I completed my bachelor's degree in Mechanical Engineering at the Technical University of Munich (TUM) and continued my academic journey by pursuing a master's degree in Mechatronics and Robotics, also at TUM. During this period, I broadened my horizons by embarking on a semester abroad in Svalbard, where I studied Arctic Engineering at the University Centre of Svalbard. Thereafter, I decided to extend my stay in Norway. I had the privilege of finalizing my master's degree while collaborating with the Norwegian University of Science and Technology (NTNU) in conjunction with my home institution.
Within the realm of Mechatronics and Robotics, I developed a particular interest in Applied Machine Learning for technical processes. This interest led me to undertake a compelling term project at TUM, focusing on the Automated and Optimized Parameterization of 3D Measurements for Robot-Based Optical Measurement Systems based on Gaussian Processes. This project was integral to the quality assurance process in the automotive industry. Building on this foundation, I culminated my master's degree by conducting in-depth research on Predictive Maintenance within the context of a salmon farm. I officially graduated in July 2023.
Currently, I am part of the SUBPRO-Zero project at NTNU as PhD candidate. My research area is Incorporating AI in safety-critical systems.
My research endeavors are focused on the examination of opportunities and challenges associated with the integration of artificial intelligence (AI) in safety-critical systems. This comprehensive research initiative encompasses several key objectives:
Examination of the Current Landscape: An analysis of existing safety-critical systems will be conducted to gain a comprehensive understanding of their operational intricacies, paving the way for the identification of potential areas for AI-driven improvements.
Assessment of AI Limitations: My research is dedicated to a thorough examination of the limitations and uncertainties surrounding AI (e.g. degradation of AI performance after implementation, explainability of AI, and regulations) in the context of adhering to functional safety standards. This critical investigation is essential for ensuring the dependability and adherence to rigorous safety protocols.
Development and Testing of AI Models: The research will involve the design, development, and rigorous testing of AI models within safety-critical systems. These models will be subjected to meticulous scrutiny to ensure they meet the stringent requirements of safety-critical applications.
Impacts and Benefits Assessment: In addition to the technical aspects, the research will explore the broader implications of AI integration in safety-critical systems. This encompassing assessment includes the evaluation of potential consequences and advantages associated with the incorporation of AI in various industries, environments, and societal contexts.