Jan Schwiddessen

Measurable performance through mathematical models and algorithms. PhD candidate in technical mathematics.

Algorithms at scale:
Performance you can measure

I design and ship efficient algorithms for high-performance optimization, machine learning, and AI at large scale.

In practice, that means:

  • Making intractable problems practical
  • Measuring impact in runtime, memory, and accuracy
  • Applying approximation to tame complex, large-scale systems
  • Leveraging computational geometry, mathematical programming, and modern ML
  • Shipping efficient, robust open-source code

I’m a PhD candidate in technical mathematics at the University of Klagenfurt (Austria), focusing on the interface of optimization and machine learning. See Publications and Talks for papers and slides, and Software for focus areas and projects.