Teaching
I teach rigorous quantitative courses that focus on showing students how to derive and implement foundational methods in statistical and mathematical modeling.
Courses
Biostatistics
Provides an overview of key statistical concepts and approaches for analyzing biological data. The lectures focus on the mathematical foundations of statistics and the labs demonstrate how to apply each method to real data using the free and open-source R programming environment. Topics include probability, probability distributions, experimental design, as well as statistical inference via point or interval estimation and hypothesis testing. Classic parametric and non-parametric statistical tests for one or more population(s) are discussed. Special emphasis is placed on the appropriateness of each test based on its assumptions, statistical power, and the structure of the data.
Undergraduate courseAdvanced Biostatistics
Provides an overview of advanced statistical concepts and methods for studying biological systems. The lectures explain the mathematical underpinnings of the statistical methods and the in-class tutorials demonstrate how to implement them in the R programming language. Topics include general(ized) linear models, likelihood, model selection, mixed-effects models, multivariate statistics, and machine learning.
Undergraduate and graduate sectionsPopulation Dynamics
Provides a comprehensive overview of the mathematical and computational concepts needed to construct dynamical models. The lectures describe how to mathematically model the effects of species interactions, space, disease, and environmental variability in order to understand the dynamics of populations in a changing world. Special emphasis is placed on the mathematical tools required to analyze the dynamical behavior of models and validate their predictions using empirical data. The tutorials teach students how to implement, analyze, and test models using the free R programming language.
Undergraduate and graduate sections