Bio
Lead AI & Software Engineer at Digital Rail, where I lead a team building deep reinforcement learning approaches to optimize railway traffic. I combine high-impact research with hands-on engineering — designing, training, and evaluating neural networks at scale, and turning research ideas into production-quality code.
- Hands-on ML engineer and researcher building machine learning and deep learning models with Python (8+ years)
- Experienced across industries (railway, cloud, telecom, manufacturing) and the ML toolbox (reinforcement learning, deep learning, trees/boosting, transformers) — with a focus on reinforcement learning and rigorous benchmarking and evaluation
- Curious researcher with 30+ peer-reviewed papers (3 awards, h-index 16) and open-source contributions used across the community
- Passionate technical leader of AI-focused teams and a computer science PhD with distinction from Paderborn University
Experiences
Leading a team of AI researchers and engineers building deep reinforcement learning approaches to optimize railway traffic. I set and drive the AI strategy and work hands-on (Python, PyTorch, Ray, MLflow). I design and train neural networks (including graph- and transformer-based architectures) and establish rigorous evaluation and benchmarking to ensure meaningful progress.
Built training and evaluation pipelines for deep reinforcement learning. Developed a scalable, high-performance simulation used as the RL training environment (PyTorch, NumPy). Coordination and prioritization between AI and simulation teams.
Designed, implemented, and trained machine learning and deep reinforcement learning models for self-learning network and service management, using diverse optimization approaches — from numerical optimization and metaheuristics to machine learning. Research on network optimization, network softwarization (NFV/SDN), cloud and edge computing, and 5G and beyond.
- Prototyping and evaluating protocols for vehicular networking.
- Teaching two undergrad classes on Java and systems software.
Education
German grades: 1.0 = excellent, 4.0 = sufficient/passing grade
Honors & Awards
For the digest of my PhD thesis at the 2023 IEEE Conference on Network Function Virtualization and Software Defined Networks (NFV-SDN)
For our paper at 2020 IEEE/IFIP International Conference on Network and Service Management (CNSM)
Accepted at Software Campus with €100K grant to lead my own 2-year research project, RealVNF.
Awarded by the computer and communication systems group, Paderborn University
Awarded by Germany’s oldest and most prestigious scholarship foundation
National scholarship awarded to less than 1% of students in Germany
Paderborn University’s computer science scholarship incl. conference grants
Award for outstanding performance in mathematics (best of my year)
Skills & Technology
Languages:
German (native), English (professional), Spanish, FinnishProgramming:
Python (proficient), Java, C++, JavaScriptMachine Learning:
PyTorch, TensorFlow, NumPy, pandas, scikit-learn, MLflowRL:
Ray RLlib, Stable Baselines, CleanRL, GymnasiumLLMs & Agents:
HuggingFace, MCP, Agent Skills & Hooks, OrchestrationCloud & Container:
Docker, Kubernetes, AWS, GCP DevOpsHobbies
- Technology & Machine Learning
- Traveling & Languages
- Hiking & Running
- Cooking & Eating