Teaching
Research-oriented, English-language courses. My guiding principle is to "de-blackbox" students: without a deep understanding of how software is implemented, technology risks becoming a black box. I aim to give students the methodological depth, cross-domain knowledge, and critical reflection needed to find new niches where technology creates broad societal value.
I teach research-oriented, English-language courses in evolutionary computation, machine learning, and autonomous systems. My approach emphasizes methodological depth, cross-domain reflection, and the ability to critically assess how technology is designed and deployed. Students are encouraged to move beyond surface-level understanding and engage with the underlying mechanisms of the systems they build.
Full-day workshop on large language models and generative AI for PhD and M.Sc. students, covering foundational concepts, practical applications, and critical perspectives. Responsible for the didactic design and delivery.
Workshop leader at the international summer school on machine learning, teaching applied machine learning methods to an international cohort of students.
English-taught, research-oriented course in the M.Sc. Autonomous Systems programme. Covers evolutionary algorithms, genetic algorithms, neuroevolution, and quality-diversity optimization, with an emphasis on applying these methods to real research problems.
Lab course on deploying machine learning models on low-power hardware, covering model efficiency, embedded inference, and the practical constraints of resource-limited devices.
Guest lectures introducing artificial intelligence and its applications to problems in social work, aimed at students without a technical background.
Guest lectures on quality-diversity algorithms and multi-solution optimization for students at LIACS, Leiden University.
Workshop organizer and lecturer introducing genetic algorithms to young students through hands-on, accessible activities.
Examiner and supervisor for Bachelor's and Master's theses as well as research and development projects across Departments 02, 03, and 05. Independent strength in structured student supervision, exam administration, and workshop organization.
Organizer and lecturer of a summer school on genetic algorithms, introducing students to evolutionary computation, genetic operators, and practical implementation.
Internal lecture on Gaussian process regression, given to researchers and students at the Department of Computer Science.
Master's course on evolutionary computation, consisting of genetic algorithms, neuroevolution, genetic programming and the like. Heavily application oriented.
For 10 semesters I gave courses on genetic algorithms and neuroevolution, including lectures and exercises and practical projects. Students implemented GAs, various genetic operators, and learned how to analyze results. In the neurevolution course, students learned how to evolve neural networks. Evolutionary techniques and neural networks were the two corner stones of these courses that I fully built and managed myself.
For six semesters I was a teaching assistant, leading the exercises on programming for autonomous mobile robots. Using the Robot Operating System, Python and C++, students implemented algorithms for such tasks as path finding in a simulated environment. Coming from various backgrounds, the course also offered the possibility to learn programming from scratch. A high intensity course for first semester students, including individual and group work.
Exercise that accompanied the lecture series on abstract algebra and number theory.
This tutorial was given to students that had difficulties understanding the concepts of the theoretical computer science.
Successful primary, secondary, or tertiary supervision of over 40 student theses and projects at H-BRS — frequently in close cooperation with industry and research partners. This reflects an independent strength in structured supervision, exam administration, and workshop organization.
A curated selection from the full list of supervised theses and R&D projects.
Tania Guerrero — Master's Thesis
Precise prediction of urban cold airflow around buildings. Project: OpenSKIZZE
Shashwat Pandey — Master's Thesis
On-chip multi-modal perception for industrial safety using a 3D Time-of-Flight camera on Hailo-8 NPU. Cooperation with SICK
Noah Plingen — Master's Thesis
Impact of data and feature scope on variant effect predictors for ENaCs. Project: CytoTransport
Ludovico Scarton — Master's Thesis
Surrogate modeling techniques for quality diversity optimization — voxel-based urban planning with OpenFOAM.
Maximilian Mensing — Master's Thesis
Conditional GenAI model to generate optimal urban planning designs based on QD data. Project: OpenSKIZZE
Marc Prismak — Master's Project
Whole-body vibrations on bicycles for delivery riders. Cooperation with DGUV
Vishwas Sharma — Master's Thesis
Learning simulation for plastic sheet extrusion in a blow molding machine. Cooperation with Kautex Textron
Maximilian Müller — Master's Thesis
Efficient global multiscale optimization of Lennard-Jones parameters for molecular simulations. Project: UMMBAS
Oleg Bayer — Bachelor's Thesis
Route optimization for couriers in the Arab region — TSP/VRP with genetic algorithms. Cooperation with Aramex
Jonas Meier — Bachelor's & Master's Thesis
Game design with biofeedback and SAIL (BA); automated feature engineering (MA). Cooperation with inovex GmbH
Wei-Chan Hsu — R&D Project
SAIL robot recovery: data-efficient behavioral repertoire learning for damage recovery.
Dirk Rusche — Bachelor's Thesis
Combination of neuroevolution methods with gradient descent for feed-forward networks. Awarded
Research & Collaboration
My teaching is closely tied to my research in evolutionary computation, quality-diversity optimization, and machine learning. If you are interested in collaborating or pursuing a thesis in these areas, please reach out.