Dr. Pan Xiang, PhD graduate of the Department of Information Engineering (2022) has won the prestigious 2024 ACM SIGEnergy Doctoral Dissertation Award for his PhD thesis titled: “Deep Neural Networks for Optimal Power Flow in Electric Power Systems: Design, Analysis, and Experiments”.

Pan Xiang received his Ph.D. in Information Engineering from The Chinese University of Hong Kong in 2022. His research focuses on machine learning and its application in energy systems and multimedia signal processing, e.g., Neural Image Compression. Pan Xiang is currently a researcher at Tencent Media Lab. He (as the core member) received the Winner Award of the CVPR 2018 Challenge on Learned Image Compression. During his Ph.D., he developed the DeepOPF as the first work in the literature that employs deep neural networks (DNN) to solve optimal power flow (OPF) problems directly by learning input-to-solution mapping. Once DNNs learn the mapping, they can be used to obtain quality solutions upon giving load inputs instantly without iteration, speeding up the computation time by 2~4 orders of magnitude compared to the state-of-the-art iterative OPF solvers. 

During his PhD studies at CUHK, Pan Xiang was supervised by former IE Professor Chen Minghua and Professor Zhao Changhong. His research focuses on machine learning and its applications in energy systems and multimedia signal processing, such as Neural Image Compression. Currently, he is a researcher at Tencent Media Lab.

The annual SIGEnergy Doctoral Dissertation Award recognizes an outstanding PhD thesis in the field of energy systems and informatics. For more details, please check: https://energy.acm.org/doctoral-dissertation-award-2024-winner/