links (5)
Articles (5)
A Dataset on Malicious Paper Bidding in Peer Review
WWW '23: Proceedings of the ACM Web Conference 20232023 In conference peer review, reviewers are often asked to provide “bids” on each submitted paper that express their interest in reviewing that paper. A paper assignment algorithm then uses these bids (along with other data) to compute a high-quality assignment of reviewers to papers. However, this process has been exploited by malicious reviewers who strategically bid in order to unethically manipulate the paper assignment, crucially undermining the peer review process.
Inverse Game Theory for Stackelberg Games: the Blessing of Bounded Rationality
Advances in Neural Information Processing Systems 35 (NeurIPS 2022)2022 Optimizing strategic decisions (a.k.a. computing equilibrium) is key to the success of many non-cooperative multi-agent applications. However, in many real-world situations, we may face the exact opposite of this game-theoretic problem --- instead of prescribing equilibrium of a given game, we may directly observe the agents' equilibrium behaviors but want to infer the underlying parameters of an unknown game.
Curriculum Reinforcement Learning using Optimal Transport via Gradual Domain Adaptation
Advances in Neural Information Processing Systems 35 (NeurIPS 2022)2022 Curriculum Reinforcement Learning (CRL) aims to create a sequence of tasks, starting from easy ones and gradually learning towards difficult tasks. In this work, we focus on the idea of framing CRL as interpolations between a source (auxiliary) and a target task distribution. Although existing studies have shown the great potential of this idea, it remains unclear how to formally quantify and generate the movement between task distributions. Inspired by the insights from gradual domain adaptation in semi-supervised learning, we create a natural curriculum by breaking down the potentially large task distributional shift in CRL into smaller shifts.
PerfectDou: Dominating DouDizhu with Perfect Information Distillation
Advances in Neural Information Processing Systems 35 (NeurIPS 2022)2022 As a challenging multi-player card game, DouDizhu has recently drawn much attention for analyzing competition and collaboration in imperfect-information games. In this paper, we propose PerfectDou, a state-of-the-art Doudizhu AI system that summits the game, in an actor-critic framework with a proposed technique named perfect information distillation.In detail, we adopt a perfect-training-imperfection-execution framework that allows the agents to utilize the global information to guide the training of the policies as if it is a perfect information game and the trained policies can be used to play the imperfect information game during the actual gameplay.
Tradeoffs in Preventing Manipulation in Paper Bidding for Reviewer Assignment
arXiv:2207.113152022 Many conferences rely on paper bidding as a key component of their reviewer assignment procedure. These bids are then taken into account when assigning reviewers to help ensure that each reviewer is assigned to suitable papers. However, despite the benefits of using bids, reliance on paper bidding can allow malicious reviewers to manipulate the paper assignment for unethical purposes (e.g., getting assigned to a friend's paper). Several different approaches to preventing this manipulation have been proposed and deployed. In this paper, we enumerate certain desirable properties that algorithms for addressing bid manipulation should satisfy. We then offer a high-level analysis of various approaches along with directions for future investigation.
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