This workshop will provide foundational knowledge for new R users regarding R basics, including operations, data types and structures, functions, control structures, and wrangling.

This workshop serves as an introduction to causal inference. Participants will learn the basics of the Rubin Causal Model, how to construct causal diagrams, and how to apply basic conditioning strategies to recover causal effects under explicit assumptions. No background in causal inference is necessary. 

Explore strategies for high-dimensional data using factor models, PLS, and modern machine learning. We compare linear, sparse, and nonlinear approaches for effective prediction and insight.

This workshop will provide foundational knowledge for new R users regarding R basics, including operations, data types and structures, functions, control structures, and wrangling.

This workshop explores strategies for estimating causal effects when conditioning strategies are implausible. Participants will learn tools such as difference-in-differences, instrumental variables, and regression discontinuity designs. Prior exposure to the basics of causal inference is expected. 

This workshop will go over methods of how to create data matrices, how to align DNA sequences and finally how to create phylogenetic trees using differing methods.

Learn how LLMs work—tokens, attention, training—and apply them to literature review, coding, and text analysis.