LI, Zhen

Assistant Professor

Presidential Young Fellow
Education Background

PhD (University of Hong Kong)

Research Field
Deep learning, computational biological and computer vision
Academic Area
Computer Engineering, Artificial Intelligence and Robotics
Personal Website
Email
lizhen@cuhk.edu.cn
Biography

Dr. Li Zhen is currently an assistant professor at the School of Science and Engineering (SSE) of The Chinese University of Hong Kong, Shenzhen/Future Intelligent Network Research Institute (FNii) of The Chinese University of Hong Kong, Shenzhen. He is also a research scientist at the Shenzhen Institute of Big Data (SRIBD) and a special researcher at the South China Hospital Affiliated to Shenzhen University. Dr. Li Zhen was selected for the 2021-2023 Seventh China Association for Science and Technology Young Talent Support Project. Dr. Zhen Li received his PhD in Computer Science from the University of Hong Kong (2014-2018), a MS in Communication and Information Systems from Sun Yat-Sen University (2011-2014), and a BS in Automation from Sun Yat-Sen University (2007-2011). He was also a visiting scholar at the University of Chicago in 2018 and a visiting student at the Toyota Technical Institute (TTIC) in Chicago in 2016. His research interests include interdisciplinary research in artificial intelligence, 3D vision, computer vision, and deep learning-assisted medical big data analysis. He has published more than 30 papers in top conferences and journals, such as top journals Cell Systems and Nature Communications, IEEE TNNLS, IEEE TMI, PLOS CB, etc. and top conferences CVPR, ICCV, ECCV, AAAI, IJCAI, ACL, ECAI, MICCAI, RECOMB, ISBI, etc.

Meanwhile, Dr. Zhen Li is the contact map prediction champion in the Olympiad in protein structure prediction (CASP12) and serves as the baseline method for the DeepMind team's AlphaFold first version. The corresponding paper has won the PLOS CB Breakthrough and Innovation Award (one per year) and is a highly cited paper in Web of Science. As a mentor, Dr. Li Zhen led the students to win the SemanticKITTI championship in the large-scale point cloud analysis competition, the second place in the ICCV2021 large-scale urban street scene understanding competition, and the first place in the IEEE ICDM Global A.I. Weather Challenge (out of 1700 teams). Finally, Dr. Li Zhen also received scientific research funding from national, provincial, municipal and industrial circles.

Academic Publications

1.     Xiaoguang Han#, Zhen Li#, Haibin Huang, Evangelos Kalogerakis, and Yizhou Yu, High-Resolution Shape Completion Using Deep Neural Networks for Global Structure and Local Geometry Inference, International Conference on Computer Vision (ICCV), Venice, October 2017. (ICCV spotlight, 2.61%) 

2.     Sheng Wang#, Zhen Li#, Yizhou Yu and Jinbo Xu. Folding membrane proteins by deep transfer learning, Cell Systems,2017 5(3):202-211.

3.     Sheng Wang, Siqi Sun, Zhen Li, Renyu Zhang and Jinbo Xu. Accurate De Novo Prediction of Protein Contact Map by Ultra-Deep Learning Model. PLOS Computational Biology, 2017. (official 1st for CASP12, Highly cited paper of web of science, PLOS Computational Biology Research Prize 2018 in the category Breakthrough Advance/Innovation.).

4.     Zhen Li, Sheng Wang, Yizhou Yu and Jinbo Xu, Predicting membrane protein contacts from non-membrane proteins by deep transfer learning. The 21st International Conference on Research in Computational Molecular Biology (RECOMB) 2017

5.     Zhen Li, Yukang Gan, Xiaodan Liang, Yizhou Yu, Hui Cheng, and Liang Lin, LSTM-CF: Unifying Context Modeling and Fusion with LSTMs for RGB-D Scene Labeling, European Conference on Computer Vision (ECCV), Amsterdam

6.     Zhen Li and Yizhou Yu, Protein Secondary Structure Prediction Using Cascaded Convolutional and Recurrent Neural Networks, Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence (IJCAI), New York

7.     Sheng Wang#, Zhen Li#, Yizhou Yu, Xin Gao†. WaveNano: a signal-level nanopore base-caller via simultaneous prediction of nucleotide labels and move labels through bi-directional WaveNets. Quantitative Biology, 2018.