Laboratory Introduction

The purpose of this laboratory is to coordinate and utilize resources to conduct research related to scheduling algorithms and data science. One of the research directions is the analysis and design of scheduling algorithms, exploring how to develop specific scheduling algorithms based on the structure and characteristics of problems, in order to achieve faster problem-solving and improved solution quality.
The research direction in data science aims to cultivate students' ability to handle big data in companies (data preprocessing, application of machine learning models and statistical methods). Python is the basic language in this laboratory. Even if you have not previously studied Python but are determined and passionate about learning, you are welcome to join. One of the learning goals of this laboratory is to enhance students' computer skills to equip them with the information capabilities necessary for solving future problems. Students who wish to join the laboratory need to be determined to use English to search for relevant materials or literature and must have the ability to read English literature.
This laboratory adopts SAP Core components: MM, PP, and SD modules. Students are required to participate in one of the ERP center's student teams for one module and contribute to the implementation of the SAP system, accumulating and passing on teaching materials. Graduates often use this as a stepping stone for their job search, and a certain percentage of them continue to use SAP systems as tools in their future careers.
For the 2022 September intake of the master's program general students, the research direction will only focus on scheduling-related problems. We welcome new students this year who are interested in research on scheduling problems to join.
To join Professor Shen's lab, incoming students are required to attend the Python coding and data structure/type improvement class during the summer before entering, to make up for any deficiencies in research background.

Main Areas:

• Scheduling Algorithms and Optimization
• Data Science