Faculty

Current Position

Assistant Professor, Institute of Environmental and Occupational Health Sciences, National Yang Ming Chiao Tung University

Adjunct Assistant Professor, Institute of Environmental Engineering, National Yang Ming Chiao Tung University

Academic Degrees

1.    Ph.D., Department of Physics, Hong Kong Baptist University (2016)

2.    BEng., Electrical Engineering, National Taiwan Normal University (2008)

Experience

1.     Researcher, Industry, Science and Technology International Strategy Center, Industrial Technology Research Institute

2.     R&D Engineer, Electronic and Optoelectronic System Research Laboratories, Industrial Technology Research Institute

Awards

Wiley Outstanding Researchers "Insights from Machine Learning Techniques for Predicting the Efficiency of Fullerene Derivatives-Based Ternary Organic Solar Cells at Ternary Blend Design" (2019)

Research Interests

1.    Workplace wearable sensors for risk assessment

2.    Machine learning in healthcare and medical applications

3.    Intelligent environmental sensing

4.    Machine learning-based device modeling and performance optimization

5.    Scalable fabrication and coating methods for optoelectronic thin films

6.    Functional thin films and nanostructures for sensors

Selected Publication

1.   Min-Hsuan Lee, Lixiang Chen, Ning Lia and Furong Zhu, MoO₃-induced oxidation doping of PEDOT:PSS for high performance full- solution-processed inverted quantum-dot light emitting diodes. J. Mater. Chem. C, 5, 10555-10561(2017)

2.   Lixiang Chen^,†Min-Hsuan Lee,^† Yiwen Wang, Ying Suet Lau, Ali Asgher Syed, and Furong Zhu, Interface Dipole for Remarkable Efficiency Enhancement in All solution processable Transparent Inverted Quantum Dot Light emitting Diodes. J. Mater. Chem. C, 2018,6, 2596-2603.

3.    Weidong Zhang, Weixia Lan, Min-Hsuan Lee, Jai Singh, Furong Zhu, A versatile solution-processed MoO3/Au nanoparticles/MoO3 hole contact for high performing PEDOT:PSS-free organic solar cells. Organic Electronics, 52,1-6 (2018)

4.    Min-Hsuan Lee, Insights from machine learning techniques for predicting the efficiency of fullerene derivatives-based ternary organic solar cells at ternary blend design, Advanced Energy Materials. Adv. Energy Mater. 2019, 1900891

5.    Min-Hsuan Lee, Machine Learning for Understanding the Relationship between the Charge Transport Mobility and Electronic Energy Levels for n‐Type Organic Field‐Effect Transistors. Adv. Electron. Mater. 2019, 1900573

6.    Min-Hsuan Lee, Robust random forest based non-fullerene organic solar cells efficiency prediction, Organic Electronics, 76, 2020,105465

7.    Min-Hsuan Lee, A Machine Learning–Based Design Rule for Improved Open‐Circuit Voltage in Ternary Organic Solar Cells. Adv. Intell. Syst., 2020, 2: 1900108

8.   Min-Hsuan Lee, Performance and Matching Band Structure Analysis of Tandem Organic Solar Cells Using Machine Learning Approaches. Energy Technol., 2020, 8: 1900974

9.    Min-Hsuan Lee, Identification of host–guest systems in green TADF-based OLEDs with energy level matching based on a machine-learning study, Phys. Chem. Chem. Phys., 2020,22, 16378-16386

10.   Lan, Z., Lee, M.* and Zhu, F.* (2021), Recent Advances in Solution-Processable Organic Photodetectors and Applications in Flexible Electronics. Adv. Intell. Syst. 2100167 (Invited Review)

11.   Min-Hsuan Lee, Identifying correlation between the open-circuit voltage and the frontier orbital energies of non-fullerene organic solar cells based on interpretable machine-learning approaches, Solar Energy, 234, 2022, 360-367