Fei Fang

Assistant Professor Carnegie Mellon University

  • Pittsburgh PA

Fei Fang's research lies in the field of artificial intelligence and multi-agent systems, focusing on computational game theory.

Contact

Carnegie Mellon University

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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

Multi-Agent Systems
Optimization
Artificial Intelligence
Game Theory
Computer Science

Media Appearances

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."

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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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Social

Industry Expertise

Research
Education/Learning
Computer Software

Accomplishments

Innovative Application Award

Innovative Applications of Artificial Intelligence (IAAI’16)

Outstanding Paper Award in Computational Sustainability Track

International Joint Conferences on Artificial Intelligence (IJCAI’15)

Education

Tsinghua University

B.Eng.

Electronic Engineering

2011

University of Southern California

Ph.D.

Computer Science

2016

Articles

A Dataset on Malicious Paper Bidding in Peer Review

WWW '23: Proceedings of the ACM Web Conference 2023

2023

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.

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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.

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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.

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