Dean, East China jiaotong university, Japan
👨🏫 Dr. Xiaohui Huang is an Associate Professor at the School of Information Engineering, East China Jiaotong University. He earned his PhD from the School of Computer Science, Harbin Institute of Technology in November 2014. He has been a visiting scholar at the German Cancer Research Center and Nanyang Technological University. Dr. Huang has been leading several high-impact research projects funded by national and provincial bodies. He is an expert reviewer for various prestigious journals and a member of notable academic associations.
Profile
Scopus
Education
🎓 PhD in Computer Science, Harbin Institute of Technology, November 2014, German Cancer Research Center, December 2010 – October 2011, School of Computer Science and Engineering, Nanyang Technological University, November 2017 – November 2018
Experience
💼 Associate Professor, School of Information Engineering, East China Jiaotong University, January 2018 – Present
Lecturer, School of Information Engineering, East China Jiaotong University, December 2014 – December 2017
Visiting Scholar, Nuclear Medicine Research Group, German Cancer Research Center, December 2010 – October 2011
Software Engineer, Yichun Branch, China Telecom, August 2008 – February 2010
🔬 Research Interests
Deep Learning. Remote Image Analysis. Intelligent Transportation
🏆 Awards
Principal Investigator for various prestigious research projects including the National Natural Science Foundation of China and Jiangxi Province Natural Science Foundation.
Publications
Multi-view dynamic graph convolution neural network for traffic flow prediction. Expert Systems With Applications, 2023 (SCI Zone 1 top)
Cited by: 15 articles
MAPredRNN: Multi-attention predictive RNN for traffic flow prediction by dynamic spatio-temporal data fusion. Applied Intelligence, 2023 (SCI Zone 2)
Cited by: 10 articles
SS-TMNet: Spatial–Spectral Transformer Network with Multi-Scale Convolution for Hyperspectral Image Classification. Remote Sensing, 2023 (SCI Zone 2, top)
Cited by: 8 articles
Multi-mode dynamic residual graph convolution network for traffic flow prediction. Information Sciences, 2022 (SCI Zone 1 top)
Cited by: 20 articles
A time-dependent attention convolutional LSTM method for traffic flow prediction.