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. Video Action Recognition and Understanding
2. Multi-modal Video Analysis and Retrieval
3. Video Generation from Text and Image
4. Medical Image Recognition and Processing
5. Data Mining and Analysis in Medicine and Healthcare
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
- Guan Luo, Shuang Yang, Guodong Tian, Chunfeng Yuan, Weiming Hu, Stephen J. Maybank: Learning Human Actions by Combining Global Dynamics and Local Appearance. IEEE Trans. Pattern Anal. Mach. Intell.(TPAMI) 36(12): 2466-2482 (2014)(Featured Paper)
- Guan Luo, Jiutong Wei, Weiming Hu, Stephen J. Maybank: Tangent Fisher Vector on Matrix Manifolds for Action Recognition. IEEE Trans. Image Processing(TIP) 29: 3052-3064 (2020)
- Ge F, Zheng A, Wan M, Luo G and Zhang J (2021) Psychological State Among the General Chinese Population Before and During the COVID-19 Epidemic: A Network Analysis. Front. Psychiatry 12:591656.
- Chaochen Wu, Guan Luo, Chao Guo, Yin Ren, Anni Zheng, Cheng Yang: An attention-based multi-task model for named entity recognition and intent analysis of Chinese online medical questions. J. Biomed. Informatics 108: 103511 (2020)
- Qing Yin, Guan Luo, Xiaodong Zhu, Qinghua Hu, Ou Wu: Semi-interactive Attention Network for Answer Understanding in Reverse-QA. PAKDD (2) 2019: 3-15
- Haoran Wang, Chunfeng Yuan, Guan Luo, Weiming Hu, Changyin Sun: Action recognition using linear dynamic systems. Pattern Recognition 46(6): 1710-1718 (2013)
- Guan Luo, Weiming Hu: Learning silhouette dynamics for human action recognition. ICIP 2013: 2832-2836 (Oral)
- Juntian Lin,Guan Luo, Zhu Zhan, Xiaoyao Guan, A deep learning approach to mining the relationship of depression symptoms and treatments for prediction and recommendation. International Conference on Information Science and Applications (ICISA): 2018, pp. 413-422.
- 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.
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)