I am a Ph.D. student in Statistics at Northwestern University. My research develops evaluation and modeling systems for multimodal large language models, sparse video understanding, and AI for science. I am especially interested in turning model failures into measurable diagnostics and interventions, including video-language reasoning, physical plausibility for video generation, benchmark construction, tabular learning, sparse Hopfield memory, and reinforcement learning for scientific control.

Before starting my Ph.D., I received an M.S. in Computer Science from Northwestern University and a B.S. in Data Analytics from The Ohio State University. I am a research assistant at Northwestern and a research collaborator with Dolby Laboratories.

You can also find my work on Google Scholar, my Northwestern Statistics and Data Science profile, and the CV page.

Selected Publications

Towards Sparse Video Understanding and Reasoning.
Chenwei Xu, Zhen Ye, Shang Wu, Weijian Li, Zihan Wang, Zhuofan Xia, Lie Lu, Pranav Maneriker, Fan Du, Manling Li, Han Liu
CVPR 2026 Poster [Link]

AdvEvo-MARL: Shaping Internalized Safety through Adversarial Co-Evolution in Multi-Agent Reinforcement Learning.
Zhenyu Pan, Yiting Zhang, Zhuo Liu, Yolo Y. Tang, Zeliang Zhang, Haozheng Luo, Chenwei Xu, Yuwei Han, Jianshu Zhang, Dennis Wu, Hong-Yu Chen, Haoran Lu, Haoyang Fang, Manling Li, Chenliang Xu, Philip S. Yu, Han Liu
ICML 2026 Poster

Let’s Try Again: Eliciting Multi-Turn Reasoning in Language Models via Simplistic Feedback.
Licheng Liu, Zihan Wang, Linjie Li, Chenwei Xu, Yiping Lu, Han Liu, Avirup Sil, Manling Li
ICML AI for Math Workshop 2025

BiSHop: Bi-Directional Cellular Learning for Tabular Data with Generalized Sparse Modern Hopfield Model.
Chenwei Xu*, Yu-Chao Huang*, Jerry Yao-Chieh Hu*, Weijian Li, Ammar Gilani, Hsi-Sheng Goan, Han Liu
ICML 2024 [Link]

* Equal contribution.