Team Member

Principal Investigator

Her research integrates methodologies from system and control theory, analysis, and optimization to address pressing and fundamental challenges in complex and networked energy systems. She aims at driving the widespread utilization of renewable energy resources while enhancing the resiliency and efficiency of energy systems through developing rigorous theory and scalable computational tools. She is the recipient of a Best Paper Award at the MIT/Harvard Applied Energy Symposium in 2022 and was named a Barbour Scholar in 2021.

Postdocs

Dr. Mudiyanselage received his Ph.D in Electrical Engineering from the University of California, San Diego in 2025, and a Master’s degree in Computer & Systems Engineering at Rensselaer Polytechnic Institute in 2022. In 2025, he was selected as one of the prestigious ROSEI Postdoctoral Scholars and joined the PENSA Lab at Johns Hopkins University, under the supervision of Prof. Sijia Geng.

His current work focuses on developing intelligent, safe, and resilient control frameworks for future inverter-dominated power systems. By combining theoretical advances with real-world impact, he looks forward to contributing to ROSEI’s mission of accelerating the transition to sustainable energy.

Ph.D. Students

His research interests lie at the intersection of Nonlinear Analysis, Control, and Optimization theory in Modern Power Systems, where he focuses on developing theoretical tools to advance the complex world of inverter-based resources. In particular, his current research is centered on nonlinear understanding of the erratic behavior in renewable power grids, as well as their application in control and optimization theory.

Shigeng Wang is currently pursuing the Ph.D. degree in Electrical and Computer Engineering (ECE) with the Power and Energy Network System Analysis (PENSA) Lab at Johns Hopkins University. Before his Ph.D. journey, he received the B.Eng. degree in Automation from Shanghai Jiao Tong University in 2022, and the M.Eng. degree in Control Engineering from the same institution in 2025. 

His research mainly focuses on Control Theory, Machine Learning, and Networks. He aims to integrate learning and control within a unified framework that is both intelligent and trustworthy for cyber-physical systems, such as modern power systems. His long-term goal is to bridge the gap between theoretical research and applications.

Master Students

Undergrad Students