A Glimpse Into Who I Am

I am a highly motivated Machine Learning Scientist specializing in Neural Network Optimization across both Large Language Models and Computer Vision. From foundation models like SAM, DINO, and DepthAnything to LLMs, I focus on making AI more efficient through model sparsification, quantization, and knowledge distillation for deployment on constrained devices.

Quick Facts

• 📍 Based in Munich, Germany
• 🎓 MSc in Robotics, Cognition, and Intelligence — TUM
• 🌟 Published research at NeurIPS & AABI on neural network optimization
• 🔧 Foundation Models, Computer Vision, LLM Optimization, Bayesian Inference, Edge AI

What Drives Me

My passion is making complex AI models more efficient and accessible — whether that's compressing vision foundation models for real-time edge inference, optimizing LLMs for on-device deployment, or developing Bayesian approaches to neural network pruning. I thrive on bridging the gap between state-of-the-art research and practical, resource-constrained deployment.

Experience Highlights

• Carl Zeiss — ML Research Scientist
• CARIAD (Volkswagen) — Visual Perception Research
• ETH Zurich / Helmholtz AI — NeurIPS Publication
• BMW — Edge Device Optimization
• Huawei — Robot Fleet Optimization with RL & LLMs
• Intel — Sparse & Quantized Networks
• Infineon — AI Pipeline for Microchip Testing

Certifications