菜单总览
— 优秀师资 —

李镇

职位:

研究助理教授

教育背景:

博士(香港大学)

研究领域
深度学习,蛋白质结构预测,计算机视觉
Email

lizhen@cuhk.edu.cn

个人简介:


李镇博士分别在2011年和2014年于中山大学获得学士和硕士学位,在2018年于香港大学获得博士学位。李镇博士同时在2016年和2018年于芝加哥大学,丰田芝加哥研究院进行访问学者研究工作。李镇博于2018年九月加入香港中文大学深圳和深圳大数据研究院。

李镇博士的研究方向主要是利用数据挖掘和深度学习算法进行蛋白质结构预测,从序列层面到折叠层面。他是蛋白质结构预测竞赛CASP12冠军的主要成员,并获得PLOS CB 2018最新突破和创新奖项。他同时从事机器学习算法和三维计算机视觉问题的研究,例如RGB-D语意分割,形状补全等。


学术著作:


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.