Fei Fang
Associate Professor
- 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
Her research interests lie in the area of artificial intelligence, focusing on the intersection of computational game theory and machine learning. Fang's work has been motivated by and applied to security, sustainability, and mobility domains, contributing to the theme of AI for Social Good.
Fang's 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
Media Appearances
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.
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."
Media
Social
Industry Expertise
Accomplishments
Outstanding Paper Award in Computational Sustainability Track
International Joint Conferences on Artificial Intelligence (IJCAI’15)
Innovative Application Award
Innovative Applications of Artificial Intelligence (IAAI’16)
Education
University of Southern California
Ph.D.
Computer Science
2016
Tsinghua University
B.Eng.
Electronic Engineering
2011
Links
Articles
Ai realtor: Towards grounded persuasive language generation for automated copywriting
Proceedings of the ACM Conference on AI and Agentic SystemsJibang Wu, Chenghao Yang, Yi Wu, Simon Mahns, Chaoqi Wang, Hao Zhu, Fei Fang, Haifeng Xu
2026-05-26
This paper develops an agentic framework that employs large language models (LLMs) for grounded persuasive language generation in automated copywriting, with real estate marketing as a focal application. Our method is designed to align the generated content with user preferences while highlighting useful factual attributes. This agent consists of three key modules: (1) Grounding Module, mimicking expert human behavior to predict marketable features; (2) Personalization Module, aligning content with user preferences; (3) Marketing Module, ensuring factual accuracy and the inclusion of localized features. We conduct systematic human-subject experiments in the domain of real estate marketing, with a focus group of potential house buyers. The results demonstrate that marketing descriptions generated by our approach are preferred over those written by human experts by a clear margin while maintaining the same level of factual accuracy. Our findings suggest a promising agentic approach to automate large-scale targeted copywriting while ensuring factuality of content generation.
Realm: A dataset of real-world llm use cases
Findings of the Association for Computational Linguistics: ACL 2025Jingwen Cheng, Kshitish Ghate, Wenyue Hua, William Yang Wang, Hong Shen, Fei Fang
2025-07-27
Large Language Models (LLMs), such as the GPT series, have driven significant industrial applications, leading to economic and societal transformations. However, a comprehensive understanding of their real-world applications remains limited. To address this, we introduce** REALM**, a dataset of over 94,000 LLM use cases collected from Reddit and news articles.** REALM** captures two key dimensions: the diverse applications of LLMs and the demographics of their users. It categorizes LLM applications and explores how users’ occupations relate to the types of applications they use. By integrating real-world data,** REALM** offers insights into LLM adoption across different domains, providing a foundation for future research on their evolving societal roles. An interactive dashboard ([https://realm-e7682. web. app/](https://realm-e7682. web. app/)) is provided for easy exploration of the dataset.
PATIENT-
Proceedings of the 2024 Conference on Empirical Methods in Natural Language ProcessingRuiyi Wang, Stephanie Milani, Jamie C Chiu, Jiayin Zhi, Shaun M Eack, Travis Labrum, Samuel M Murphy, Nev Jones, Kate V Hardy, Hong Shen, Fei Fang, Zhiyu Chen
2024-11-12
Mental illness remains one of the most critical public health issues. Despite its importance, many mental health professionals highlight a disconnect between their training and actual real-world patient practice. To help bridge this gap, we propose PATIENT-
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.
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.
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.