Hey, I'm Max, AI student at JKU Linz working on biologically-inspired neural architectures.
I'm interested in how intelligence emerges from simple components - cells self-organizing into adaptive networks that learn, communicate, and remember. Drawing ideas from artificial life, neuroevolution, and developmental biology, I try to find the minimal ingredients for continual learning and open-ended discovery.
Currently exploring:
- Self-organizing neural architectures with learned dynamic connectivity
- Meta-learning where shared parameters define how neurons communicate while their context determines their role
- Automated curricula optimizing task diversity and learning potential via LLM-guided exploration
On my website mwolf.dev, you'll find detailed notes and thoughts (major updates coming soon).
Below are highlighted projects and paper implementations.
Project | Description | Tools/Languages |
---|---|---|
dotfiles | Config files, utility scripts, ... | |
Obsidian Homepage | Personal-stats visualization from Obsidian daily-note metadata | |
aleator | RNG-based habit formation app. |
High School Projects
Project | Description | Tools/Languages |
---|---|---|
Sentinel-2 Landcover Classification | (Diploma Project) Landcover classification on sentinel-2 data with Prithvi, EfficientNet-Unet and OSM / CNES Landcover labels. | |
TruthTabler | Boolean expression parser, simplifier, converter. | |
2048JavaFx-Bot | 2048-Game in JavaFx, including a parallel expectimax bot which gets to 4096 most of the time. |