I study how behavioral realism shapes energy and climate outcomes in global transportation systems. My work bridges autonomous vehicle adoption, travel demand modeling, and energy policy, exploring how the future of mobility will reshape global emissions.
Evaluating smart charging flexibility at scale, using machine learning counterfactuals to quantify real-world DR program effectiveness.
Scenario analysis of global transportation energy demand and emissions under reference and net-zero pathways using an IAM (GCAM).
Evaluated demand response program effectiveness across 558 Korean EV charging stations using a CatBoost-based counterfactual modeling approach. Automatic DR controls achieved 11.8% load reductions versus 0.4% for manual adjustments, revealing where and when behavioral and infrastructure factors determine program success.
Open to research collaborations, conference discussions, and questions about transportation energy modeling and autonomous mobility.