Skip to main content
Featured Scientist
Michael T.S. Lee, Ph.D.

College of Management

Distinguished  Professor 

Department of Business Administration

Personal Website

Artificial intelligence, and industrial engineering and management.

  Professor Michael T.S. Lee is currently the Vice President of International Affairs and University Distinguished Professor at Fu Jen Catholic University. He obtained his Ph.D. in Operation Research and Industrial Engineering from The University of Texas at Austin. USA. He used to serve as the Dean of the College of Management, Director of Master Program of Global Entrepreneurial Management, and Director of Graduate Institute of Management at Fu Jen Catholic University.
  The areas specialties of Professor Michael T.S. Lee are data mining, applied artificial intelligence, and industrial engineering and management.
  The results of data mining are: <using data exploration technology to establish a disease risk-factor analysis model -- illustrated with the dialysis treatment of diabetic nephropathy as an example> (2016), <based on data exploration technology to conduct correlation analysis between diabetes and breast cancer > (2016)
 Academic achievements making use of Artificial Intelligence:
“A Hybrid Machine Learning Scheme to Analyze the Risk Factors of Breast Cancer Outcome in Patients with Diabetes Mellitus” (2018)
  Academic achievements in terms of industrial engineering and management:
“A Study Of Industry - Institutes Collaborative Behaviors In Initialization, Collaboration, Transference And Commercialization Stage From The Technology Readiness Level” (2016)
“A multi-stage control chart pattern recognition scheme based on independent component analysis and support vector machine” (2016)
  Current research projects by Professor Michael T.S. Lee are:
1. Integrated research on health technology, fintech, and sports technology based on artificial intelligence classification and prediction technology;
2. Real volatility prediction of financial big data -- integrated framework of time-series motif analysis and machine learning technology