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Presentations

Slides and materials for talks, workshops, and invited presentations.

2026 (upcoming)

  • JSM - Automate all the things [repo]

  • ICOTS - Teaching Statistical Computing in the Age of AI [repo]

2025

  • DSC - Reflections on a decade of teaching statistical computing [repo | slides]

  • UseR! - Parsing Quarto and R Markdown documents in R [repo | slides]

  • posit::conf - Shiny for R (Workshop) [repo]

  • Duke DSS - Computing Bootcamp (Workshop) [repo]

2024

  • JSM - Programmatic Manipulation of Quarto Documents for Teaching [repo | slides]

  • posit::conf - Shiny for R (Workshop) [repo]

  • Duke DSS - Computing Bootcamp (Workshop) [repo]

2023

  • JSM - Quarto and automation [repo | slides]

  • Preparing to Teach - Teaching-focused careers [repo | slides]

  • posit::conf - Shiny for R: Intro and Dashboards (Workshop) [intro | dashboards]

  • Duke DSS - Computing Bootcamp (Workshop) [repo]

2022

  • ISBA - An overview of Bayesian computational frameworks for teaching [repo | slides]

  • TWR - Teaching Statistical Computing with Git and GitHub [repo | slides]

  • rstudio::conf - Getting Started with Shiny (Workshop) [repo]

  • Duke DSS - Computing Bootcamp (Workshop) [repo]

2021

  • rstudio::conf - parsermd: a brief look at the parsermd package [repo | slides]

  • ISI WSC - Teaching Data Science (Workshop) [repo]

  • Duke DSS - Computing Bootcamp (Workshop) [repo]

2020

  • rstudio::conf - livecode: broadcast your code from R [repo | slides]

  • UseR! - livecode: broadcast your code from R [repo]

  • JSM - Computation infrastructure for teaching Bayesian modeling [repo | slides]

  • TLMSCO - Teaching computing using git and GitHub [repo | slides]

  • ECME - Using git and GitHub in the Classroom [repo | slides]

  • Edinburgh - Teaching Statistics and Data Science Online (Workshop) [repo]

2019

  • JSM - ghclass: tools for managing classes on GitHub [repo | slides]

  • JSM - Reproducible Computing (Workshop) [repo]

  • UseR! - ghclass [repo | slides]

  • SDSS - Using Rocker containers and CI for teaching R-based courses [repo | slides]

  • EdinbR - ghclass [repo | slides]

  • ECME - Live coding for teaching computation [repo | slides]

  • Edinburgh - Computation is fundamental to (modern) statistics [repo]

2018

  • rstudio::conf - Kaggle in the Classroom: using R and GitHub to run predictive modeling competitions [repo | slides]

  • ICOTS - Introducing modern computation into a data science / statistics curriculum (Workshop) [repo | github]

  • Duke DVS - Tidying Hierarchical Data from the Web [repo | slides]

  • Edinburgh - Computational infrastructure for a modern statistical curriculum [repo | slides]

  • Edinburgh Teaching - Teaching Talk [repo | slides]

  • ISBA - Reproducible Computing (Workshop) [repo]

  • Duke DSS - Computing Bootcamp (Workshop) [repo]

2017

  • JSM - Moving Away from Ad Hoc Statistical Computing Education [repo | slides]

  • UseR! - Data Carpentry: Open and Reproducible Research with R (Workshop) [site | repo]

  • DataFest - Working with Large Data [repo | slides]

  • Duke DSS - Computing Bootcamp (Workshop) [repo]

2016

  • JSM - Statistical Computing as an Introduction to Data Science [repo | slides]

  • UseR! - Continuous Integration and Teaching Statistical Computing with R [repo | slides]

  • DataFest - Data Munging with R and dplyr [repo | slides]

  • Proseminar - Building an Online Presence and Network [repo | slides]

2015

  • JSM - Teaching statistical computing leveraging the GitHub ecosystem [repo | slides]

  • UseR! - Teaching R using the GitHub ecosystem [repo | slides]

  • DataFest - Data Munging with R and dplyr [repo | slides]

  • Duke SSRI DABSS - Geospatial data and the R ecosystem [repo | slides]

  • Duke Focus - A brief introduction to Gaussian processes [repo | slides]

  • Duke StatSci - GPUs and the computational efficiency of Gaussian process based models [repo | slides]

2014

  • JSM - A Data Fusion Approach for Space-Time Analysis of Speciated PM2.5 [repo | slides]

  • Duke StatSci - Using GPUs to improve the computational efficiency of Gaussian process models [repo | slides]

2013

  • JSM - GPUs, linear algebra, and efficient computing for Gaussian process models [repo | slides]

  • UseR! - Leveraging GPU libraries for efficient computation of Gaussian process models in R [repo | slides]

2012

  • JSM - Leveraging GPU Libraries for Efficient Computation of Bayesian Spatial Assignment Models in R [repo | slides]

  • UseR! - rgeos: spatial geometry predicates and topology operations in R [repo | slides]

2011

  • JSM - Spatial Models for Bird Origin Assignment Using Genetic and Isotopic Data [repo | slides]

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