Michael Engel

Michael Engel is a Researcher at the Chair of Remote Sensing Technology, Technical University of Munich since March 2021. He holds a Master's in Materials Science and Engineering (2020) and a Bachelor's in Engineering Science (2018), both from TU Munich, with a focus on Uncertainty Quantification and Bayesian Inverse Problems. His research interests revolve around optimizing objective functions in inverse problems, leveraging methods like Curriculum Learning and Multi-Task Systems via Multiresolution Analysis. He combines classical numerical techniques with machine learning to address challenges in Bayesian problems and regression.

Education:

• Bachelor's in Engineering Science, TU Munich (2018)
• Master's in Materials Science and Engineering, TU Munich (2020)

Research interests include:
- Bayesian Inverse Problems
- Machine Learning Integration in Numerical Methods
- Optimization of Multi-Task Systems
- Uncertainty Quantification in Environmental Models

Key projects include the Global Earth Monitor (GEM), aiming to enhance Copernicus data utilization for large-scale environmental monitoring. His publications span remote sensing optimization, wavelet-based sensitivity analysis, and deforestation assessment using multi-sensor data.

He contributes to the TU Munich's Engineering Risk Analysis Group and collaborates with the Chair of Numerical Mathematics and Hydrology. His work bridges physical modeling principles with computational efficiency in earth observation systems.