Long CHEN 陳隆
Contact Information
Research Interests
Biography
Prof. Long Chen is an Assistant Professor in the Department of Computer Science and Engineering (CSE), the Hong Kong University of Science and Technology (HKUST). Before joining HKUST, he was a postdoctoral research scientist in the Digital Video and Multimedia (DVMM) Lab at Columbia University, working with Prof. Shih-Fu Chang. He received his Ph.D. degree in Computer Science from Zhejiang University in 2020, under the supervision of Prof. Jun Xiao. He was also a visiting Ph.D. student in Nanyang Technological University (NTU) and National University of Singapore (NUS), under the supervision of Prof. Hanwang Zhang and Prof. Tat-Seng Chua, respectively.
His research mainly focuses on computer vision, multimedia, machine learning, and natural language processing. Specifically, he aims to build an efficient vision system that can understand complex visual scenes as humans. By "human-like", we mean that the vision systems should be equipped with several types of abilities, such as explainable, robust, and universal. He served as an Area Chair (AC) or a Senior Program Committee (SPC) at multiple international conferences including NeurIPS, CVPR, AAAI, IJCAI, and BMVC.
Research Interests
Biography
Prof. Long Chen is an Assistant Professor in the Department of Computer Science and Engineering (CSE), the Hong Kong University of Science and Technology (HKUST). Before joining HKUST, he was a postdoctoral research scientist in the Digital Video and Multimedia (DVMM) Lab at Columbia University, working with Prof. Shih-Fu Chang. He received his Ph.D. degree in Computer Science from Zhejiang University in 2020, under the supervision of Prof. Jun Xiao. He was also a visiting Ph.D. student in Nanyang Technological University (NTU) and National University of Singapore (NUS), under the supervision of Prof. Hanwang Zhang and Prof. Tat-Seng Chua, respectively.
His research mainly focuses on computer vision, multimedia, machine learning, and natural language processing. Specifically, he aims to build an efficient vision system that can understand complex visual scenes as humans. By "human-like", we mean that the vision systems should be equipped with several types of abilities, such as explainable, robust, and universal. He served as an Area Chair (AC) or a Senior Program Committee (SPC) at multiple international conferences including NeurIPS, CVPR, AAAI, IJCAI, and BMVC.