PNNL

Rounak Meyur is a data scientist with the Data Science and Machine Intelligence group at Pacific Northwest National Laboratory (PNNL). His research interests lie at the intersection of optimization, control, and learning applied to complex networked systems. In particular, his expertise lies in the area of mathematical programming, convex optimization, optimal control, heuristics and approximation algorithms. He applies these techniques to solve problems that arise in enhancing cyber-physical security, creating synthetic networked infrastructure, decarbonization of energy infrastructure systems, and improving resiliency of infrastructure systems. He was a research assistant at the Biocomplexity Institute & Initiative of the University of Virginia between 2020 and 2022. He was involved in developing frameworks for the realistic modeling and analysis of power grids. He has previously interned at PNNL and PJM Interconnection. 

• Combinatorial Optimization
• Distributed Energy Resources
• Graph Theory
• Network Science
• Power Systems and Electrical Engineering
• Python

PhD, Electrical Engineering, University of Virginia

MS, Electrical Engineering, Virginia Polytechnic Institute & State University

BS, Electrical & Electronic Engineering, National Institute of Technology, Warangal, India

2025

• Sadnan R., R. Meyur, D.M. Glover, A.P. Reiman, and J.D. Follum. 2025. "Parameter Estimation of Unreported PVs using Distribution System State Estimation Algorithm." In IEEE Green Technologies Conference (GreenTech 2025), March 26-28, 2025, Wichita, KS, 1-5. Piscataway, New Jersey:IEEE. PNNL-SA-201913. doi:10.1109/GreenTech62170.2025.10977689

2024

• Mitra B., M. Ramesh, R. Meyur, R. Sadnan, T. Slay, A.P. Reiman, and J.D. Follum. 2024. Survey of Use Cases and Scenarios on the Open Energy Data Initiative Solar Systems Integration (OEDI SI) Platform. PNNL-35875. Richland, WA: Pacific Northwest National Laboratory. Survey of Use Cases and Scenarios on the Open Energy Data Initiative Solar Systems Integration (OEDI SI) Platform
• Purohit S., R. Meyur, O.M. Bel, A. Mendoza Sanchez, B.K. Webb, and S.A. Donald. 2024. Efficient Hybrid Attack Graph Generation for Cyber-Physical System Resilience Experimentation: Final Project Report. PNNL-36847. Richland, WA: Pacific Northwest National Laboratory. Efficient Hybrid Attack Graph Generation for Cyber-Physical System Resilience Experimentation: Final Project Report
• Wang D., B. Mitra, S. Nekkalapu, S. Datta, B. Mathew, R. Meyur, and H. Wang, et al. 2024. "Hy-DAT: A Tool to Address Hydropower Modeling Gaps Using Interdependency, Efficiency Curves, and Unit Dispatch Models." In IEEE Green Technologies Conference (GreenTech 2024), April 3-5, 2024, Springdale, AR, 91-95. Piscataway, New Jersey:IEEE. PNNL-SA-183732. Hy-DAT: A Tool to Address Hydropower Modeling Gaps Using Interdependency, Efficiency Curves, and Unit Dispatch Models">doi:10.1109/GreenTech58819.2024.10520554Hy-DAT: A Tool to Address Hydropower Modeling Gaps Using Interdependency, Efficiency Curves, and Unit Dispatch Models

2023

• Donald S., R. Meyur, and S. Purohit. 2023. "Hybrid Attack Graph Generation with Graph Convolutional Deep-Q Learning." In IEEE International Conference on Big Data (BigData 2023), December 15-18, 2023, Sorrento, Italy, 3127-3133. Piscataway, New Jersey:IEEE. PNNL-SA-191137. doi:10.1109/BigData59044.2023.10386675