Contact Info

Chenhao Tan is an assistant professor of computer science at the University of Chicago. He obtained his PhD degree in the Department of Computer Science at Cornell University and bachelor’s degrees in computer science and in economics from Tsinghua University. Prior to joining UChicago, he spent a year at University of Washington as a postdoc and three years at CU Boulder as an assistant professor. His research interests include human-centered machine learning, natural language processing, and computational social science. He has published papers primarily at ACL and WWW, and also at KDD, WSDM, ICWSM, etc. His work has been covered by many news media outlets, such as the New York Times and the Washington Post. He also won an NSF CAREER award, an NSF CRII award, a Salesforce research award, an Amazon research award, a Facebook fellowship, and a Yahoo! Key Scientific Challenges award.

Research

Focus Areas: Computational Social Science, Human-centered AI, Natural Language Processing

My research brings together social sciences and machine learning to develop the best AI for humans. Specifically, my work aims to enable effective human-AI interaction by

Understanding human decision making through language. We analyze large amounts of textual data to unfold the connection between language and decisions in two directions. In one direction, we leverage natural experiments to understand how language shapes human decisions (e.g., what makes effective persuasion and language of bargaining). In the other direction, we examine explanations of human decisions to identify their biases.

Generating human-centered explanations. Our work shows that current methods of generating explanations for AI predictions fail to improve human-AI decision making. We develop a novel theoretical framework to show that the missing link is to model human interpretation of AI explanations. We thus build algorithms to align AI explanations with human intuitions and demonstrate substantial improvements in human performance.

Developing novel paradigms of human-AI interaction. We explore additional possibilities that humans and AI can complement each other in three directions: 1) appropriate and effective delegation to AI; 2) decision-focused summarization, a novel formulation of the classic NLP task to identify the most relevant information to support decision making; and 3) few-shot learning from human explanations so that humans can effectively improve large language models (LLMs).

Research

AI & Machine Learning

Foundations and applications of computer algorithms making data-centric models, predictions, and decisions

Human Computer Interaction

Exploring the interfaces between people and technologies

Programming Languages

Devising new and better ways of programming the computers of today and tomorrow

Labs & Groups

Chicago Human + AI (CHAI) Lab

Chenhao Tan
Developing synergy between humans and artificial intelligence through a better understanding of human behavior and human interaction with AI.

News & Events

Computer Science Displays Catch Attention at MSI’s Annual Robot Block Party

Apr 07, 2023

Assistant Professor Chenhao Tan Receives Sloan Research Fellowship

Feb 15, 2023

UChicago AI Summit Examines Promise and Concerns for Science and Society

Nov 01, 2022

UChicago CS Faculty Receive Industry Grants From J.P. Morgan, Google

Jul 19, 2022

Faculty Bill Fefferman and Chenhao Tan Receive Google Research Scholar Awards

Jun 21, 2022

UChicago CS Labs Join Museum of Science & Industry For Robot Block Party

Apr 20, 2022

New Data & Democracy Research Initiative Launched at University of Chicago

Mar 09, 2022

DSI Discovery Challenge Awardees Train Data Science on Medicine, Clean Water, and Education

Apr 09, 2021

Six UChicago CS Faculty Receive CAREER Awards

Mar 08, 2021

New Asst. Prof. Chenhao Tan Explores The Future of Human-Centered AI

Feb 16, 2021

CDAC Rising Stars in Data Science Workshop

Jan 11, 2021

Four New Faculty Join UChicago Computer Science for 2020-21

Jul 27, 2020
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