M.Sc.
Michael
Eichelbeck
Technical University of Munich
Informatics 6 - Associate Professorship of Cyber Physical Systems (Prof. Althoff)
Postal address
Boltzmannstr. 3
85748 Garching b. München
Place of employment
Informatics 6 - Associate Professorship of Cyber Physical Systems (Prof. Althoff)
Boltzmannstr. 3(5607)/III
85748 Garching b. München
Curriculum Vitae
Michael Eichelbeck joined the Cyber-Physical Systems Group as a PhD candidate under the supervision of Prof. Dr.-Ing. Matthias Althoff in October 2021. Previously, he studied control systems at Imperial College London and received his Master’s degree with a thesis on non-cooperative decentralized optimization.
His current research revolves around safe control for power systems by merging reinforcement learning with formal validation. He is a member of the DFG-funded project “Safe-Guarding Artificial Intelligence in Power Systems (SAFARI)“.
Past/Ongoing Student Projects
Interdisciplinary Project (IDP)
- Economic dispatch with DC power flow constraints using safe reinforcement learning (co-supervised with Hannah Markgraf)
- Machine learning with safety guarantees - Formal conformance checking for prediction models
Bachelor Theses
- Solving optimal power flow with graph neural networks
- Observation Space Reduction for Single-Agent Controlled Microgrid Power Systems
Master Theses
- Solving optimal power flow with reinforcement learning
- Solving optimal power flow with heterogeneous graph neural networks
- Data-Driven Runtime Estimation and Defect Recognition of Batteries in Intelligent Shopping Carts
- Reinforcement Learning for Intra-Day Energy Trading with Renewable Energy Assets
- Predicting Building Types and Functions at Transnational Scale
- Costumer feedback loop for AI-triggered home emergency calls
- Wind taxonomy from sensor data using time series classification
- Deep-learning-based EV charging behavior prediction
- Efficient and Robust Time Series Forecasting with Transformer Models: A Novel B-Spline Embedding Approach
- Transformer-Based Deep Learning for Inactivity Detection in Residential NILM Data
Teaching
Practical Course – Machine Learning for Power Systems (co-organized with Hannah Markgraf)
- WiSe 24/25 - Online benchmarking platform for CommonPower
- SoSe 24 - Reinforcement learning for heat pump control
- SoSe 24 - Smart grid control benchmark scenario designer
- WiSe 23/24 - Forecasting of residential load profiles
- SoSe 23 - Forecasting of wind power generation
Practical Course – Verification, Controller Synthesis, and Design of Cyber-Physical Systems
- WiSe 22/23 - Verification of graph neural networks (co-supervised with Tobias Ladner)
Seminar – Cyber-Physical Systems
- WiSe 22/23 - Forecasting of renewable energy generation and power demand (co-supervised with Hannah Markgraf)
- WiSe 22/23 - Solving optimal power flow with machine learning (co-supervised with Hannah Markgraf)
