CV

Education

• PhD in Computational Electromagnetic since 2023
• M.Sc. in Computer Science, Leibniz University Hannover, 2023
• B.Sc. in Computer Science, Leibniz University Hannover, 2021
• Highschool Graduation, Gymnasium Soltau, 2017

Honors & Awards

• Best Graduate in the Academic Year 2022/2023, M.Sc. Computer Science
• Outstanding Paper Honorable Mention in Reproducibility Challenge 2022
• Best Graduate in the Academic Year 2020/2021, B.Sc. Computer Science
• Exceptional Highschool Graduation in Physics and Chemistry

Work experience

• 2022 April - June: AI Research Scientist Intern at dSPACE
  • Duties: Research on Post-Hoc Interpretability of Image Classification Neural Networks for Autonomous Driving
  • Full-Time Internship
• 2018 - 2023: Teaching Assistant at Institutes of Leibniz University Hannover
  • Duties: Student Supervision, Teaching in Labs
  • Part-Time
• 2017 June - September: Communications Executive at Heidepark Soltau
  • Duties: Customer Service
  • Full-Time

Publications

Multi-Agent Reinforcement Learning for Inverse Design in Photonic Integrated Circuits

Mahlau, Y., Schier, M., Reinders, C., Schubert, F., Bügling, M., & Rosenhahn, B. (2025). Multi-Agent Reinforcement Learning for Inverse Design in Photonic Integrated Circuits. arXiv preprint arXiv:2506.18627.

A flexible framework for large-scale FDTD simulations: open-source inverse design for 3D nanostructures

Mahlau, Y., Schubert, F., Bethmann, K., Caspary, R., Lesina, A. C., Munderloh, M., ... & Rosenhahn, B. (2025, March). A flexible framework for large-scale FDTD simulations: open-source inverse design for 3D nanostructures. In Photonic and Phononic Properties of Engineered Nanostructures XV (Vol. 13377, pp. 40-52). SPIE.

Quantized Inverse Design for Photonic Integrated Circuits

Schubert, F., Mahlau, Y., Bethmann, K., Hartmann, F., Caspary, R., Munderloh, M., ... & Rosenhahn, B. (2025). Quantized inverse design for photonic integrated circuits. ACS omega, 10(5), 5080-5086

Mastering zero-shot interactions in cooperative and competitive simultaneous games

Mahlau, Y., Schubert, F. & Rosenhahn, B. (2024, July). Mastering zero-shot interactions in cooperative and competitive simultaneous games. In Proceedings of 41st International Conference on Machine Learning (ICML).

[Re] On Explainability of Graph Neural Networks via Subgraph Explorations

Mahlau, Y., Berg, L., & Kayser, L. (2023, July). [Re] On Explainability of Graph Neural Networks via Subgraph Explorations. In ML Reproducibility Challenge 2022.