1.Brief Profile
Qingkui CHEN, Doctoral, Professor, Supervisor for Ph.D. The leader in Computer Science and Technology. A Member of the Academic Committee of the University of Shanghai for Science and Technology. The Director of Shanghai Smart Home Large-scale Common Technology Research and Engineering Center. A Member in CCF DPCS, CCF CSCW Special Committee, Technical expert in the Ministry of Science and Technology of China, China National Science Foundation, Shanghai, Zhejiang Province, Guangdong Province, etc. An International Journal reviewer of IEEE Transactions on Reliability, The Journal of Supercomputing, Cluster Computing, and Information Sciences. Presided over the completion of 3 National Natural Science Foundation projects and more than 10 key projects of Shanghai or provincial and ministerial level. Won the first prize of Provincial and ministerial science and technology progress. Won 2 times of Shanghai Technical Invention Awards. Published more than 200 papers in SCI, EI, and core journals in national first-level journals such as Chinese Journal of Computers, Journal of Software, Journal of Computer Research and Development, Chinese Journal of Electronics, and Journal of Communications, as well as some famous international journals. Obtained more than 10 invention patent authorizations. Currently engaged in the research of large-scale access to the Internet of Things, artificial intelligence model based on GPU cluster, Bus crowding degree based on deep learning, road traffic congestion and behavior for old people service, edge computing models and applications which are all under the support of national, Shanghai and other projects Carry out extensive cooperation research with bus and bus groups, smart homes, video surveillance, elderly care services, Internet of Things, intelligent security, and other industry companies. The research results of "Real-time Video Image Intelligent Analysis Technology" hosted by him use the parallel GPU cluster technology to improve the real-time video analysis capabilities of the deep neural network model, which can effectively support the analysis of the vehicle operation status of large-scale public transport systems for large cities, currently has been used in Shanghai bus system. Xinhua News Agency, Wenhui, Shanghai Observer, Sina, and other major media have reported this as one of the Shanghai Top 10 benefits for citizens applications. Has supervised 6 Ph.D. students, and another 6 Ph.D. candidates are under supervised, trained nearly 100 graduate students. The main courses provided are high-performance cluster computing (for graduate students) and database principles (for undergraduate students).
2. Research Area
Large-scale Internet of Things, parallel computing, GPU processing structure for large-scale artificial intelligence, intelligent real-time analysis for the big data of video, network computing, edge computing, artificial intelligence and its applications.
3. Contact Information
Email address: chenqingkui@usst.edu.cn
