I am a PhD statistician passionate about implementing and developing statistical methods for real world applications. I am also an experienced R package developer (>90,000 downloads) and YouTube educator (>1.4 million views).
My research spans a few topics, but my favorite contributions to the statistical community are statistical methodology and R packages. The R package glmm uses Monte Carlo likelihood approximation to fit generalized linear mixed models (downloads per month), and the R package stableGR provides a stabilized Gelman-Rubin convergence diagnostic (for Markov chain Monte Carlo (downloads per month). (Given my love for R, it’s probably not surprising that I co-organize the Twin Cities chapter of R Ladies.)
Here are some links to some recent papers:
- Causes and timing of recurring subarctic Pacific hypoxia (DOI: 10.1126/sciadv.abg2906) published in Science Advances
- Revisiting the Gelman-Rubin Diagnostic (DOI: 10.1214/20-STS812) published in Statistical Science
- Likelihood-based inference for generalized linear mixed models: Inference with the R package glmm (DOI 10.1002/sta4.339) published in Stat
Service at the national level is important to me. For the last couple years, I’ve served on the MAA/ASA’s Joint Committee on Undergraduate Statistics and Data Science Education. I also serve on the ASA’s Committee on Women in Statistics, and I am the program chair elect for the ASA’s JEDI (justice, equity, diversity, and inclusion) outreach group.
My Ph.D. is from the School of Statistics at the University of Minnesota. My doctoral dissertation, Monte Carlo Likelihood Approximation for Generalized Linear Mixed Models, focuses on likelihood-based inference for generalized linear mixed models. My co-advisers were Galin Jones and Charles Geyer. You can read more about generalized linear mixed models, download the R package glmm through CRAN, and learn how to use the package with our Stat paper.
If you wish to contact me, please email me at firstname.lastname@example.org. I look forward to hearing from you.