I build evaluation-driven AI systems, clinical NLP workflows, and production-minded ML pipelines with emphasis on reliability, grounding, and measurable signal over polished demos.
I bring a research-trained approach to production AI: careful problem definition, disciplined evaluation, and systems that stay understandable when the work gets messy.
At Carta Healthcare, I owned clinical ETL and NLP pipelines with HL7/FHIR-aligned workflows and selective LLM-assisted extraction. At TripleTen, I mentor and review end-to-end machine learning projects with emphasis on evaluation rigor, failure analysis, and production-minded system design.
5 peer-reviewed publications across nonlinear dynamics and complex systems. View publications
Case studies in evaluation-driven AI systems, retrieval and grounding, and clinical NLP.
Carta Healthcare · Data Scientist
Owned clinical ETL and NLP pipelines at Carta Healthcare, standardizing heterogeneous healthcare inputs into HL7/FHIR-aligned workflows and integrating LLM-assisted extraction into registry operations.
TripleTen · Data Science and AI/ML Instructor
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 technical judgment.
Essays on machine learning practice, systems thinking, scientific reasoning, and technical judgment.
In production ML systems, the hardest part is often not the algorithm. It is making data, labels, and evaluation trustworthy enough for the model to matter.
Scientific training strengthens applied AI because it builds the habits that reliable ML systems depend on: questioning assumptions, isolating variables, and taking evidence seriously.
I am especially open to AI engineering, machine learning, and research-informed product work where rigor, reliability, and communication all matter.