My research lies at the intersection of mathematical optimization, machine learning, power engineering and economics. The overarching goal of his work is to develop principled and trustworthy decision-making frameworks that improve how critical infrastructure is understood, planned and operated to ensure efficient and reliable performance. His research advances the theoretical and algorithmic foundations of data-driven and optimization-based decision making, with emphasis on scalability, interpretability and robustness. Applications include power and energy systems, safety-critical systems and electricity markets.
