About me

I am an associate professor of the practice in Statistical Science at Duke University. My research interests include applied spatial statistics with an emphasis on Bayesian statistics and computational methods. My other focus is the development of pedagogy for teaching statistical computing and data science at the undergraduate and graduate levels. I am also interested in the support and deployment of the computational infrastructure necessary for teaching in these areas.

Current course(s):

• Sta 523 - Programming for Statistical Science (R) Fa '26

Previous courses (Duke):

• Sta 030 - Statistics and Quantitative Literacy Sp '12
• Sta 102 - Intro Biostatistics Su '16 Sp '16 Fa '15 Sp '15 Fa '14 Sp '14 Sp '13
• Sta 111 - Probability and Statistical Inference Su '14
• Sta 112 - Better Living through Data Science Fa '16
• Sta 230 / Mth 230 - Probability Sp '12 Sp '14
• Sta 323 - Statistical Computing (R) Sp '26 Sp '25 Sp '24 Sp '22 Sp '19 Sp '18 Sp '17 Sp '16
• Sta 344 - Spatial Statistics Fa '23 Fa '22
• Sta 444 / 644 - Spatio-Temporal Modeling Fa '18 Sp '18 Sp '17
• Sta 523 - Programming for Statistical Science (R) Fa '25 Fa '24 Fa '23 Fa '22 Fa '21 Fa '18 Fa '17 Fa '16 Fa '15 Fa '14
• Sta 663 - Statistical Computation Sp '26 Sp '25 Sp '23 Sp '22
• Sta 790 - Advanced Statistical Computing Sp '19

Previous courses (Univ of Edinburgh):

• Math 08068 - Facets of Mathematics Fa '19
• Math 11176 - Statistical Programming Fa '20 Fa '19
• Math 11205 - Machine Learning in Python Sp '21 Sp '20

• An educator's perspective of the tidyverse

  Çetinkaya-Rundel, M., Hardin, J., Baumer, B. S., McNamara, A., Horton, N. J., Rundel, C. (2022) Technology Innovations in Statistics Education, 14(1)

• The Growing Importance of Reproducibility and Responsible Workflow in the Data Science and Statistics Curriculum

  Horton, N. J., Alexander, R., Parker, M., Piekut, A., Rundel, C. (2022) Journal of Statistics and Data Science Education, 30(3), 207-208

• Implementing Version Control With Git and GitHub as a Learning Objective in Statistics and Data Science Courses

  Beckman M., Çetinkaya-Rundel M., Horton N., Rundel C., Sullivan A., Tackett M. (2021) Journal of Statistics and Data Science Education, 29 (Sup 1)

• Long distance seed dispersal by forest elephants

  Poulsen, J., Beirne, C., Rundel, C., Baldino, M., Kim, S., Knorr, J., Minich, T., Jin, L., Núñez, C., Xiao, S., Mbamy, W., Obiang, G., Masseloux, J., Nkoghe, T., Ebanega, M., Clark, C., Fay, M., Morkel, P., Okouyi, J., White, L., Wright, J. (2021) Frontiers in Ecology And Evolution. 9, 962 DOI

• Teaching an Undergraduate Course in Bayesian Statistics: A Panel Discussion

  Johnson A., Rundel C., Hu J., Ross K., Rossman A. (2020) Journal of Statistics Education. 28 (3)

• Estimation of gut passage time of wild, free roaming forest elephants

  Beirne C., Nuñez C., Baldino M., Kim S., Knorr J., Minich T., Jin L., Xiao S., Mbamy W., Obiang G., Masseloux J., Nkoghe T., Ebanega M., Rundel C., Wright J., Poulsen J. (2019) Wildlife Biology. 2019 (1)

• Infrastructure and tools for teaching computing throughout the statistical curriculum

  Cetinkaya-Rundel M, Rundel CW (2017) Practical Data Science for Stats - a PeerJ Collection. PeerJ Preprint Repo

• A data fusion approach for spatial analysis of speciated PM2.5 across time

  Rundel CW, Schliep EM, Holland D, Gelfand A (2015) Environmetrics 26 (8) , 515-525.

• Novel statistical methods for integrating genetic and stable isotope data to infer individual-level migratory connectivity

  Rundel CW, Wunder M, Alvarado AH, Ruegg K, Harrigan R, Schuh A, Jeffrey K, Siegel R, DeSante DF, Smith TB, Novembre J (2013) Molecular Ecology 22 (16) , 4163-4176. Repo

• Quality of reproductive health services to limited English proficient patients

  de Bocanegra HT, Rostovsteva D, Cetinkaya M, Rundel CW, Lewis C (2011) Journal of Health Care for the Poor and Underserved 22 (4) , 1167-1178.

• Overexpression of a Drosophila Homolog of Apolipoprotein D Leads to Increased Stress Resistance and Extended Lifespan

  Walker DW, Muffat J, Rundel CW, Benzer S (2006) Current Biology 16 (7) , 674-679.

• ghclass Repo

  Tools for managing classroom and assignment related tasks on GitHub.

• parsermd CRAN Repo

  Tools for parsing and programmatically interacting with R Markdown documents.

• md4r Repo

  R wrapper of the md4c markdown parsing library.

• checklist Repo

  Tools for automating checking of R projects, with a focus on automated feedback via CI tooling like GitHub actions.

• learnrhash Repo

  Tools for recording student results for questions and exercises in learnr docu- ments.

• diffmatchpatch CRAN Repo

  A wrapper for Google's 'diff-match-patch' library. It provides basic tools for computing diffs, finding fuzzy matches, and constructing / applying patches to strings.

• livecode Repo

  Library for broadcasting source files during live codeing sessions.

• rgeos CRAN Repo

  R interface to the Geometry Engine, Open Source (GEOS) library.

• RcppGP Repo

  Tools for efficiently working with Gaussian Processes in R / C++.

• markermd Github

• Shiny for R (Workshop)

  September 2025, Posit Conf 2025

• Parsing Quarto and R Markdown documents in R

  January 2025, useR! 2025 Slides

• Reflections on a decade of teaching statistical computing (Invited)

  January 2025, Datascience in the classroom 2025 Slides

• Introduction to Shiny for R (Workshop)

  August 2024, Posit Conf 2024

• Programmatic Manipulation of Quarto Documents for Teaching (Invited)

  August 2024, Joint Statistical Meetings 2024 Slides

• Introduction to Shiny (Workshop)

  September 2023, Posit Conf 2023

• Quarto and automation in teaching statistical computing (Invited)

  August 2023, Joint Statistical Meetings 2023 Slides

• Teaching-focused careers

  January 2023, Preparing to Teach 2023 Slides

• Introduction to Shiny (Workshop)

  July 2022, RStudio Conf 2022

• An overview of Bayesian computational frameworks for teaching

  January 2022, ISBA 2022 Slides

• Teaching Statistical Computing with Git and GitHub

  January 2022, Toronto Workshop on Reproducibility 2022 Slides

• Teaching Data Science (Workshop)

  June 2021, ISI Short Course 2021 Materials

• parsermd - parsing R Markdown for fun and profit

  January 2021, RStudio Global 2021 Recording

• Teaching computing using git and GitHub

  September 2020, Teaching and Learning of Mathematical and Statistical Computing Online 2020 Slides

• Computation Infrastructure for Teaching Bayesian Modeling (Invited)

  August 2020, JSM 2020 Slides

• Teaching Statistics and Data Science Online

  July 2020, Online Workshop Materials

• livecode: broadcast your live coding sessions from and to RStudio

  January 2020, RStudio Conf 2020 Slides

• ghclass: an R package for managing classes with GitHub

  August 2019, JSM 2019 Slides

• Reproducible Computing (Workshop)

  August 2019, JSM 2019 Materials

• ghclass: an R package for managing classes with GitHub

  July 2019, UseR! 2019 Slides

• Using Rocker containers and CI for teaching R-based courses (Invited)

  May 2019, SDSS 2019 Slides

• Teaching Data Science, Reproducibly (Workshop)

  July 2018, ICOTS 2018 Site Repo

• Reproducible Computing (Workshop)

  June 2018, ISBA 2018 Repo

• Moving Away from Ad Hoc Statistical Computing Education (Invited)

  August 2017, JSM 2017 Slides

• Data Carpentry: Open and Reproducible Research with R (Tutorial)

  July 2017, UseR! 2017 Site Repo

• Statistical Computing as an Introduction to Data Science (Invited)

  August 2016, JSM 2016 Slides

• Continuous Integration and Teaching Statistical Computing with R

  July 2016, UseR! 2016 Slides

• Teaching statistical computing leveraging the github ecosystem

  August 2015, JSM 2015 Slides

• Teaching R using the github ecosystem

  July 2015, UseR! 2015 Slides

• Geospatial data and the R ecosystem

  April 2015, Duke SSRI DABSS Seminar Slides

• A Data Fusion Approach for Space-Time Analysis of Speciated PM$_{2.5}$

  August 2014, JSM 2014 Slides

• Using GPUs to improve the computational efficiency of Gaussian process models

  February 2014, Duke Dept. of Statistical Science Seminar Slides

• GPUs, linear algebra, and efficient computing for Gaussian process models

  August 2013, JSM 2013 Slides

• Leveraging GPU libraries for efficient computation of Gaussian process models in R

  July 2013, UseR! 2013 Slides

• Leveraging GPU Libraries for Efficient Computation of Bayesian Spatial Assignment Models in R

  August 2012, JSM 2012 Slides

• rgeos: spatial geometry predicates and topology operations in R

  July 2012, UseR! 2012 Slides

• Spatial Models for Bird Origin Assignment Using Genetic and Isotopic Data

  August 2011, JSM 2011 Slides

• rundel@gmail.com, colin.rundel@duke.edu
• 204 Old Chemistry Building, Duke University, Durham, North Carolina, 27708, USA
• Office hours: By appointment