Biosketch

Dr. Long CHEN (Chinese: 陈隆) is an assistant professor at the department of CSE, Hong Kong University of Science and Technology (HKUST). He is leading a computer vision and machine learning research group: LONG Group. Before joining HKUST, he was a postdoctoral research scientist at the DVMM Lab, Columbia University, working with Prof. Shih-Fu Chang. He obtained his Ph.D. degree in Computer Science from the DCD Lab, Zhejiang University in 2020. His PhD advisor is Prof. Jun Xiao. During Ph.D. study period, he was also a visiting student at the MReal Lab, Nanyang Technological University (NTU), working with Prof. Hanwang Zhang, and the NExT Center, National University of Singapore (NUS), working with Prof. Tat-Seng Chua. He obtained his B.Eng. degree from Dalian University of Technology in 2015. He was a senior research scientist at Tencent AI Lab.

General Research Interests: Specifically, he aims to build an efficient multimodal AI system that can realize "human-like" multimodal understanding and generation. By “human-like”, we mean that the vision systems should be equipped with three types of abilities:
1) Explainable: The model should rely on (right) explicit evidences when making decisions, i.e., right for the right reasons.
2) Robust: The model should be robust to some situations with only low-quality training data (e.g., training samples are biased, noisy, or limited).
3) Universal: The model design is relatively universal, i.e., it is expected to be effective for various tasks.
Meanwhile, with the rapid development of foundation models, such as large language model (LLMs), vision-language models (VLMs), and vision generation models (e.g, diffusion models), our group, we are also very interested in several releveant cutting-edge directions:
4) Building more explainable, robust, and universal vision models with the help of pretrained models (LLMs, diffusion models).
5) Designing more efficient and stronger multimodal LLMs.
6) The inherent weaknesses in existing LLMs and diffusion models.