Maximilian Gollwitzer, M.Sc.

Background

• Started my doctoral studies with Prof. Dr. Felix Dietrich in October 2025
• M.Sc. in Computational Science Engineering (CSE) October 2024 - September 2025
• B.Sc. in Informatics October 2021 - September 2024
• From Bavaria, Germany

Research interests

In general, Scientific Machine Learning. In particular, I am interested in:

• Fast/Alternative training methods for neural networks
• Understanding Machine Learning Methods and how to incorporate prior knowledge
• Applications to Science & Engineering (especially Neuroscience/Neuroengineering)

Right now, I am working on fast training of spiking neural networks and convolutional neural networks.

For publications, see Google Scholar.

Thesis Supervision

I can supervise students from across CIT. If you are interested in doing a thesis or research project with me (or other members of the group), please apply through the Thesis Application Form of the group or the thesis portal for the listed topics. You are invited to also propose your own topic in addition to the listed ones.
Available Topics:

• None at the moment.

Running or Reserved Topics (for reference; if you're interested we can probably find something similar):

• Fast Training of Spiking Neural Networks: https://thesis.aet.cit.tum.de/topics/4a76fee9-9457-4a28-9f5b-1da605531c79
• Breaking a Neural Network Training Algorithm: https://thesis.aet.cit.tum.de/topics/462ec724-c0c0-42c5-addf-faa2d93bb42c
• Data-driven sampling for Kolmogorov-Arnold Network training without Gradient Descent: https://thesis.aet.cit.tum.de/topics/53e80dc8-a678-4116-8a15-dbbd3105df9b
• Gradient-free training of Neural Networks by combining local approximation with data-driven sampling: https://thesis.aet.cit.tum.de/topics/2af2ad6f-8577-437c-911e-87cd062c87a0

In case of any questions, feel free to also send me an e-mail.

Academic Honors

Scholarship from the State of Bavaria (Max Weber-Programm)