Joe Severino

Joe Severino serves as a Researcher III in Computational Science at the National Renewable Energy Laboratory (NREL), focusing on machine learning applications for transportation and renewable energy systems. His work addresses real-time optimization challenges in traffic coordination, power grid management, and sustainable energy integration through advanced computational methods.

His research spans Machine Learning, Transportation Systems, Renewable Energy, Traffic Signal Control, Predictive Modeling, and Road Networks. He develops solutions for traffic state estimation using probe vehicle data, fairness-aware load shedding in power grids, and explainable AI for energy research, frequently employing ensemble methods and adaptive control strategies to enhance model accuracy and equity.

Analysis of his 2024-2025 publications reveals a strong trend toward real-time machine learning solutions for critical infrastructure. Key themes include network-wide traffic signal coordination using adaptive control frameworks, fairness optimization in power grid operations, and interpretable AI models for renewable energy systems. His work consistently bridges theoretical machine learning with practical engineering constraints.

As part of NREL's Computational Science group, Severino collaborates extensively with interdisciplinary teams to advance computational approaches for sustainable transportation and energy resilience.