🔬 My Research
Design of sustainable urban energy systems
I develop optimization models for energy system design that address challenges across multiple scales—from urban-level integrated planning to national capacity expansion. My work tackles both energy supply and demand dimensions, while incorporating critical factors like building energy efficiency and sector coupling between buildings and mobility. A flagship outcome of this research is the MANGO suite of models, which offers robust, strategic frameworks to help decision-makers navigate complex energy planning challenges and drive resilient, sustainable transitions.
Building energy efficiency & decarbonization
Working at multiple scales, I tackle the challenge of decarbonizing the building sector, which is a major contributor to energy consumption and emissions. I develop approaches that range from detailed building simulations assessing retrofit strategies to large-scale building stock analyses supporting policy development. By integrating techno-economic modeling with data-driven methods, I quantify the impact of energy efficiency measures and low-carbon technologies to inform both technical regulations and policy frameworks.
Co-design of policy mixes and energy systems for decarbonization
The successful transition to sustainable energy systems requires coordinated policy and technology development. I focus on developing methods for the systematic co-design of policy mixes and energy systems—an approach that recognizes their inherent interdependence and the limitations of treating them in isolation. While traditional approaches separate policy design from technical implementation, my work bridges this gap through integrated frameworks that capture the complex interactions between policy mechanisms and energy system evolution.
Decision-making under uncertainty
Designing resilient energy systems requires careful consideration of uncertainties in supply, demand, and operating conditions. Through the integration of applied statistics and global sensitivity analysis (GSA), I identify the key sources of uncertainty affecting energy system performance. Building on these insights, I apply techniques for optimization under uncertainty, such as stochastic programming, to design systems that maintain effectiveness under diverse and uncertain future conditions.
Machine learning & data-driven modeling
By harnessing machine learning techniques, I accelerate complex energy system analyses and enhance decision support. My approaches combine traditional optimization methods with data-driven techniques to overcome computational barriers in energy planning. Through efficient surrogate models, this work enables rapid exploration of design alternatives and uncertainty analysis that would be prohibitive with conventional methods alone, making sophisticated optimization more accessible to practitioners.