Resume

Education

• PhD Statistics, Iowa State U, 2016
• MS Statistics, Iowa State U 2013
• BS Math, U of Chicago 2011

Contact

• will dot landau at gmail dot com
• wlandau.github.io
• linkedin.com/in/wlandau
• github.com/wlandau

Select Publications

• Landau, W. (2021), “We Need Trustworthy R Packages”, The R Journal, 2021. https://doi.org/10.32614/RJ-2021-109.
• Landau, W. (2021), The stantargets R package: a workflow framework for efficient reproducible Stan-powered Bayesian data analysis pipelines. Journal of Open Source Software, 6(60), 3193, https://doi.org/10.21105/joss.03193.
• Chakladar, S., Liao, R., Landau, W., Gamalo, M., Wang, Y. (2021). “Discrete Time Multi-State Model with Regime Switching for Modelling COVID-19 Disease Progression and Clinical Outcomes.” Statistics in Biopharmaceutical Research, accepted, https://doi.org/10.1080/19466315.2021.1880966.
• Landau, W. (2021), “The targets R package: a dynamic Make-like function-oriented pipeline toolkit for reproducibility and high-performance computing”. Journal of Open Source Software, 6(57), 2959, https://doi.org/10.21105/joss.02959.
• 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. 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 journals.plos.org.

Select presentations

• Landau, W. (2024) “{targets}: a rediscovery from a different field.” LatinR Conference (keynote presentation), https://wlandau.github.io/LatinR2024/.
• Landau, W., and Stock, C., and Bean, A., and Sidi, Y., and Kunzmann, K. (2004) “{brms.mmrm}: a modern R package for Bayesian MMRMs.” Bayesian Biostatistics Conference, https://bayes-pharma.org/wp-content/uploads/2024/11/Will-Landau.pdf.
• Gao, C., and Landau, W.. (2024) “{mirai} and {crew}: next-generation async to supercharge {promises}, Plumber, Shiny, and {targets}”, Posit Conference, https://www.youtube.com/watch?v=s_cBi155ZIQ.
• Scott, E., and Nitta, J., and Landau, W.. (2023) “{targets} in Action.” rOpenSci Community Call, https://ropensci.org/commcalls/jan2023-targets/.

Select open-source software

• rOpenSci packages targets for general-purpose reproducible analysis pipelines.
• stantargets and jagstargets for Bayesian data analysis pipelines.
• crew: a mirai-powered worker launcher framework.
• crew.cluster and crew.aws.batch: crew for high-performance computing systems.
• brms.mmrm: a modern R package for Bayesian mixed models for repeated measures (MMRMs).

Awards

• 2023 Lilly Innovator Award. Awarded for developing a novel cloud-based computing framework to accelerate clinical trial design.
• 2021 Lilly Innovator Award. Awarded for creating the R package targets and its associated ecosystem to track workflow that empowers reproducible analytical research with efficiency.
• 2020 Lilly Innovator Award. Awarded for the development of efficient clinical trial simulation software needed for complex designs and trials of potential COVID-19 treatments.
• 2019 NumFOCUS New Contributor Recognition. Awarded for inclusive and collaborative work with rOpenSci. https://numfocus.org/blog/2019-numfocus-awards.
• 2017 Lilly Innovator Award. Awarded for leading a successful team effort to modernize Lilly’s internal process for contributing open source software.
• Student Paper Award, American Statistical Association Section on Statistical Computing, Jan 2016. Awarded for an early draft of the preprint at arxiv.org/abs/1606.06659.
• Vince Sposito Statistical Computing Award, Iowa State University, Aug 2013.
• GlaxoSmithKline Industrial Scholarship, Iowa State University, Sep 2011.
• Alumni Scholarship, Iowa State University, Aug 2011.

Skills

• Bayesian methods, Markov chain Monte Carlo, hierarchical models, high-dimensional data analysis, linear and nonlinear models, clinical trial simulation and design.
• Reproducible analysis pipelines, high-performance computing, R, package development, Stan, JAGS.
• Past experience with machine learning, C++, C, CUDA, Python, JavaScript, Fortran.

Senior Research Scientist

• October 2021 - present
• Eli Lilly and Company
• Developed Bayesian methods and proprietary software packages that were influential in the analyses and decision-making of clinical trials in Alzheimer’s and chronic pain.
• Created significant extensions to the {targets} R package and its ecosystem for Bayesian data analysis, data versioning, and cloud storage.

Research Scientist

• October 2016 - October 2021
• Eli Lilly and Company
• Created popular open-source rOpenSci packages such as targets for reproducible analysis pipelines in R.
• Drove the standardization and automation of Bayesian methods in the analysis of clinical trials.
• Led the successful transformation of the internal tool suite for the simulation and design of clinical trials.
• Increased company-wide external engagement with the R and open-source software (OSS) communities. Modernized internal processes to allow substantial contribution to OSS and served as a gatekeeper for new OSS disclosures.
• Lead statistician in early-phase autoimmune asset teams.

Research assistant

• 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 fbseq R package to distribute the analysis method.
• Implemented and distributed parallel computing backends for CUDA GPUs (fbseqCUDA) and OpenMP (fbseqOpenMP).
• Created the remakeGenerator, parallelRemake, and downsize packages to manage, ameliorate, expedite, and accelerate computationally heavy reproducible workflows that are under heavy development.

Seminar instructor

• Aug - Dec, 2012 and 2013.
• Department of Statistics, Iowa State University.
• GPU computing seminar series at wlandau.github.io/gpu.
• Educated faculty and graduate students on massively parallel computing with general-purpose graphics processing units.
• Constructed, curated, and distributed slides, video, and example code at wlandau.github.io/gpu and on YouTube.

Course instructor

• Jan - May, 2012 and 2013.
• Department of Statistics, Iowa State University.
• STAT 305: Engineering Statistics (wlandau.github.io/stat305).

Grader

• Aug - Dec, 2011.
• Department of Statistics, Iowa State University.
• STAT 231: Engineering Probability.
• STAT 105: Introduction to Engineering Statistics.

Leadership in Indianapolis

• Member of the board of directors, Friends of Fort Harrison State Park. May 2022 - present.
• Organizer and leader of volunteer bi-monthly park maintenance sessions at Fort Harrison. Sessions focus on removing and controlling invasive plants. May 2023 - present.

Leadership at Eli Lilly and Company

• Led a successful team effort to modernize Lilly’s internal procedure for contributing open source software.
• Served as a volunteer moderator in the 2017 National Science Bowl (high school Indiana Regionals).

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.

References

• Jarad Niemi, PhD advisor and major professor, niemi@iastate.edu.
• Dan Nettleton, lead principal investigator of the RNA-sequencing Working Group (Iowa State Department of Statistics), dnett@iastate.edu.
• Peng Liu, MS advisor and major professor, pliu@iastate.edu.
• Additional references available on request.

Hobbies

• Climbing, Brazillian Jiu Jitsu, sailing, windsurfing