About Me

I am an assistant professor of computer science at Brown University, where I direct the PALM🌴 research lab on computer vision, machine learning, and AI. I am also a part-time staff research scientist at Google DeepMind. Previously, I received my PhD from the University of Southern California in 2016 and my bachelor's degree in Computer Science at Tsinghua University in 2011. (Short bio)

At PALM🌴, we focus on learning generalizable temporal dynamics from unlabeled videos, whether as multimodal concepts, human behaviors, or raw pixels. We are also exploring applications of these video-centric world models in robotics.

I have received an NSF CAREER Award, a Richard B. Salomon Faculty Research Award, and a Samsung Global Research Outreach Award. My work on behavior prediction was a CVPR 2019 best paper finalist. Our research has been supported by Adobe, Honda, Meta, NASA, NSF, NVIDIA, and Samsung over the years. We are a member of the NSF AI Research Institute on Interaction for AI Assistants (ARIA).

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)
• Zilai Zeng
• Zitian Tang

PhD alumni

• Calvin Luo (PhD '26)
  • Thesis: Generalizable Decision-Making via Large-Scale Visual Generative Models
  • Research Mobility Fellowship, Joukowsky Outstanding Dissertation Prize Finalist
• Nate Gillman (PhD '26, Mathematics & Computer Science)
  • Thesis: Controlling Deep Generative Models via Physical and Mathematical Priors
  • Next position: Research Scientist at Google Research
• Shijie Wang (PhD '26)
  • Thesis: From Text to Pixels: Multimodal Video Understanding with Language Pre-training
  • Next position: Research Scientist at NVIDIA Cosmos Lab
• Tian Yun (PhD '26, co-advised with Ellie Pavlick)
  • Thesis: Internal Representations in Vision-Language Models
  • Next position: Research Scientist at Meta MSL

Undergraduate and MS alumni

• Kaleb Newman (Brown '25, CRA Honorable Mention — PhD at Princeton CS)
• Michael Freeman (Brown '20 — PhD at Cornell CS)
• Rohan Krishnan (Brown '25 — engineer at Klaviyo)
• Chia-Hong Hsu (Brown MS '25 — PhD at UBC)
• Kevin Zhao (Brown MS '24 — researcher at ByteDance Seed)
• Zilai Zeng (Brown MS '24 — PhD at Brown CS)
• Yunhao Luo (Brown MS '24 — PhD at UMich CS)
• Mandy He (Brown '24 — engineer at Duolingo)
• Minh Quan Do (Brown MS '24 — co-founder at Tan Kim Nhat Trading)
• David Heffren (Brown '24 — PhD at JHU Applied Math)
• John Ryan Byers (Brown '24 — master's at Cornell Tech)
• Ce Zhang (Brown MS '23 — PhD at UNC CS)
• Changcheng Fu (Brown MS '23 — PhD at USC CS)
• Kunal Handa (Brown '23 — member of technical staff at Anthropic)
• Jessica Li (Brown '23 — engineer at Headway)
• Usha Bhalla (Brown '22 — PhD at Harvard CS)
• Emily Byun (Brown '21 — PhD at CMU MLD)

Mentorship

• ExploreCSR
• NSF Summer REU

Services

• Workshop Chair, CVPR 2025.
• Action Editor, TMLR.
• Area Chair, ICLR 2025 and 2026.
• Area Chair, CVPR 2020 to 2026.
• Area Chair, ICCV 2023 and 2025.
• Area Chair, ECCV 2022 to 2026.
• Area Chair, NeurIPS 2023 to 2025.
• Area Chair, ACL 2023 and 2025.
• Senior PC, AAAI 2021 and 2022.
• Area Chair, WACV 2017 and 2018.

Selected Projects

	Goal Force: Teaching Video Models To Accomplish Physics-Conditioned Goals Nate Gillman, Yinghua Zhou, Zitian Tang, Evan Luo, Arjan Chakravarthy, Daksh Aggarwal, Michael Freeman, Charles Herrmann, and Chen Sun CVPR 2026 arXiv / Project / Code
	Self-Improving Loops for Visual Robotic Planning Calvin Luo*, Zilai Zeng*, Mingxi Jia, Yilun Du, and Chen Sun ICLR 2026 arXiv / Project / Code
	Spacewalk-18: A Benchmark for Multimodal and Long-form Procedural Video Understanding in Novel Domains Zitian Tang*, Rohan Myer Krishnan*, Zhiqiu Yu, and Chen Sun WACV 2026 (Oral, Award Finalist) arXiv / Project / Dataset
	Force Prompting: Video Generation Models Can Learn and Generalize Physics-based Control Signals Nate Gillman, Charles Herrmann*, Michael Freeman, Daksh Aggarwal, Evan Luo, Deqing Sun, and Chen Sun* NeurIPS 2025 arXiv / Project / Code
	How Can Objects Help Video-Language Understanding? Zitian Tang, Shijie Wang, Junho Cho, Jaewook Yoo, and Chen Sun ICCV 2025 arXiv / Project / Code
	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 / Code
	Motion Prompting: Controlling Video Generation with Motion Trajectories Daniel Geng, Charles Herrmann et al. CVPR 2025 (Oral) arXiv / Project
	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
	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 / Code
	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