Center for Internet of Things Innovation
Research Field
Shuo-Yan Chou is a distinguished professor of industrial management and the director of the Center for Internet of Things Innovation (CITI) at National Taiwan University of Science and Technology (Taiwan Tech). His research interests include Internet of Things innovation, technology-enabled services, smart energy, smart logistics, sustainability, AI, blockchain, as well as smart city and Industry 4.0 development. Besides having conducted more than 120 research projects and engaged in many international cooperation activities, he has served as the dean of international affairs at Taiwan Tech, the editor-in-chief of the Journal of Chinese Institute of Industrial Engineers (Taylor and Francis) as well as on the editorial boards of IJFS, ASCE JEE, JDE, PLOS, Wind, JIPE, among others. Dr. Chou has been a visiting professor/scholar at MIT Media Lab, ETH, Peking University, Nagoya University, Hanyang University, Hong Kong University of Science and Technology and University of Washington. He has also served as the general chair for CE2009, 2010 INFORMS Service Science Conference, MCP AP2010, and CE2014 and as the organizer of more than 30 international events locally or internationally. Dr. Chou received his PhD in industrial and operations engineering from the University of Michigan.
Center for Internet of Things Innovation
Internet of Things innovation, technology-enabled services, sustainability, smart energy, smart logistics, AI, blockchain, as well as various applications in the context of smart city and Industry 4.0 development
Dr. Chou editor-in-chief of the Journal of Chinese Institute of Industrial Engineers published by Taylor and Francis as well as on the editorial boards of IJFS, ASCE JEE, JIPE, among others. He has been a visiting professor/scholar at MIT Media Lab, ETH, Hanyang University (Korea), Peking University, Nagoya University, Hong Kong University of Science and Technology and University of Washington.
Dr. Chou received his BBA in industrial management from National Cheng-Kung University, Taiwan in 1983, his MS and PhD in industrial and operations engineering from the University of Michigan in 1987 and 1992, respectively.
2 Vacancies
Job Description
- Develop a data-driven approach to drive the replenishment and order assignment for the Robotic Order Fulfillment System (RMFS). The intern is required to have a strong background in data analytics and optimization modeling and will be working with the research team on tackling key performance problems of the RMFS operation.
- Utilizing a Large Language Model (LLM) for developing a Knowledge Management System (KMS). The intern should have a strong background in AI/ML and software engineering and be willing to explore and learn LLM and KMS.
Preferred Intern Education Level
The intern should be at least a senior student or graduate student with the background in computer science or industrial engineering.
Skill sets or Qualities
- For RMFS: experiences in data analytics, time series analysis, mathematical programming models, metaheuristics, and preferably some knowledge of warehouse management.
- For LLM for KMS: AI/ML (especially LLM), software engineering, and some experience on Knowledge Management Systems.