Past, Present, and Future Teaching

Graduate-level course on regression procedures, models, and applications. Generalized linear models, regularization, model building, feature selection, dimensionality reduction, shrinkage, non-linear methods and basis expansions, Bayesian regression, logistic regression and other classifier algorithms, other facets of supervised machine learning for regression problems.

Graduate-level course on design and analyses of experimental data. Experimental design, linear models, ANOVA, ANCOVA, hypothesis testing, model adequacy and diagnostics, balanced designs, inbalanced designs, factorial designs, confounded designs, blocks, variance components, generalized linear models, linear mixed models, and more

Learn hands-on computational genomics using beetles!

Learn an array of skills for handling “Big Data” in biology and beyond! Data wrangling, visualization, hypothesis testing, inference, predictive modeling, supervised and unsupervised learning introductions

Introductory course to principles of biology (Fall 2022)

Graduate-level course on design of experiments. Variable and data-types, hypothesis testing, grad-level introductory probability and statistics, distributions, categorical data analyses, introduction to regression, experimental designs, F-tests, and other hypotheses tests