Statistical Modeling
Turning complex questions into rigorous models, interpretable results, and defensible conclusions.
Data Scientist & Statistician
Finding the signalinside complex data.
I use statistics, machine learning, and data visualization to study consequential problems in healthcare, transportation, and the public sector.
Sampling lab
From noisy data to an estimate
95% interval
72.4 ± 5.4
Areas of focus
Analysis built for understanding.
Machine Learning
Building and evaluating predictive systems with careful attention to performance, uncertainty, and use.
Spatial & Public Data
Using GIS, transportation, healthcare, and regional data to reveal patterns that support better decisions.
Selected work
Applied data science projects.
01
Healthcare Analytics
Survival Classification in Patients
An admission-time logistic-regression study that corrects data leakage and evaluates survival classification on held-out patients.
02
Public-Sector Analytics
SANDAG Data Science Experience
Employment, mobility, GIS, and open-data products built to support regional planning across San Diego County.
03
Predictive Modeling
Prostate Capsular Involvement Prediction
A reproducible logistic-regression study of prostatic capsule penetration, comparing a 2018 benchmark with prespecified modern models.
Core toolkit
PythonRSQLMachine LearningGISData Visualization