Marc Huber, MSc

MSc (Mathematics)

Research area in the program
Hybrid Methods for Combinatorial Optimization

Keywords

• Combinatorial Optimization
• Machine Learning
• Hybrid Optimization

Research Interests

The research focus of Marc Huber lies in combining concepts of exact optimization techniques, Machine Learning, and Metaheuristics. More specifically, he aims to utilize techniques from Machine Learning, in particular Reinforcement Learning, to guide classical optimization algorithms.

Scientific CV

Education
Since 10/2020: PhD student, Vienna Graduate School On Computational Optimization, TU Wien, Austria.
10/2017 – 03/2020: MSc Mathematics, Regensburg University of Applied Sciences, Germany.
10/2013 – 09/2017: BSc Mathematics, Regensburg University of Applied Sciences, Germany.

Work Experience
06/2019 – 12/2019: MHP - A Porsche Company: Automatization of a Machine Learning Pipeline using Reinforcement Learning and Bayesian Optimization, Germany.
03/2017 – 08/2017: BMW Group: Identifying causes of defect components using methods of Data Mining, Germany.
02/2016 – 06/2016: University of Regensburg: Comparison of signal analysis with Wavelets compared to empirical mode decomposition methods, Germany.

Teaching Experience
03/2016 – 07/2016: Regensburg University of Applied Sciences, Tutor: Mathematics 2 for Medical Informatics, Germany.
10/2014 – 02/2016: Regensburg University of Applied Sciences, Tutor: Matlab for Microsystems Engineering, Germany.