Dr. Haoming Ma
Dr. Haoming Ma is a Staff Research Scientist at the University of Wyoming’s School of Energy Resources and Hydrogen Energy Research Center, where he is involved in multiple DOE-funded projects on carbon management, hydrogen production, and energy system analysis. Prior to that, he was a Postdoctoral Fellow at the University of Texas at Austin. Dr. Ma holds a Ph.D. in Chemical and Petroleum Engineering from the University of Calgary, an M.Sc. from the University of Pennsylvania, and an M.Sc. and B.Sc. from Penn State University. His research leverages artificial intelligence and remote sensing data into physics-based geo-energy system analysis to evaluate the multi-perspective performance of emerging energy and environmental technologies from process to supply-chain scales. His current work focuses on subsurface carbon capture, utilization, and storage (CCUS); methane emissions across geo-energy supply chains; underground hydrogen production and storage; and low-carbon fuel innovations. By coupling AI with system-level analysis, Dr. Ma’s research elucidates the complex environmental and financial trade-offs of geo-energy systems to inform early-stage technology deployment and investment decisions. He has authored nearly twenty peer-reviewed publications and serves on technical committees for professional societies, advancing cross-disciplinary collaboration in sustainable energy and environmental systems.
Abstract
AI-Enabled Energy System Modeling and Analysis:
From Innovation to Supply Chains
The global transition toward sustainable energy systems requires multi-disciplinary frameworks that integrate environmental performance, technological innovation, and policy design. This seminar will present AI-enabled system modeling and analysis frameworks for energy recovery and carbon management that unify data, physics, and policy across scales, from unit process modeling to global supply chains. By integrating subsurface–surface process modeling through data-driven methods and leveraging multi-scale methane emission measurements (from satellite data to ground sensors) into techno-economic and life cycle analysis (TEA&LCA), this seminar will provide system-level insights into the environmental and economic feasibility of emerging energy technologies. Case studies will highlight applications in subsurface carbon capture, utilization, and storage (CCUS), underground hydrogen storage, and liquefied natural gas (LNG) supply-chain analysis. These examples will demonstrate how AI-driven modeling can accelerate technological deployment, enhance carbon accounting accuracy, and inform sustainable infrastructure and policy design. The seminar will conclude by discussing future opportunities to extend these frameworks toward integrated resource management, aligning with the University of Kentucky’s strengths in energy research and chemical engineering.