Physics, complex systems, and applied machine learning in a single practice.

My path into machine learning began in scientific research. I studied physics at the University of The Andes, completed a master's in Complex Systems Modeling, and earned a PhD in Physics Applied to Medicine and Biology at the University of Sao Paulo.

That training still shapes how I work: define the problem carefully, make the pipeline reproducible, evaluate claims honestly, and keep the system understandable enough to trust.

Professionally, I have built ETL and NLP pipelines for clinical data at Carta Healthcare, standardizing heterogeneous sources into HL7- and FHIR-compliant formats and integrating LLM-assisted extraction into registry workflows. I also teach at TripleTen, where I mentor learners through forecasting, classification, NLP, time series, and LLM projects with an emphasis on production-minded thinking.

I am especially interested in work that sits between research depth and practical execution: machine learning systems, applied AI workflows, and technical communication that makes complex ideas genuinely usable.