[{"content":"Columbium Technologies starts operations today.\n","permalink":"https://columbium.io/posts/opening-the-lab-notebook/","summary":"\u003cp\u003eColumbium Technologies starts operations today.\u003c/p\u003e","title":"Hello World"},{"content":"Columbium Technologies LLC is a Cincinnati-based research, design, and product development company. We bridge the gap between abstract machine learning and physical reality.\nThe core thesis: Domain knowledge and statistical rigor are complements, not substitutes.\nWe reject the \u0026ldquo;black-box\u0026rdquo; application of generic ML pipelines to scientific problems. Instead, we architect models where physical constraints are structurally encoded—via specialized kernel selection, tailored likelihood functions, and conservation-law priors. The result is a system that is physically grounded and robust by design.\nCore Competencies Domain Methodology \u0026amp; Stack Surrogate Modeling Sequential and adaptive learning, Bayesian Neural Networks, Uncertainty Quantification Experimental Design Sequential/Adaptive DOE, Expected Improvement (EI), Knowledge Gradient Research Automation Multi-agent LLM systems, RAG for technical literature, automated data extraction High-Performance Computing C++20 (Template Meta-programming, SIMD), Python (JAX, PyTorch, DuckDB), cuda Production Deployment Azure ML, AWS SageMaker, GitHub Actions CI/CD, Containerization Engagement Model We focus on quantifiable outcomes. We do not deliver \u0026ldquo;pilots\u0026rdquo; or generic reports; we deliver measurable engineering results. If a project\u0026rsquo;s objective cannot be mathematically or operationally quantified at the outset, we will collaborate with you to define those metrics before work begins.\nContact For inquiries or discussions, talk to us:\nEmail: hello@columbium.io\n","permalink":"https://columbium.io/about/","summary":"\u003cp\u003eColumbium Technologies LLC is a Cincinnati-based research, design, and product development company. We bridge the gap between abstract machine learning and physical reality.\u003c/p\u003e\n\u003cp\u003eThe core thesis: \u003cstrong\u003eDomain knowledge and statistical rigor are complements, not substitutes.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe reject the \u0026ldquo;black-box\u0026rdquo; application of generic ML pipelines to scientific problems. Instead, we architect models where physical constraints are structurally encoded—via specialized kernel selection, tailored likelihood functions, and conservation-law priors. The result is a system that is physically grounded and robust by design.\u003c/p\u003e","title":"Our Philosophy"}]