I am Hao-Yeh Lee, currently employed at National Taiwan University of Science and Technology. Previously, I worked as a senior engineer at Newplus Systems Co., Ltd., accumulating valuable practical experience in the industry. I graduated from the Department of Chemical Engineering at National Taiwan University and obtained a doctoral degree. Since 2011, I have been serving as an assistant professor at the Department of Chemical Engineering at National Taiwan University of Science and Technology, promoted to associate professor in 2016, and eventually became a professor in 2020.

My research expertise lies in chemical process systems engineering, particularly in process development, intensification, design, and control. Over the past five years, I have been actively involved in industry-academia collaboration projects and projects funded by the Ministry of Science and Technology, publishing several influential academic papers. Among them, the intelligent factory prediction and monitoring model developed in collaboration with Formosa Plastics Group has saved substantial costs for the company and made significant contributions to process quality improvement.

Furthermore, I am actively engaged in talent cultivation, guiding numerous outstanding students to participate in academic competitions and achieve excellent results. At the same time, I actively participate in industry services, assisting enterprises in solving process design and optimization problems, and have achieved fruitful results.

In terms of academia, I have received numerous important awards and honors both domestically and internationally, and have served as a keynote speaker at international conferences. My goal is to continuously deepen my research, make greater contributions to the development of the chemical engineering field, and cultivate more outstanding talents in chemical engineering.

Beside professor, there are 11 master's students, one of them is an international student. Additionally, there are 2 doctoral students, one of them is an international student, and one administrative assistant.

The Process Systems Engineering Research Lab primarily utilizes first-principle and data-driven models to simulate units or entire processes.

First-principle models are based on thermodynamics, kinetics, and mass-energy balance, providing a theoretical foundation and physical significance.

Data-driven models, on the other hand, utilize a large amount of known data to search for the most relevant inputs to the desired output. Through machine learning, they establish the relationship between inputs and outputs.

By using these models, it's possible to find and estimate optimal operating conditions without the need for trial and error on-site, thereby reducing the cost of process optimization. Additionally, these models can be used to determine whether process results are reasonable, if instruments need calibration, and achieve detection capabilities.