M.Sc. Alexandre Capone
Technical University of Munich
Munich School of Engineering (MSE)
- Phone: +49 (89) 289 - 25729, 52770
Since October 2017: Research Assistant and PhD candidate at Munich School of Engineering (MSE) and Institute for Information-Oriented Control (ITR), Technical University of Munich
-> Supervisor: Prof. Sandra Hirche, Technical University of Munich
October 2016: M.Sc. in Mechanical Engineering (cum laude), RWTH Aachen, Germany
-> Thesis title: Design and Optimization of a Three-Phase Reactive Batch Distillation Column as an Underdetermined System of Differential-Algebraic Equations with Optimization Criteria
-> Supervisor: Prof. Alexander Mitsos
July 2014: B.Eng. in Mechanical Engineering RWTH Aachen, Germany
-> Thesis title: Modelling and Feedback Control of a 6-Phase Drive System
-> Supervisor: Prof. Dirk Abel, RWTH Aachen
- Energy Systems Modelling and Optimization
- Smart Grids
- Reinforcement Learning
- Gaussian Processes
- My current research focuses on Gaussian Processes in Control-oriented settings. I am also interested in applying machine learning methods to energy systems.
- I am currently involved in the MEMAP Project, a collaboration between MSE, ITR and multiple firms with ties to the energy sector. My goal within the project is to apply machine learning to district energy systems.
- MA: Machine Learning for Energy Systems [PDF].
- MA: Learning and Control of Nonlinear Systems [PDF].
- MA: Gaussian Process based MPC for Autonomous Vehicles [PDF].
- MA: Safety verification for neural networks [PDF]
Please feel free to contact me if any of the topics above interest you for a Bachelor's or Master's thesis, or if you have a topic of your own.
Motivated students are encouraged to contact me via E-mail if interested in doing their Bachelor's or Master's thesis under my supervision. Please include your CV and credentials with your application.
- Localized active learning of Gaussian process state space models. Learning for Dynamics & Control, 2020 more… BibTeX
- Data Selection for Multi-Task Learning Under Dynamic Constraints. IEEE Control Systems Letters 5 (3), 2020, 959-964 more… BibTeX
- Localized Active Learning of Gaussian Process State Space Models. 2020 more… BibTeX
- Parameter Optimization for Learning-based Control of Control-Affine Systems. Learning for Dynamics & Control, 2020 more… BibTeX
- How Training Data Impacts Performance in Learning-based Control. IEEE Control Systems Letters, 2020, 1-1 more… BibTeX
- Smart Forgetting for Safe Online Learning with Gaussian Processes. Learning for Dynamics & Control, 2020 more… BibTeX