Long Cheng

Profile

Long Cheng is a Professor at the School of Control and Computer Engineering, North China Electric Power University, and also a research staff member at the State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources. His research interests include process intelligence, smart energy systems, deep reinforcement learning, and foundation models. He also contributes to the university’s digital and intelligent transformation initiatives. He received his bachelor’s degree from Harbin Institute of Technology in 2007, and later obtained his master’s and Ph.D. degrees in Germany and Ireland, respectively. He conducted postdoctoral research under the supervision of Prof. Wil van der Aalst, widely recognized as the “Godfather of process mining”, and subsequently served as an Marie Skłodowska-Curie Fellow and an Assistant Professor at Dublin City University, Ireland. Prof. Cheng has published more than 150 papers in leading international journals and conferences. He has been listed among Stanford University’s World’s Top 2% Scientists for three consecutive years, and has received honors including the EU Marie Skłodowska-Curie Fellowship and the Springer Nature Outstanding Editor Award. He has led or participated in more than ten research projects funded by the National Natural Science Foundation of China, the National Key R&D Program of China, State Grid Corporation of China, CRRC, and other organizations. His research outcomes have received honors including the Second Prize of the Shandong Provincial Science and Technology Progress Award and a letter of appreciation from the Ministry of Science and Technology of China. He is a Fellow of the British Computer Society and a Senior Member of IEEE, and serves as a Special Issue Editor of Journal of Cloud Computing, as well as an editorial board member or guest editor for several international journals in the fields of ACM, IEEE, and artificial intelligence.

Research Direction

• Process Intelligence
• Smart Energy Systems
• Deep Reinforcement Learning
• Agentic AI