Learning Guide

Start with programming, probability, linear algebra, and regression, then explore specialized topics as your project develops. We can choose readings and practice projects together based on your background and interests.

Statistics and Regression

• Linear and generalized linear models; interpretation and diagnostics.
• Regularization, model selection, and cross-validation.
• Mixed models and variance components.
• Statistical inference, uncertainty, and multiple testing.

Probability and Asymptotics

• Distributions, conditional expectation, variance, and covariance.
• Laws of large numbers, central limit theorem, delta method, and modes of convergence.

Linear Algebra and Optimization

• Matrix operations, projections, and positive definiteness.
• Eigendecomposition, singular value decomposition, and principal component analysis.
• Gradients, convex optimization, and penalized objectives.

Statistical Genetics and Genomics

• Genetic variation, linkage disequilibrium, and population structure.
• Genome-wide association studies, polygenic scores, heritability, and genetic correlation.
• Gene expression, eQTLs, single-cell data, and multi-omics integration.
• Confounding, batch effects, and biological interpretation.

Computing

• R or Python, command-line tools, and large datasets.
• Reproducible workflows, Git, and documentation.
• Simulations, numerical experiments, and checking results.

See prospective student guidance for information about joining the group.