Teaching

Current course(s):

  • Sta 323 - Statistical Computing Sp '18
  • Sta 444 / 644 - Spatio-Temporal Modeling Sp '18

Previous courses:

Publications

Talks

  • 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

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

    August 2014, JSM 2014 Slides

  • Geospatial data and the R ecosystem

    April 2014, Duke SSRI DABSS Seminar 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

Contact

  • rundel@gmail.com
  • 204 Old Chemistry Building, Duke University, Durham, North Carolina, 27708, USA
  • Monday 12:00 to 2:00 pm or by appointment