Academic background

• Bachelor of Mathematics (TUM) with a focus on Statistics, Probability Theory, and Optimization
• Bachelor's thesis on Self-Distillation in Machine Learning
• Master of Mathematics in Data Science (TUM) with a focus on Machine Learning, Deep Learning, Mathematical Statistics, and Causality
• Master's thesis on Cluster-DAGs as background knowledge for causal discovery
• Internships and working student positions in Data Science (Amazon), Research (TUM) and Finance (Noerr/KPMG)
• JADS PhD scholarship recipient at TUM and ICL for COMET (Collaborative Machine Learning for the Energy Transition)

Research interests

• Application of Machine Learning methods in energy systems
• Collaborative/Federated Machine Learning
• Reinforcement Learning
• Causal inference & causal discovery
• Forecasting
• Energy markets
• Data markets

Teaching

• Machine Learning in Energy Systems