Sibylle Sager-Müller

Sibylle Sager-Müller is a Lecturer at the Lucerne School of Computer Science and Information Technology, part of Lucerne University of Applied Sciences and Arts (HSLU). She holds a Dipl.-Ing. in Mechanical Engineering from the University of Stuttgart and a Dr. sc. techn. from ETH Zürich, where she received the ETH Medal and ECCOMAS Prize. Her professional experience includes roles as a simulation engineer at Robert Bosch GmbH and as a Product Owner for Explainable AI projects in Powertrain Solutions. She has held visiting fellowships at Stanford University, California Institute of Technology, and NASA Ames Research Center.

• Education:
  • Dipl.-Ing. Maschinenbau, Universität Stuttgart
  • Dr. sc. techn. ETH Zürich (awarded ETH Medal & ECCOMAS Prize)
• Professional Experience:
  • Simulationsingenieurin, Robert Bosch GmbH
  • Product Owner for Explainable AI (AI in Powertrain Solutions), Bosch
  • Visiting Fellowships: Stanford, Caltech, NASA Ames

Her research focuses on data science, evolutionary algorithms, and optimization techniques, with applications in autonomous systems, fluid dynamics, and AI. Recent work emphasizes explainable AI (xAI) dashboards for computer vision and optimization of flow systems using stochastic and bio-inspired methods.

Key projects include developing AI-assisted offering processes, analyzing transport company performance via geo-localized data, and studying customer behavior in e-bike sharing systems. Her publications span over two decades, with a strong emphasis on evolutionary strategies, machine learning, and their engineering applications.

Awards: ETH Medal (ETH Zurich), ECCOMAS Prize, and recognition for contributions to computational methods in applied sciences.

Advising & Grants: Extensive experience in project leadership across Bosch and academic collaborations, though specific grant details are not detailed in the provided text.

She contributes to interdisciplinary teams in AI development and leads research on smart transportation systems and fluid dynamics optimization.