SIGEnergy Doctoral Dissertation Award 2025 Winner

SIGEnergy establishes an annual Doctoral Dissertation Award to recognize an outstanding Ph.D. thesis in the field of energy systems and informatics. The key evaluation criteria are the depth, impact, and novelty of the thesis’ contributions.

For the 2025 cohort, the award committee has chosen Ruihang Wang from Nanyang Technological University for their thesis titled “Physics-Informed Machine Learning for Green Data Center Operations”

Ruihang Wang is a Research Fellow at the College of Computing and Data Science, Nanyang Technological University (NTU), where he also earned his Ph.D. His research focuses on machine learning for optimizing large-scale, energy-intensive data center operations. His thesis, Physics-Informed Machine Learning for Green Data Center Operations, integrates deep learning and physical principles to address data scarcity and safety challenges. His work has been published in top venues including ACM/IEEE ICCPS, ACM Transactions on Cyber-Physical Systems, ACM e-Energy, and ACM BuildSys. Notably, his research has also been applied in industry-grade data centers, contributing to solutions such as high-fidelity digital twins and intelligent decision engines for operational optimization.

Ruihang will receive the award at ACM e-Energy 2025, Rotterdam, Netherlands, June 17 – 20, 2025.

SIGEnergy Doctoral Dissertation Award 2025 Honorable Mentions (in alphabetical order of last names):

Jing Yu at California Institute of Technology for their thesis titled “Safe and Scalable Learning-based Control: Theory and Application in Sustainable Energy Systems”

Jing Yu is an incoming Assistant Professor of Electrical and Computer Engineering at the University of Washington, Seattle. She is currently a postdoctoral fellow with R. Srikant at the University of Illinois Urbana-Champaign and previously worked with Necmiye Ozay at the University of Michigan. She earned her Ph.D. in Control and Dynamical Systems from Caltech in 2024, co-advised by John Doyle and Adam Wierman. Her honors include the 2022 Amazon AI4Science Fellowship, a Best Paper Finalist Award at ACM e-Energy 2022, and the 2024 Amori Doctoral Prize in Computing and Mathematical Sciences at Caltech.

Tianyu Zhang at University of Alberta for their thesis titled “Data-Enabled Optimization of Building Operations”

Tianyu Zhang received his Ph.D. in 2024 from the University of Alberta, where he was advised by Omid Ardakanian. His research focused on fundamental reinforcement learning algorithms and smart building control systems, with the goal of optimizing building operations and occupant relocation to reduce energy consumption while maintaining thermal and visual comfort. He developed scalable control strategies and simulation platforms by integrating transfer learning, diversity-induced multi-agent reinforcement learning, and combinatorial optimization to enable energy-aware decision-making in complex, multi-agent environments. He is currently a Senior AI Research Scientist at Autodesk Research, where he focuses on graph-structured data and developing scalable reinforcement learning solutions for generative models. His broader research interests span machine learning, intelligent control, and sustainable systems.