Biography

Greetings! I’m a PhD student delving into scientific machine learning at the University of Stuttgart. My academic path led me to the captivating world of engineering cybernetics, where I honed my skills in mathematics, mechanics, and control theory, and formed a deep interest in systems theory and understanding complex models.

In my research, I’m driven to efficiently and cost-effectively approximate complex models by blending core numerical methods with artificial intelligence learning techniques. I’m passionate about the synergy between scientific principles and cutting-edge technologies.

Interests
  • Scientific Machine Learning
  • Model Order Reduction
  • Surrogate Modeling
Education
  • PhD in Simulation Technology, in progress

    University of Stuttgart

  • MSc in Engineering Cybernetics, 2020

    University of Stuttgart

  • BSc in Engineering Cybernetics, 2017

    University of Stuttgart

Experience

 
 
 
 
 
Department of Civil and Environmental Engineering
Visiting Researcher
September 2023 – September 2023 Polytechnic University of Milan, Milan, Italy
Development of a reduced-order modeling with uncertainty quantification framework using generative AI algorithms.
 
 
 
 
 
Artificial Intelligence Institute in Dynamic Systems
Research Intern
August 2022 – November 2022 University of Washington, Seattle (US)
Development of a multi-hierarchic surrogate modeling approach using graph convolutional neural networks and mesh simplification.
 
 
 
 
 
Robert Bosch GmbH
Working Student
May 2019 – April 2020 Stuttgart, Germany
Process automation in project planning within the area of autonomous driving.
 
 
 
 
 
Daimler AG
Industrial Internship
September 2018 – April 2019 Sindelfingen, Germany
Scenario generation and tool development for the simulative validation of autonomous driving systems.

Publications

(2024). Data-driven identification of latent port-Hamiltonian systems.

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(2024). On using machine learning algorithms for motorcycle collision detection. Springer Science and Business Media LLC.

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(2024). VENI, VINDy, VICI: a variational reduced-order modeling framework with uncertainty quantification. arXiv.

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(2024). Multi-hierarchical surrogate learning for explicit structural dynamical systems using graph convolutional neural networks. Computational Mechanics.

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(2023). Low-dimensional data-based surrogate model of a continuum-mechanical musculoskeletal system based on non-intrusive model order reduction. Archive of Applied Mechanics.

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