About Me

I am an Assistant Professor at AI Thrust, Information Hub of Hong Kong University of Science and Technology (Guangzhou). I am also an Affiliated Assistant Professor of the department of Computer Science & Engineering (Clear Water Bay Campus). I am a faculty member of the Deep Vision Lab. Previously, I was a Postdoctoral Associate at Computer Science & Artificial Intelligence Lab of Massachusetts Institute of Technology, where I had the privilege of working with Prof. Dina Katabi. I earned my Ph.D. degree from the Chinese University of Hong Kong, under the mentorship of Prof. Jiaya Jia. I am honored as Distinguished Young Scholars (Overseas). For more information about my research group, please visit EnVision-Research.

Interests
  • Computer Vision
  • Generative Models
  • AI+X
Prospective Students

My current research is increasingly focused on AI systems grounded in real-world deployment, real data, and long-term data flywheels. I am especially interested in problems that cannot be solved by simply following existing papers, benchmarks, or publication-driven templates.

I am looking for students who are willing to work on uncertain, long-horizon problems, care about real-world impact and durable technical value, and can stay focused without being driven solely by short-term metrics such as paper counts, internships, or resume building.

If you are primarily looking for a conventional publication-driven PhD path, frequent industry internships, or short-term career optimization, my group may not be the best fit. If you resonate with this direction, please read my full advising statement before reaching out.

Read the full advising statement

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Recent Publications
Harold H. Chen, Disen Lan, Wen-Jie Shu, Qingyang Liu, Zihan Wang, Sirui Chen, Wenkai Cheng, Kanghao Chen, Hongfei Zhang, Zixin Zhang, Rongjin Guo, Yu Cheng, Ying-Cong Chen. TiViBench: Benchmarking Think-in-Video Reasoning for Video Generative Models. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2026.
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Baijiong Lin, Weisen Jiang, Feiyang Ye, Yu Zhang, Pengguang Chen, Ying-Cong Chen, Shu Liu, Ivor W. Tsang, James T. Kwok. Dual-balancing for multi-task learning. Neural Networks, 2026.
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Haojian Huang, Chuanyu Qin, Yinchuan Li, Ying-Cong Chen. Find, Fix, Reason: Context Repair for Video Reasoning. Proceedings of the International Conference on Machine Learning (ICML), 2026.
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Litao Guo, Jinsong Zhou, Shuaibo Li, Man Chen, Xinli Xu, Zixin Zhang, Harold Haodong Chen, Ying-Cong Chen. ImpText: A Benchmark and Tool-Augmented Framework for Implicit Text Reasoning. Proceedings of the International Conference on Machine Learning (ICML), 2026.
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Zhen Yang, Guibao Shen, Minyang Li, Liang Hou, Mushui Liu, Luozhou Wang, Xin Tao, Ying-Cong Chen. RectifiedHR: Enable Efficient High-Resolution Synthesis via Energy Rectification. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Findings, 2026.
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