Collaborative Disagreement Resolution for Scalable Oversight
Yuyang Jiang*‡, Chacha Chen*, Teng Wu, Liwen Sun, Han Liu, Shi Feng and Chenhao Tan.
Disagreement Resolution improves scalable oversight by shifting the interaction mechanism from adversarial debate to collaborative truth-seeking.
ICML 2026.

GPT-4V Cannot Generate Radiology Report Yet
Yuyang Jiang*‡, Chacha Chen*, Dang Nguyen, Benjamin M. Mervak, Chenhao Tan.
We systematically evaluate GPT-4V's ability to generate radiology reports and identify key limitations in clinical reasoning and structured reporting.
ML4H 2024, NAACL 2025. [Paper]

The Use of Generative Search Engines for Knowledge Work and Complex Tasks
Siddharth Suri, Scott Counts, Leijie Wang, Chacha Chen, Mengting Wan, Tara Safavi, Jennifer Neville, Chirag Shah, Ryen W. White, Reid Andersen, Georg Buscher, Sathish Manivannan, Nagu Rangan, Longqi Yang.
An empirical analysis of large-scale user interactions with Bing Chat, a GPT-4 backed generative search engine.
[Preprint]

Tutorial: Uncertainty Quantification and Confidence Calibration in Large Language Models: A Survey
Xiaoou Liu, Tiejin Chen, Longchao Da, Chacha Chen, Zhen Lin, Hua Wei.
KDD 2025. [Paper] [Tutorial Website]

Can Domain Experts Rely on AI Appropriately? A Case Study on AI-Assisted Prostate Cancer MRI Diagnosis
Chacha Chen, Han Liu, Ziyang Guo, Jiamin Yang, Benjamin M. Mervak, Bora Kalaycioglu, Grace Lee, Emre Cakmakli, Matteo Bonatti, Sridhar Pudu, Osman Kahraman, Gul Gizem Pamuk, Aytekin Oto, Aritrick Chatterjee, Jessica Hullman, Chenhao Tan.
We find that radiologists under-rely on AI assistance in prostate cancer diagnosis, missing opportunities for improved accuracy even when AI predictions are correct.
FAccT 2025 [Paper]

Machine Explanations and Human Understanding
Chacha Chen*, Shi Feng*, Amit Sharma, Chenhao Tan.
FAccT 2023, TMLR 2023, and Best Paper at HMCaT @ ICML 2022
[Paper] [Slides] [Video]
[Machine Explanations Human Studies Literature Survey]

Towards a Science of Human-AI Decision Making: A Survey of Empirical Studies
Vivian Lai, Chacha Chen, Q. Vera Liao, Alison Smith-Renner, Chenhao Tan.
A comprehensive survey of 100+ papers on human-AI decision making, identifying key factors and proposing a framework for future research.
FAccT 2023. [Paper] [Slides] [Survey Website]

Learning Human-Compatible Representations for Case-Based Decision Support.
Han Liu, Yizhou Tian, Chacha Chen, Shi Feng, Yuxin Chen, Chenhao Tan.
We develop a method to learn case representations that align with human similarity judgments for more effective AI-assisted decision support.
ICLR 2023. [Paper]

UNITE: Uncertainty-based Health Risk Prediction Leveraging Multi-sourced Data
Chacha Chen, Junjie Liang, Fenglong Ma, Lucas Glass, Jimeng Sun, Cao Xiao.
A framework for health risk prediction that quantifies uncertainty when integrating multiple data sources like claims and lab results.
WWW 2021. [Paper] [Slides]

ImIn-GAIL: Learning to Simulate with Imitation-Interpolation on Sparse Trajectory Data
Hua Wei, Chacha Chen, Chang Liu, Guanjie Zheng, Zhenhui Li.
A framework for learning realistic driving behavior from sparse trajectory data by combining interpolation with imitation learning.
ECML-PKDD 2020 and Best Applied Data Science Paper Award. [Paper]

Toward a Thousand Lights: Decentralized Deep Reinforcement Learning for Large-Scale Traffic Signal Control
Chacha Chen, Hua Wei, Nan Xu, Guanjie Zheng, Ming Yang, Yuanhao Xiong, Kai Xu, Zhenhui Li.
A scalable decentralized reinforcement learning approach for coordinating traffic signals across large urban networks.
AAAI 2020. [Paper]