Summer Interns 2019

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We received almost 400 applications for our 2019 internship program from students with very diverse backgrounds. After interviewing several dozen people and making some very difficult decisions, we are pleased to announce that these twelve interns have accepted positions with us for this summer:

  • Therese Anders: Calibrated Peer Review. Prototype tools to conduct experiments to see whether calibrated peer review is a useful and feasible feedback strategy in introductory data science classes and industry workshops. (mentor: Mine Çetinkaya-Rundel)

  • Malcolm Barrett: R Markdown Enhancements. Tidy up and refactoring the R Markdown code base. (mentor: Rich Iannone)

  • Julia Blum: RStudio Community Sustainability. Study, enhance documentation and processes, and onboard new users. (mentor: Curtis Kephart)

  • Joyce Cahoon: Object Scrubbers. Help write a set of methods to scrub different types of objects to reduce their size on disk. (mentors: Max Kuhn and Davis Vaughan)

  • Daniel Chen: Grader Enhancements. Enhance [grader]( to identify students’ mistakes when doing automated tutorials. (mentor: Garrett Grolemund)

  • Marly Cormar: Production Testing Tools for Data Science Pipelines. Build on applicability domain methods from computational chemistry to create functions that can be included in a dplyr pipeline to perform statistical checks on data in production. (mentor: Max Kuhn)

  • Desiree De Leon: Teaching and Learning with RStudio. Create a one-stop guide to teaching with RStudio similar to Teaching and Learning with Jupyter. (mentor: Alison Hill)

  • Dewey Dunnington: ggplot2 Enhancements. Contribute to ggplot2 or an associated package (like scales) by writing R code for graphics and helping to manage a large, popular open source project. (mentor: Hadley Wickham)

  • Maya Gans: Tidy Blocks. Prototype and evaluate a block-based version of the tidyverse so that young students can do simple analysis using an interface like Scratch. (mentor: Greg Wilson)

  • Leslie Huang: Shiny Enhancements. Enhance Shiny’s UI, improve performance bottlenecks, fix bugs, and create a set of higher-order reactives for more sophisticated programming. (mentor: Barret Schloerke)

  • Grace Lawley: Tidy Practice. Develop practice projects so learners can practice tidyverse skills using interesting real-world data. (mentor: Alison Hill)

  • Yim Register: Data Science Training for Software Engineers. Develop course materials to teach basic data analysis to programmers using software engineering problems and data sets. (mentor: Greg Wilson)

We are very excited to welcome them all to the RStudio family, and we hope you’ll enjoy following their progress over the summer.

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