I'm an Energy Engineer with a growing focus on mathematical optimization and quantitative decision-making. My work sits at the intersection of stochastic and mixed-integer programming, business analytics, and power systems — using math to turn messy operational problems into decisions someone can actually act on.
- Mathematical Optimization & MILP: Building mixed-integer and stochastic optimization models in Python (PuLP) and Gurobi, from power system operation to broader industrial decision-support problems.
- Decision-Making under Uncertainty: Stochastic programming, robust optimization, and approximate dynamic programming, applied across energy, markets, and logistics.
- Prescriptive & Business Analytics: Turning descriptive/predictive models into optimization-backed recommendations — most recently a decision-support system for industrial energy procurement and tail-risk hedging.
- Power Systems & Electricity Markets: Grid stability, inverter-based resources (Grid-Forming & Grid-Following), and utility-scale solar & battery-storage integration.
Part of an MSc in Digital Energy Systems at DTU, currently deepening the quantitative side:
- Optimization in Modern Power Systems (convex optimization, duality theory, Gurobi)
- Decision Support and Strategic Assessment
- Machine Learning for Energy Systems & Machine Learning Operations
- Renewables in Electricity Markets (Spring 2027)
- Optimization: MILP · Gurobi · PuLP · stochastic & robust optimization
- Data & ML: Python · Pandas · Machine Learning
- Domain: Power systems · electricity markets · solar & battery-storage integration