I build optimization systems that run at scale: scheduling 30M passengers yearly, processing 500M warehouse items, optimizing factory schedules in real-time.
My work sits at the intersection of algorithm design and production engineering: taking complex decision problems (which truck goes where, which orders to pick next, how to schedule a factory) and building software that solves them fast enough to matter in the real world.
I have shipped these systems across logistics, travel, e-commerce, and maritime, from PhD research to production deployments handling millions of decisions daily. The projects below tell that story.
Projects
Here's where I've applied optimization to solve real-world problems at scale.
juna.ai
AI Agents for Industrial Scheduling
Building AI-powered scheduling solutions for manufacturing and logistics.
Auerbach
Maritime Data & Voyage Optimization
Built a centralized data platform and optimization models for shipping operations.
Zalando
Warehouse Order Batching
Developed order batching algorithms reducing picking costs by ~5% across 500M items yearly.
Mobi Systems
Real-time Bus Scheduling
Built a fleet optimization platform from scratch, generating trips for 30M+ passengers yearly.
Sixt
Vehicle Defleet Optimization
Research project combining ML and optimization to improve vehicle transport logistics.
University of Göttingen
PhD: Public Transport & Robust Optimization
Doctoral research at Göttingen on integrated public transport planning and optimization under uncertainty.
Side Project
Algobench
A framework to automatically generate and evaluate heuristics for optimization problems.