Guan LUO, Associate Professor
Ph.D., IEEE Member, BMVA Member
Director of Big Data Laboratory in Healthcare
Member of the Intelligence Service Committee, Chinese Association for Artificial Intelligence (CAAI)
Member of China Computer Federation (CCF)
National Laboratory of Pattern Recognition
Institute of Automation, Chinese Academy of Sciences
No.95 Zhongguancun East Road, Beijing, China
1. Big Data Mining and Analysis in Medicine and Healthcare
2. Deep Learning in Genomics
3. Text Mining in Medical Literature
4. Mobile Medical Technology
5. Time-series Data Mining
6. Image Classification and Video Event Recognition
1. 2001 – 2004, Ph.D., Department of Electronic Engineering, Northwestern Polytechnical University
2. 1999 – 2001, M.Eng., Department of Electronic Engineering, Northwestern Polytechnical University
3. 1995 – 1999, B.Eng., Department of Electronic Engineering, Northwestern Polytechnical University
1. 2006 – present, Associate Professor, National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences
2. 2004 – 2006, Senior Research Associate, School of Creative Media, City University of Hong Kong
- You Qiang, Wu Ou,Luo Guan, Hu Weiming. A probabilistic matrix factorization method for link sign prediction in social networks. International Conference on Machine Learning and Data Mining in Pattern Recognition (ICMLDM): 2016, pp. 415-420.
- You Qiang, Wu Ou,Luo Guan, Hu Weiming. Metadata-based clustered multi-task learning for thread mining in web communities. International Conference on Machine Learning and Data Mining in Pattern Recognition (ICMLDM): 2016, pp.421-434.
- Yang Libao, Li Zhe,Luo Guan. MH-ARM: A multi-mode and high-value association rule mining technique for healthcare data analysis. International Conference on Computational Science and Computational Intelligence (ICCSCI): 2016, pp. 122-127.
- Guan Luo, Shuang Yang, Guodong Tian, Chunfeng Yuan, Weiming Hu, and Stephen J. Maybank. Learning Human Actions by Combining Global Dynamics and Local Appearance. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI): 2014(Dec) ,36(12) ,2466-2482 (Featured Paper).
- Guan Luo, Weiming Hu. Learning silhouette dynamics for human action recognition. IEEE International Conference on Image Processing (ICIP): 2013, pp.2832-2836 (Oral).
- Haoran Wang, Chunfeng Yuan, Guan Luo, Weiming Hu, Changyin Sun. Action recognition using linear dynamic systems. Pattern Recognition: 2013, 46(6), 1710-1718.
- Wei Li, Bing Li, Xiaoqin Zhang, Weiming Hu, Hanzi Wang, Guan Luo. Occlusion Handling with L1-Regularized Sparse Reconstruction. Asian Conference on Computer Vision (ACCV): 2010, 630-640.
- Xi Li, Weiming Hu, Zhongfei Zhang, Xiaoqin Zhang, Guan Luo. Trajectory-Based Video Retrieval Using Dirichlet Process Mixture Models. British Machine Vision Conference (BMVC): 2008, 1-10 (Oral).
- Xi Li, Weiming Hu, Zhongfei Zhang, Xiaoqin Zhang, Guan Luo. Robust Visual Tracking Based on Incremental Tensor Subspace Learning. IEEE International Conference on Computer Vision (ICCV): 2007, 1-8.
- Guan Luo, Hao, C. Y., Zhang, W., et al. Research on Skeleton Modeling Method for Virtual Human based on Multi-Rigid-Body System Theory. Journal of Computer Aided Design and Computer Graphics: 2005, 17, 1354-1359.
- Guan Luo, Hao, C. Y., Huai, Y. J., et al. Design and Implementation of a Virtual Reality Engine. Chinese Journal of Computers: 2001, 24, 1163-1169.
1. A spatio-temporal feature extraction method based on linear dynamical system. SIPO China, 201410363723.8
2. A similarity measurement based on linear dynamical system. SIPO China, 201410427511.1
3. A primitive database for video classification. SIPO China, 201410578801.6
4. A key frame extraction method based on linear dynamical system. SIPO China, 201410578831.7
1. 2015 – present, Clustering massive online health records for precision treatment, supported by Beijing Haola Technology Co., Ltd.
2. 2013 – 2016, Motion pattern mining by linear dynamical systems, supported by National Natural Science Foundation of China (NSFC)
3. 2008 – 2010, Motion pattern mining based on sparse spatio-temporal features, supported by National Natural Science Foundation of China (NSFC)