- Landau, W. (2020) “Reproducible computation at scale with targets”, R/Pharma 2020 Conference, half-day short course, https://github.com/wlandau/targets-tutorial.
- Landau, W. (2020) “Reproducible computation at scale with targets”, R/Pharma 2020 Conference, presentation, https://github.com/wlandau/rpharma2020.
- Landau, W. (2020) “Reproducible computation at scale with drake”, R/Medicine Conference. https://wlandau.github.io/rmedicine2020.
- Landau, W. (2020) “Reproducible computation at scale with drake”, useR! 2020 Conference, half-day short course, https://github.com/wlandau/learndrake.
- Landau, W. (2019) “Reproducible workflows at scale with drake”, rOpenSci Community Call, https://ropensci.org/commcalls/2019-09-24/.
- Landau, W. (2019) “Machine learning workflow management with drake”, invited 4-hour workshop, R/Pharma Conference.
- Landau, W. (2019) “Reproducible Computation at Scale in R”, Harverd DataFest.
- Landau, W. (2018) “The drake R package: reproducible data analysis at scale”, R/Pharma Conference.
- Landau, W., and Niemi, J. (2016), “A Fully Bayesian Strategy for High-Dimensional Hierarchical Modeling Using Massively Parallel Computing”. Joint Statistical Meetings, Section on Statistical Computing, Section on Statistical Graphics, Statistical Computing and Graphics Student Awards — Topic Contributed Papers. https://ww2.amstat.org/meetings/jsm/2013/onlineprogram/AbstractDetails.cfm?abstractid=307645.
- Landau, W., and Liu, P. (2013), “Dispersion Estimation and Its Effect on Test Performance in RNA-Seq Data Analysis”. Joint Statistical Meetings, Biometrics Section, contributed poster. https://ww2.amstat.org/meetings/jsm/2013/onlineprogram/AbstractDetails.cfm?abstractid=307645.