About Me

I am an assistant professor of computer science at Brown University, where I direct the PALM🌴 research lab, studying computer vision, machine learning, and artificial intelligence. I work part-time as a staff research scientist at Google DeepMind.

Our research focuses on multimodal concept learning and reasoning, as well as controllable, principled, and scalable image and video generation, and their applications in robotic planning and control. A long-standing research tradition of the lab is video understanding.

Previously, I received my Ph.D. from the University of Southern California in 2016, advised by Prof. Ram Nevatia. I completed my bachelor degree in Computer Science at Tsinghua University in 2011.

I have received Brown University's Richard B. Salomon Faculty Research Award and Samsung's Global Research Outreach Award for multimodal concept learning from videos. My research on behavior prediction appeared in the CVPR 2019 best paper finalist. Our lab's research has been supported by Adobe, Honda, Meta, NASA, and Samsung.

My office hours are 3:30 to 5:00 pm ET on Tuesdays at CIT 379.

Teaching

• CSCI 2470, Deep Learning, Fall.
• CSCI 2952-N, Advanced Topics in Deep Learning, Spring.
• Short course on Multimodal Transformers, ICASSP 2022 and AAAI 2023.
• CSCI 2950-K, Special Topics in Computational Linguistics (co-taught with Eugene Charniak), Fall 2020.

Group

PhD students

• Apoorv Khandelwal (Presidential Fellow, co-advised with Ellie Pavlick)
• Calvin Luo
• Megan Wei
• Nate Gillman (Department of Mathematics)
• Shijie Wang
• Tian Yun (Co-advised with Ellie Pavlick)
• Yuan Zang
• Zilai Zeng
• Zitian Tang

Alumni

• Kevin Zhao (master of 2024 at Brown, now researcher at TikTok)
• Zilai Zeng (master of 2024 at Brown, now PhD student at Brown CS)
• Yunhao Luo (master of 2024 at Brown, now research assistant at Georgia Tech)
• Mandy He (class of 2024 at Brown, now software engineer at Duolingo)
• Minh Quan Do (master of 2024 at Brown, now co-founder at Tan Kim Nhat Trading)
• David Heffren (class of 2024 at Brown, now PhD student at JHU Applied Math)
• John Ryan Byers (class of 2024 at Brown, now master student at Cornell Tech)
• Ce Zhang (master of 2023 at Brown, now PhD student at UNC CS)
• Changcheng Fu (master of 2023 at Brown, now PhD student at USC CS)
• Kunal Handa (class of 2023 at Brown, now member of technical staff at Anthropic)
• Jessica Li (class of 2023 at Brown, now software engineer at Headway)
• Tian Yun (master of 2022 at Brown, now PhD student at Brown CS)
• Usha Bhalla (class of 2022 at Brown, now PhD student at Harvard CS)
• Emily Byun (class of 2021 at Brown, now PhD student at CMU MLD)

Mentorship

• ExploreCSR
• NSF Summer REU

Services

• Workshop Chair, CVPR 2025.
• Action Editor, TMLR.
• Area Chair, ICLR 2025.
• Area Chair, CVPR 2020 to 2024.
• Area Chair, ICCV 2023 and 2025.
• Area Chair, ECCV 2022 and 2024.
• Area Chair, ACL 2023.
• Area Chair, NeurIPS 2023 to 2025.
• Senior PC, AAAI 2021 and 2022.
• Area Chair, WACV 2017 and 2018.

Selected Projects

	Fourier Head: Helping Large Language Models Learn Complex Probability Distributions Nate Gillman*, Daksh Aggarwal*, Michael Freeman, Saurabh Singh, and Chen Sun ICLR 2025 arXiv / Project / Code
	Solving New Tasks by Adapting Internet Video Knowledge Calvin Luo*, Zilai Zeng*, Yilun Du, and Chen Sun ICLR 2025 arXiv / Project
	Motion Prompting: Controlling Video Generation with Motion Trajectories Daniel Geng, Charles Herrmann et al. CVPR 2025 arXiv / Project
	Pre-trained Vision-Language Models Learn Discoverable Visual Concepts Yuan Zang, Tian Yun, Hao Tan, Trung Bui, and Chen Sun Transactions on Machine Learning Research (TMLR) arXiv / Project / Code
	Text-Aware Diffusion for Policy Learning Calvin Luo, Mandy He*, Zilai Zeng*, and Chen Sun NeurIPS 2024 (Also appeared at NeurIPS 2023 workshop on Diffusion Models) arXiv / Poster / Project / Code
	Self-Correcting Self-Consuming Loops for Generative Model Training Nate Gillman, Michael Freeman, Daksh Aggarwal, Chia-Hong Hsu, Calvin Luo, Yonglong Tian, and Chen Sun ICML 2024 arXiv / Poster / Project / Code
	Spacewalk-18: A Benchmark for Multimodal and Long-form Procedural Video Understanding in Novel Domains Rohan Myer Krishnan*, Zitian Tang*, Zhiqiu Yu, and Chen Sun preprint arXiv / Project / Dataset
	Vamos: Versatile Action Models for Video Understanding Shijie Wang, Qi Zhao, Minh Quan Do, Nakul Agarwal, Kwonjoon Lee, and Chen Sun ECCV 2024 arXiv / Project / Code
	AntGPT: Can Large Language Models Help Long-term Action Anticipation from Videos? Qi Zhao*, Shijie Wang*, Ce Zhang, Changcheng Fu, Minh Quan Do, Nakul Agarwal, Kwonjoon Lee, and Chen Sun ICLR 2024 arXiv / Project
	Goal-Conditioned Predictive Coding for Offline Reinforcement Learning Zilai Zeng, Ce Zhang, Shijie Wang, and Chen Sun NeurIPS 2023 arXiv / Project / Code
	Learning Temporal Dynamics from Cycles in Narrated Video Dave Epstein, Jiajun Wu, Cordelia Schmid, and Chen Sun ICCV 2021 arXiv / Research Blog / Project
	What Makes for Good Views for Contrastive Learning? Yonglong Tian, Chen Sun, Ben Poole, Dilip Krishnan, Cordelia Schmid, and Phillip Isola NeurIPS 2020 arXiv / Research Blog / Project / Code
	VectorNet: Encoding HD Maps and Agent Dynamics from Vectorized Representation Jiyang Gao*, Chen Sun*, Hang Zhao, Yi Shen, Dragomir Anguelov, Congcong Li, and Cordelia Schmid CVPR 2020 arXiv / Waymo Blog / VentureBeat
	VideoBERT: A Joint Model for Video and Language Representation Learning Chen Sun, Austin Myers, Carl Vondrick, Kevin Murphy, and Cordelia Schmid ICCV 2019 arXiv / Research Blog / VentureBeat