Electrical Engineering and Computer Science
Biography
Mert Korkali is an assistant professor in the David L. Payne Department of Electrical Engineering and Computer Science at the University of Missouri (MU). Before joining MU, he was a research staff member at Lawrence Livermore National Laboratory, where he led and contributed to U.S. Department of Energy-sponsored research on power-grid operations and planning, solar-grid integration, uncertainty quantification, and extreme-event modeling. Previously, he was a postdoctoral research associate at the University of Vermont. At MU, he is the Director of the Risk-Aware and Resilient Electric Grids and Energy Networks (RAREGEN) Laboratory. His research develops computational foundations for reliable and resilient power-grid decision-making when uncertainty, rare events, and model complexity challenge conventional approaches. He combines probabilistic modeling, optimization, machine learning, and quantum computation to advance grid security, resource adequacy, and operational planning across modern electric and integrated energy systems. He currently leads MU’s participation in a project funded by the U.S. Department of Energy Office of Electricity’s Advanced Grid Modeling program to develop quantum computing methods for power flow, optimal power flow, and state estimation. He is also affiliated with the Mizzou Quantum Innovation Center (QIC), MU’s AI Education, Research and Infrastructure (AERI) Center, and MU’s Center for Rural Energy Security (CRES). Within the IEEE Power & Energy Society, he formerly chaired the Task Force on Standard Test Cases for Power System State Estimation. He serves as an associate editor for IEEE Transactions on Power Systems, IEEE Power Engineering Letters, Journal of Modern Power Systems and Clean Energy, and ACM Transactions on Probabilistic Machine Learning. He is a Senior Member of IEEE.
Education
PhD from Northeastern University
MS from Northeastern University
BS from Bahçeşehir University
Website:
Technical Focus
- Probabilistic grid intelligence
- Bayesian and nonparametric uncertainty quantification
- Multifidelity and surrogate-based grid modeling
- Rare-event simulation and tail-probability estimation
- Risk-adaptive grid decision-making
- Risk-aware optimization for secure grid operations
- Adaptive control for grid reliability and resilience
- Resilience-oriented planning under extreme events
- Quantum-enabled power-system computing
- Variational and hybrid algorithms for power-flow analysis
- Quantum optimization for optimal power flow and unit commitment
- Quantum-accelerated state estimation and security assessment
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