The Jiang-Su algebra is strongly self-absorbing revisited
Journal of Functional Analysis, 2021
A new proof that the Jiang-Su algebra is strongly self-absorbing, with applications to the classification program for nuclear C*-algebras.
Mathematician turned data scientist. I completed my PhD at the University of Münster in C*-algebras and non-commutative geometry, then moved into industry where I work on machine learning for last-mile logistics at flaschenpost SE.
I care about the intersection of rigorous mathematics and practical engineering — distributed systems, Kubernetes, and building ML pipelines that actually hold up in production.
Skills
Mathematics
- C*-algebras
- K-theory
- Non-commutative geometry
- Functional analysis
Data Science & ML
- Python
- Machine learning
- Last-mile delivery optimization
- Feature drift analysis
Engineering
- Kubernetes
- OpenShift
- Cloud (AWS/Azure)
- Linux & automation
Languages
- Python
- Julia
- Rust
- Go
Featured Projects & Publications
Algebraic K-theory and a semi-finite Fuglede-Kadison determinant
Annals of K-Theory 3-2, 2018
Defines a Fuglede-Kadison determinant for semi-finite von Neumann algebras using algebraic K-theory and studies its properties.
Feature Drift Analysis
Personal project
Interactive Marimo notebook for monitoring and visualizing feature drift in machine learning production pipelines.
