Wenkai Guan
Sustainable AI; energy-efficient system-on-chip design; machine learning for data center optimization
Background & Expertise
Wenkai Guan is a tenure-track assistant professor of Computer Science at the University of Minnesota Morris, specializing in the intersection of generative AI and environmental sustainability. His research aims to address the concerns of AI data centers' impacts on water quantity and quality.
As the Lead Principal Investigator for a 2026 IonE Impact Goals grant and a core collaborator with the UMN Water Circularity Center, Guan works with an interdisciplinary team to develop predictive models and decision-support tools that translate dynamic micro-level GPU workloads into macro-level water quantity and quality forecasts. His work empowers data center operators, water treatment providers, regulators and communities to safely optimize water circularity while mitigating thermal and chemical pollution.
An active member of IEEE, Guan's broader expertise includes sustainable AI, energy-efficient system-on-chip design, and machine learning for data center optimization. Prior to joining UMN, he earned his Ph.D. and M.S. from Marquette University, his B.S. from the Wuhan University of Technology, and served as a research assistant at the Huazhong University of Science and Technology.
Featured Project: Wenkai Guan
AI's Hidden Thirst
A predictive decision-support tool for data center water circularity and governance
All Projects Featuring Wenkai Guan
AI's Hidden Thirst
A predictive decision-support tool for data center water circularity and governance
