Yunchuan Liu

Project: A comprehensive analysis and actionable recommendations to enhance the preparedness, planning, and mitigation strategies of IIN member universities regarding the energy and water impacts of data center

Research Interests:

• Machine Learning
• Data Mining
• Embedded System

Yunchuan Liu is an Assistant Professor in the Division of Science, Mathematics and Technology at Governors State University, Illinois . He earned his Ph.D. in Computer Science and Engineering from the University of Nevada, Reno, under the supervision of Professor Lei Yang.

Dr. Liu’s research interests lie at the intersection of Machine Learning, Data Mining, Embedded Systems, and Power Systems. His recent work has focused on developing robust data-driven solutions for critical infrastructure, with a particular emphasis on leveraging Phasor Measurement Unit (PMU) data for smart grid applications. He has made significant contributions to event classification and detection in power transmission grids, as well as applying federated learning and self-evolving neural networks for distributed learning tasks and renewable energy forecasting (specifically short-term wind farm generation) .

His scholarly work has been published in leading journals and conferences, including the IEEE Internet of Things Journal and IEEE Transactions on Smart Grid, as well as the IEEE PES General Meeting and IEEE SmartGridComm. Notably, his research on weakly supervised event classification using real-world PMU data received a Best Conference Paper Award at the 2022 IEEE PES General Meeting.