About Me
I’m a data scientist passionate about using machine learning and analytics to improve health outcomes. Most of my work focuses on public health and healthcare, but I’m excited by any project where data can drive meaningful change.
Recent highlights:
Graph neural networks for financial fraud detection (Columbia capstone) (Github) (Page)
Modeled cost-effectiveness of pediatric obesity intervention (Publication) (Page)
New project: Federated learning + EHR data to predict gastric cancer risk
Tech stack:
Python, R, SQL, SAS
Causal inference, GNNs, PyTorch, simulation modeling, LLMs
Geospatial analysis (ArcGIS)
