Ce Guo

Dr. Ce Guo is a Research Fellow at the Department of Computing, Faculty of Engineering, Imperial College London. He is affiliated with the Centre for High-Throughput Digital Electronics and Machine Learning. His research focuses on interdisciplinary areas including Electrical and Electronic Engineering, Computer Hardware, Software, and Distributed Computing, with a strong emphasis on machine learning applications in transportation systems and energy networks. Dr. Guo’s work addresses challenges such as smart grid integration of electric vehicles, traffic prediction under disruptions, and optimization of autonomous transit systems.

His research interests span traffic engineering, machine learning, and energy systems. Key areas include developing predictive models for urban traffic using deep learning techniques, optimizing charging infrastructure for electric vehicles, and analyzing heterogeneous traffic flows involving autonomous and human-driven vehicles. He also explores domain adaptation frameworks to enhance the transferability of machine learning models across different transportation networks.

Dr. Guo has contributed to over 30 publications, with recent work emphasizing real-time traffic management, knowledge transfer in network-scale predictions, and the integration of computational methods with transportation infrastructure. His research often bridges theoretical advancements with practical applications, aiming to improve urban mobility efficiency and sustainability.

Notable technical contributions include frameworks for short-term traffic prediction, signal priority strategies for buses, and methodologies for calibrating car-following models. His work on EV charging patterns and load management has implications for grid stability and renewable energy adoption. Despite his prolific output, no scientific awards are explicitly mentioned in the provided texts.