Prof. Dr. Teng Li received his B.S. degree from the
University of Science and Technology of China (USTC) in 2001, his M.S. Degree
from the Institute of Automation, the Chinese Academy of Sciences (CASIA) in
2004, and his Ph.D. degree from the Korea Advanced Institute of Science and
Technology (KAIST) in 2010. He is currently a Professor at the School of
Electrical Engineering and Automation, Anhui University, Hefei, China.
Previously, he has worked in the Institute of Automation, Chinese Academy of
Sciences, Alibaba Cloud, Baidu, and Huawei. Prof. Dr. Li is mainly engaged in
the research of image and video intelligence analysis and its application in
electrical equipment. His proposed analysis method based on mid-level feature
scene classification is applied to substation inspection, which improves the
efficiency and accuracy of electrical image diagnosis. He has published more
than 70 SCI/EI papers and has 10 authorized invention patents and 1 US
invention patent. He received the IEEE T-CSVT Best Paper Award in 2014 and the
Excellent Paper Award of the National Multimedia Conference in 2018. He is also
a member of IEEE, the CCF Computer Vision Committee, and the Machine Vision Committee
of the China Graphics and Imaging Society.
Research Keywords & Expertise
machine learning
Multimedia
medical image
Image process
computer vison
Short Biography
Prof. Dr. Teng Li received his B.S. degree from the
University of Science and Technology of China (USTC) in 2001, his M.S. Degree
from the Institute of Automation, the Chinese Academy of Sciences (CASIA) in
2004, and his Ph.D. degree from the Korea Advanced Institute of Science and
Technology (KAIST) in 2010. He is currently a Professor at the School of
Electrical Engineering and Automation, Anhui University, Hefei, China.
Previously, he has worked in the Institute of Automation, Chinese Academy of
Sciences, Alibaba Cloud, Baidu, and Huawei. Prof. Dr. Li is mainly engaged in
the research of image and video intelligence analysis and its application in
electrical equipment. His proposed analysis method based on mid-level feature
scene classification is applied to substation inspection, which improves the
efficiency and accuracy of electrical image diagnosis. He has published more
than 70 SCI/EI papers and has 10 authorized invention patents and 1 US
invention patent. He received the IEEE T-CSVT Best Paper Award in 2014 and the
Excellent Paper Award of the National Multimedia Conference in 2018. He is also
a member of IEEE, the CCF Computer Vision Committee, and the Machine Vision Committee
of the China Graphics and Imaging Society.
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