Assist. Prof. Dr. I-Ming Jiang | Dynamic Stochastic Processes | Best Researcher Award

Assist. Prof. Dr. I-Ming Jiang | Dynamic Stochastic Processes | Best Researcher Award

Yuan Ze University | Taiwan

Dr. I-Ming Jiang is an Assistant Professor in the College of Management at Yuan Ze University, specializing in financial engineering, risk management, big-data statistical applications, real options, and empirical financial studies. His work also covers asset pricing in incomplete markets and the use of artificial intelligence in trading systems and technical analysis. He teaches courses such as calculus, financial innovation, risk management, quantitative and computing methods, and money and banking. He has held multiple academic appointments in finance and has published 29 research works. His current research focuses on computational methods and dynamic stochastic processes.

Featured Publications

Huishui Su, Jiang, I.-M., & Liu, D. (2025). Detecting financial fraud risk using machine learning: Evidence based on different categories and matching samples. Finance Research Letters, 85(Part A), 107858.

Liu, Y.-H., Jiang, I.-M., & Hung, M.-W. (2025). Pricing vulnerable options when debts have performance-sensitivity provisions. International Review of Economics and Finance.

Mr. Danyang Mei | Data Analysis Techniques | Best Researcher Award

Mr. Danyang Mei | Data Analysis Techniques | Best Researcher Award

Beihua Institute of Aerospace Engineering  | China

Mr. Mei Danyang is an accomplished researcher and engineer specializing in energy equipment design, drilling technology, and deep learning applications in mechanical systems. He has led and participated in several innovation and optimization projects related to hydraulic motors, drilling tools, and equipment industrialization. His research focuses on advanced modeling and prediction techniques, particularly using deep learning for energy-efficient drilling torque prediction. Mr. Danyang has received multiple national-level awards, including top prizes in the China Postgraduate Energy Equipment Innovation Design Competition, along with several academic scholarships and honors for excellence and innovation. Professionally, he has contributed to the design and development of large-diameter raise boring machines, downhole tools, and machine tool electrical systems, demonstrating strong expertise in structural design, vibration analysis, and system integration.

Profile: Scopus 

Featured Publications

Cao, W., Mei, D., Guo, Y., & Ghorbani, H. (2025). Deep learning approach to prediction of drill-bit torque in directional drilling sliding mode: Energy saving. Measurement: Journal of the International Measurement Confederation.