- Associate Professor
- Department of Information Engineering
- The Chinese University of Hong Kong
I am an Associate Professor at the department of Information Engineering, the Chinese University of Hong Kong, and the Director of CUHK-SenseTime Joint Laboratory. I received the B.Eng. degree from the University of Science and Technology of China (USTC) in 2004, the M. Phil. degree from the Chinese University of Hong Kong (CUHK) in 2006, and the Ph.D. degree from Massachusetts Institute of Technology (MIT) in 2012. Prior to joining CUHK, I served as a Research Assistant Professor at Toyota Technological Institute at Chicago, from 2012 to 2014.
My research interest covers computer vision and machine learning. In recent years, I primarily focused on deep learning and its applications on high-level visual understanding, cross-domain modeling, and big data analytics. I have published over 120 papers on top conferences and journals, e.g. CVPR, ICCV, ECCV, ICML, NeurIPS, and T-PAMI. My work on a new construction of Bayesian nonparametric models has won the best student paper award in NIPS, the most prestigious conference on machine learning, in year 2010. I also received the outstanding reviewer awards in ICCV 2009 and ICCV 2011. I supervised or co-supervised CUHK teams in international competitions and won multiple awards in ImageNet 2016, ActivityNet 2016 & 2017, COCO 2018 & 2019.
I serve on the editorial board of the International Journel of Computer Vision (IJCV). I also serve as an area chair for multiple conferences, including ECCV 2018, ACM Multimedia 2018, BMVC 2018, CVPR 2019, BMVC 2019, AAAI 2020, and CVPR 2021.
Besides research, I am also actively working on extending AI education to high schools. Particularly, I served as the Executive Editor and led the editing team of "Fundamentals of Artificial Intelligence", the first AI textbook for high school education.
- Deep learning technologies in large-scale applications
- High level visual understanding in connection with linguistic analysis
- Efficient modeling and analytics of videos and movies
- The use of deep networks in general probabilistic inference