Research Notes: Computational Social Science and Health Data

Computational social science gives researchers a way to study social patterns using data, code, and theory together. For health research, this is especially useful because access, inequality, risk, and behavior are often shaped by social conditions.

My research direction

My work sits between social research, machine learning, and health analytics. I am interested in how computational tools can help answer practical questions about healthcare access, public health inequality, document intelligence, and policy implementation.

Why this matters

Health data is not just technical. It reflects institutions, social structures, and lived experience. That is why computational methods should be combined with careful research design and domain knowledge.

Tools I use

I often work with Python, R, survey data, text data, machine learning models, and reproducible reporting workflows. The goal is not only to build models, but to produce evidence that can be interpreted and used.

Ongoing focus

I am continuing to build projects around healthcare analytics, DHS survey analysis, NLP systems, and applied computational research.