About

I am an Associate Director of Data Science building production machine learning systems for pharmaceutical manufacturing that combine rigorous GxP compliance with cutting-edge ML techniques. My journey from theoretical physics to industrial ML gives me a unique perspective on solving complex problems at the intersection of fundamental science and practical engineering.

I hold a PhD in Theoretical Physics from the Max Planck Institute for Dynamics and Self-Organization, where I specialized in stochastic processes and non-equilibrium statistical mechanics. After completing a Postdoctoral position followed by a Researcher position at NORDITA (Nordic Institute for Theoretical Physics) in Applied Mathematics, I transitioned to industry, bringing deep mathematical foundations to real-world ML challenges.

Current Focus

• Production ML Systems: Building GxP-compliant models for pharmaceutical manufacturing (autoencoders, VAEs, RL, GenAI, predictive maintenance)
• From-Scratch Implementations: Building deep learning architectures without pre-trained models to deeply understand fundamentals (Transformers, attention mechanisms, custom training pipelines)
• Physics-Informed ML: Applying stochastic process theory and statistical physics to machine learning problems
• Competitive ML: Kaggle Expert
• Open Source Research Tools: Scientific literature RAG systems, GPU-accelerated implementations, production-grade ML pipelines

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Connect

• GitHub
• LinkedIn
• Kaggle
• Google Scholar