Teaching
Teaching
Statistical training that connects theory, computation, and scientific application.
My teaching emphasizes the connection between statistical theory and the computational tools needed to apply that theory to modern scientific problems. Across methodological and applied courses, I aim to develop both statistical understanding and the ability to translate that understanding into reproducible analysis.
Courses
GRADUATE
Advanced Statistical Computing
Computational methods and tools for modern statistical research, with an emphasis on implementation, reproducibility, and efficient statistical programming.
GRADUATE
Spatial & Spatiotemporal Modeling with Applications
Statistical modeling for spatially and temporally structured data, connecting Gaussian-process methodology with practical analysis and computation.
GRADUATE
Generalized Linear Models
Theory and application of generalized linear models, with emphasis on statistical inference, model formulation, diagnostics, and implementation.
GRADUATE
Statistical Inference I
Foundations of statistical inference, including likelihood-based methods, estimation, hypothesis testing, and theoretical properties of statistical procedures.
Workshops & Specialized Instruction
Spatial & Spatiotemporal Modeling with Applications in R
Urban Health Collaborative Summer Workshop
An applied introduction to spatial and spatiotemporal data analysis using R, designed to connect statistical methodology with substantive problems in public health and related fields.
Statistical Inference I & II — Ph.D. Qualifying Examination Preparation
A twelve-week review covering core material in statistical inference and preparation for the doctoral qualifying examination.
Mentoring
Mentoring is an important part of my teaching. I work with graduate students through doctoral advising, master’s research, independent studies, and collaborative methodological projects.
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Doctoral Students
Current doctoral students co-advised in Biostatistics.
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Master’s Students
Master’s research and thesis supervision in Biostatistics.
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Award-Winning Thesis
Master’s thesis on spatial and temporal trends in air pollution, recipient of the Dornsife School of Public Health Best Thesis Award.
Teaching Materials
Selected course materials, computational examples, and workshops can be made available here.