LightRiver — Master Thesis
- Ported an Online Machine Learning model from Python to Rust, achieving a 97% reduction in execution time and attaining a processing capability of 10,000 records per second for IoT devices.
- Optimized memory usage to a minimal 20MB, facilitating deployment on robots with stringent memory limits and resulting in a 55% enhancement in positioning accuracy.
- Specialized implementation of Mondrian Forests, streamlining the model for real-time learning in performance-sensitive applications.
Thesis — Paper
📜 Twente Publication
📜 Aalto Publication (mirror)
📜 Download PDF (mirror)