Yannik Mahlau, M. Sc.
Leibniz Universität Hannover
Institut für Informationsverarbeitung
Appelstr. 9A
30167 Hannover
Germany
phone: +49 511 762-5319
fax: +49 511 762-5333
office location: room 1301

Yannik Mahlau studied computer science at Leibniz University Hannover. He completed his master's degree in September 2023 with the thesis "AlphaZero for Simultaneous Perfect-Information Games".
 

Research topics:

  • Reinforcement Learning
  • Game Theory
  • Graph Neural Networks
  • Inverse Design and Optical Simulations

If you are interested in any of these topics for a student position, bachelor's thesis, or master's thesis, feel free to contact me (<lastname>@tnt.uni-hannover.de)

Show selected publications only
  • Yannik Mahlau, Frederik Schubert, Lukas Berg, Bodo Rosenhahn
    FDTDX: High-Performance Open-Source FDTD Simulation with Automatic Differentiation
    Journal of Open Source Software, The Open Journal, Vol. 11, No. 117, p. 8912, January 2026
  • Frederik Schubert, Yannik Mahlau, Konrad Bethmann, Fabian Hartmann, Reinhard Caspary, Marco Munderloh, Jörn Ostermann, Bodo Rosenhahn
    Quantized Inverse Design for Photonic Integrated Circuits
    ACS Omega, January 2025
  • Yannik Mahlau, Frederik Schubert, Konrad Bethmann, Reinhard Caspary, Antonio Calà Lesina, Marco Munderloh, Jörn Ostermann, Bodo Rosenhahn
    A flexible framework for large-scale FDTD simulations: open-source inverse design for 3D nanostructures
    Preprint, January 2025
  • Konrad Bethmann, Yannik Mahlau, Frederik Schubert, Marco Munderloh, Bodo Rosenhahn, Bernhard Roth, Jörn Ostermann
    Inverse design of robust out-of-plane coupling elements
    proceedings Photonics West, January 2025
  • Yannik Mahlau, Maximilian Schier, Christoph Reinders, Frederik Schubert, Marco Bügling, Bodo Rosenhahn
    Multi-Agent Reinforcement Learning for Inverse Design in Photonic Integrated Circuits
    Reinforcement Learning Journal, Vol. 6, pp. 1794--1815, 2025
  • Yannik Mahlau, Frederik Schubert, Bodo Rosenhahn
    Mastering Zero-Shot Interactions in Cooperative and Competitive Simultaneous Games
    Proceedings of the 41st International Conference on Machine Learning (ICML), July 2024