Statistical methods for spatially and temporally correlated data, machine learning, global infectious diseases, vector-borne diseases.
- Spatio-temporal models
- Statistical models for correlated observational data
- Malaria early warning systems
- Malaria elimination strategies
- Vector-borne disease research in low-income countries
- Prediction models for health outcomes
- R programming
- Machine learning
- Analysis of correlated data
- BIOS 6640: R and Python for Data Science
- PhD in Biostatistics, University of California, Berkeley, 2013
- MSPH in Biostatistics, Tulane University, 2005
- BS in Health and Exercise Science, Colorado State University, 2003
What makes you passionate about or interested in biostatistics?
I enjoy biostatistics because it allows me to contribute my statistical knowledge and skills to public health research and to learn about the epidemiology of many different important diseases. I am passionate about research that aims to reduce the incidence of global infectious diseases, especially in pediatric populations.
Personal Interests & Hobbies
Traveling, hiking, and knitting