I work with models for sequential data using machine learning/AI and simulation. The goal is to identify patterns and trends in large datasets that are relevant to the energy system—for example, those that indicate the potential for various energy system measures.
My doctoral dissertation focused on how simulation and optimization can be used to describe the profitability—in terms of total energy system cost—of collaboration among actors in the electricity market. One basis for cost-effective collaboration is the marginal cost of electricity. These costs can be obtained through so-called shadow prices derived from the optimization of the electricity system.
- I became an associate professor in energy systems in 2018.
- I teach and mentor students, and I also serve as a co-advisor to a doctoral student.
- A selection of my publications can be found at the link to Publications and on my ORCID iD.
