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David C. Li, PhD

Associate Professor of Big Data Analytics

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Dr. David C. Li is a seasoned professional with a strong academic background and nearly two decades of experience in the information technology sector. He earned his M.S. degree in Information Technology Application from Monash University, Melbourne, Australia, and subsequently obtained his Ph.D. degree in Computer Science and Information Engineering from the esteemed National Central University, Taoyuan, Taiwan. Dr. Li has demonstrated his expertise through various leadership roles, including serving as the Chief Researcher in the R&D department, Deputy Manager of Data Science, and Director of the Advanced Technology Office at Foxconn TransIOT Technology Co., Ltd.


Currently holding the position of Associate Professor at the International College of Ming Chuan University, Dr. Li is actively involved in research and academia. His research interests span a diverse range of topics, including Internet of Things, Internet of Vehicles, Big Data Analysis, Artificial Intelligence Applications, Cloud Computing, Edge Computing, Software Engineering, and Software Project Management.

Research Interests

  • Big Data Analytics

  • Internet of Things

  • Internet of Vehicles

  • Artificial Intelligence

  • Software Defined Network

  • Cloud Computing

  • Edge Computing

  • ​Software Engineering​

Selected Publications

David Chunhu Li, et al., “NNSplit-SØ REN: Supporting the Model Implementation of Large Neural Networks in a Programmable Data Plane,” Computer Networks, vol. 222, p. 109537, 2023. DOI: [SCIE, Impact Factor: 5.6, 9/62 in Computer Science, Hardware & Architecture]

David. Chunhu. Li*, et al., "Macroscopic Big Data Analysis and Prediction of Driving Behavior With an Adaptive Fuzzy Recurrent Neural Network on the Internet of Vehicles," in IEEE Access, vol. 10, pp. 47881-47895, 2022, doi: 10.1109/ACCESS.2022.3171247. [SCIE, Impact Factor: 3.9]

David Chunhu Li, et al., “Fuzzy-based Microservice Resource Management Platform for Edge Computing in the Internet of Things”, Sensors, 2021 May 31; 21(11): 3800. [SCIE, Impact Factor: 3.9]


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