Chaoqi Chen (陈超奇)

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Assistant Professor (Hundred Talents Program)
College of Computer Science and Software Engineering
Shenzhen University
Shenzhen 518060, Guangdong, P.R.China

Office: Room L6-711-1@VCC, Zhizhen Building, Canghai Campus, SZU

I finished my PhD at the University of Hong Kong, advised by Prof. Yizhou Yu (ACM/IEEE Fellow), and my master's degree at Xiamen University, advised by Prof. Yue Huang and Prof. Xinghao Ding. Before that, I received my bachelor's degree at Xiamen University.

cqchen1994[at]gmail.com | Google Scholar | GitHub

 

About

Dr. Chaoqi Chen is an Assistant Professor (“Hundred Talents Program”) with the College of Computer Science and Software Engineering at Shenzhen University.

We are currently focused on advancing open-world visual language foundation models, emphasizing adaptability, generalization, and robustness. Our research spans key areas such as cross-domain inference, causality-inspired learning, test-time adaptation, and unsupervised domain adaptation. Our goal is to push the boundaries of open-world, open-domain, and open-vocabulary computer vision, while striving to create trustworthy artificial intelligence systems. With over 20 publications in prestigious journals and conferences like IEEE T-PAMI, CVPR, ICCV, NeurIPS, and AAAI, we are committed to advancing the field. We are always looking for ambitious and motivated individuals—whether at the bachelor, master, doctoral, or postdoctoral level—to join us in researching and developing cutting-edge AI technologies.

 

Research Interests

His research interests lie at the intersection of Computer Vision , Machine Learning , Trustworthy AI and Data-centric AI , aiming to develop algorithms and fundamental understandings to enable efficient, adaptive, and safe learning in the open and ever-changing world. Currently, I am working on (i) deep learning under distribution shifts , (ii) open-world visual understanding , and (iii) geometric machine learning for vision tasks.

 

To Prospective Students

  • Students with a background in Machine Learning and Computer Vision.
  • If you are SZU students, I will respond to all your emails (100% guaranteed). Alternatively, you can come to my office (Room L6-711-1@ VCC Lab) for in-person discussions.
  • For PhD applicants, please contact me at least half a year (one semester) prior to your application. It is recommended that you read these documents (CV Writing Tips, SZU PhD Admission) before sending emails.

 

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