AI/ML Engineer · PhD in Physics
I develop machine learning and AI systems, combining scientific reasoning, rigorous evaluation, and practical software engineering.
Clinical data workflows and an independent retrieval evaluation build: two contexts for practical engineering and careful evidence.
Carta Healthcare · Data Scientist
Built clinical ETL and NLP pipelines at Carta Healthcare and applied LLM-assisted extraction to registry workflows. A retrospective on data preparation, workflow boundaries, and evaluation considerations.
Independent Build · AI Systems Builder
Built an independent RAG evaluation prototype with 12 synthetic-adversarial documents, 22 labeled queries, and three lexical retrieval baselines, separating retrieval scoring from deterministic answer evaluation.
My applied industry work overlapped doctoral research in physics. That combination informs how I build: define the problem carefully, make experiments reproducible, and keep evaluation separate from assumptions.
At TripleTen, my work included technical mentorship, data-science project review, and actionable feedback. Explaining modeling choices and evaluating other people’s work are an important part of my engineering practice.
Technical mentorship at TripleTen5 selected peer-reviewed publications across nonlinear dynamics and complex systems. View publications
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.
For AI/ML engineering, applied data science, and applied research opportunities where evaluation, reliability, and communication matter.