- Landau, W., Niemi, J., and Nettleton, D., “Fully Bayesian analysis of RNA-seq counts for the detection of gene expression heterosis”. Journal of the American Statistical Association, https://doi.org/10.1080/01621459.2018.1497496.
- Landau, W. (2018), “The drake R package: a pipeline toolkit for reproducibility and high-performance computing”. Journal of Open Source Software, 3(21), 550, https://doi.org/10.21105/joss.00550.
- Niemi, J., Mittman, E., Landau, W., and Nettleton, D. (2015), “Empirical Bayes Analysis of RNA-seq Data for Detection of Gene Expression Heterosis,” Journal of Agricultural, Biological, and Environmental Statistics, 20, 1-15. Available at
- Landau, W. and Liu, P. (2013), “Dispersion Estimation and Its Effect on Test Performance in RNA-seq Data Analysis: A Simulation-Based Comparison of Methods,” PLOS One, 8. Available at
- Ratliff, B., Womack. C., Tang, X., Landau, W., Butler, L., and Szpunar, D. (2010), “Modeling the Rovibrationally Excited C2H4OH Radicals from the Photodissociation of 2-Bromoethanol at 193 nm,” Journal of Physical Chemistry, 114, 4934-4945. Available at
Open Source Software
- drake, an R-focused pipeline toolkit for reproducible computation and high-performance computing. Part of rOpenSci.
- txtq, a minimalist, serverless, socketless message queue for interprocess communication.
- downsize, and R package to toggle between the test and production versions of large workflows.
- R packages fbseq, fbseqCUDA, and fbseqOpenMP. A toolkit for the fully Bayesian analysis of genomic count data.
- Reproducible research, statistical computing, hierarchical models, Bayesian methods, Markov chain Monte Carlo, high-dimensional data analysis, genomics data analysis, exploratory analysis, visualization, linear and nonlinear models, data mining, machine learning, predictive modeling, multivariate analysis.
- High-performance computing, R, R package development, general-purpose graphics processing unit (GPU) computing, CUDA, shell scripting, LaTeX, HTML, CSS.
- October 2016 - Present
- Eli Lilly and Company
- Developed internal statistical tools and capabilities for the design, simulation, and analysis of clinical trials.
- Served as the lead statistician in early-phase autoimmune asset teams.
- Supported late-phase clinical trial teams with advanced analytics, including clinical program simulation and tailored therapeutics.
- Published open-source software packages drake and txtq to improve reproducibility and high-performance computing in R.
- May 2013 - Aug 2016
- RNA-sequencing Working Group, Department of Statistics, Iowa State University.
- Funded by NIH grant R01GM109458 with Drs. Dan Nettleton and Jarad Niemi.
- Developed a new fully Bayesian analysis method for high-dimensional genomic datasets using hierarchical models.
- Implemented massively parallelized Markov chain Monte Carlo.
- Created the
R package to distribute the analysis method.
- Implemented and distributed parallel computing backends for CUDA GPUs (fbseqCUDA) and OpenMP (fbseqOpenMP).
- Created the
downsize packages to manage, ameliorate, expedite, and accelerate computationally heavy reproducible workflows that are under heavy development.
- Aug - Dec, 2011.
- Department of Statistics, Iowa State University.
- STAT 231: Engineering Probability.
- STAT 105: Introduction to Engineering Statistics.
Leadership at Eli Lilly and Company
Leadership at Iowa State University
- Founder and leader, Cloud Computing Working Group, Sep - Dec 2015.
- Member, Computation Advisory Committee, Sep 2015 - May 2016.
- Volunteer instructor, Office of Precollegiate Programs for Talented and Gifted (OPPTAG), Mar 13, 2014.
- Fellow, Preparing Future Faculty, Aug 2013 - May 2014.
- Assistant Coach, Boxing Club, Aug 2013 - Dec 2013.
- Climbing, Brazillian Jiu Jitsu, sailing, windsurfing