Clinical Registry Intelligence
Built clinical ETL and NLP systems at Carta Healthcare, normalizing heterogeneous data into HL7/FHIR-aligned workflows and integrating LLM-assisted extraction into registry operations.
Victor Camargo
Applied data scientist and AI engineer building reliable machine learning systems for real-world workflows.
I work across clinical NLP, LLM-assisted extraction, reproducible ML pipelines, and technical education - bringing scientific rigor to production systems and clear thinking to ambiguous problems.
A technical practice shaped by physics, machine learning, and teaching.
My background spans a PhD in Physics Applied to Medicine and Biology, a master's in Complex Systems Modeling, and applied work across healthcare data infrastructure and AI/ML systems.
At Carta Healthcare, I built ETL and NLP pipelines for clinical data, standardized heterogeneous sources into HL7- and FHIR-compliant formats, and integrated LLM-assisted extraction into registry workflows. At TripleTen, I mentor learners through end-to-end machine learning projects with a strong emphasis on evaluation rigor, failure analysis, and production-minded thinking.
Research
5 peer-reviewed publications across nonlinear dynamics and complex systems. View publications
Projects
Selected projects
Draft case studies drawn from real work in clinical machine learning systems and technical instruction.
Teaching ML Like Production
Built a rigorous mentoring and review practice at TripleTen, guiding learners through end-to-end ML systems with emphasis on evaluation, failure analysis, and production-minded decisions.
Writing
Recent writing
Essays on machine learning practice, systems thinking, scientific reasoning, and technical judgment.
Machine Learning Starts Before the Model
In real systems, the hardest part of machine learning is often not the algorithm. It is the work of making data coherent, reliable, and usable enough for modeling to matter.
Scientific Reasoning for Applied AI
A scientific background is useful in applied AI not because it makes the work abstract, but because it trains you to question assumptions, isolate variables, and take evidence seriously.
Contact
Get in Touch Open to thoughtful collaborations.
I take on selected consulting, applied AI, writing, and teaching work where technical depth, reliability, and clear communication all matter.