I'm a Chief Software Developer at Eksponent with a strong foundation in mathematics, statistics, and software engineering. My work bridges theoretical rigor with practical implementation, particularly in statistical computing, machine learning, and enterprise software development.
My passion lies in Bayesian inference and computational statistics. I maintain several repositories exploring advanced topics in probabilistic machine learning, MCMC methods, and statistical modeling. I'm particularly interested in:
- Markov Chain Monte Carlo algorithms and convergence diagnostics
- Bayesian statistical inference and probabilistic programming (PyMC)
- Machine learning theory and applications
- AI Applications: RAG systems, semantic search, and LLM integration
With ~30 years of experience in software development, I specialize in:
- Backend Development: C#/.NET ecosystem, Azure DevOps, Domain-Driven Design
- Financial Systems: Complex architecture and integration patterns
- Web Technologies: ASP.NET Core, Blazor, WebAssembly
📚 Teaching & Learning I regularly work on educational materials for applied statistics and machine learning courses at the University of Copenhagen, and I'm actively exploring modern ML techniques through implementations and coursework.
- 📝 Technical blog: carsten-j.github.io
- 💼 Location: Copenhagen, Denmark
📊 Languages & Tools
Python C#/.NET Jupyter PyMC Azure Statistical Computing Machine Learning Bayesian Methods