Dear Friends,
I am pleased to announce that our SLDS May webinar will be held on May 28, 2 pm Eastern Time, featured by Dr. Hadley Wickham from Posit. Hope to see you there!
Title: y code when ai?
Speakers: Dr. Hadley Wickham, Posit
Date and Time: May 28, 2026, 2:00 to 3:30 pm Eastern Time
Registration Link: ASA SLDS Webinar Registration Link [eventbrite.com]
Abstract:
Software engineering has irrevocably changed, and there is now relatively little need to personally type code. But I don't think this means you should no longer learn to code. In fact, it's now a better time to learn than ever before. Coding agents might have changed how we create code, but code as an artifact of reasoning and reproducibility is still incredibly valuable.
I'll share what I've learned using AI tools in my own software engineering practice, particularly the importance of the "double-entry accounting" of programming—unit tests—and the importance of creating an adversarial environment for code generation. I'll also include some speculation about how AI will affect data science. It's less clear to me how this is going to play out, since it's much harder to automatically validate the most important parts of data science.
This is a scary time, but it's also exhilarating. I built an iPhone app in a couple of hours without ever having written Swift. The ceiling on what individuals can build has never been higher, and it's easier than ever to bring your imagination to life.
Presenter: Hadley Wickham is Chief Scientist at Posit PBC, winner of the 2019 COPSS award, and a member of the R Foundation. He builds tools (both computational and cognitive) to make data science easier, faster, and more fun. His work includes packages for data science (like the tidyverse, which includes ggplot2, dplyr, and tidyr) and principled software development (e.g. roxygen2, testthat, and pkgdown). He is also a writer, educator, and speaker promoting the use of R for data science. Learn more on his website, <http://hadley.nz>.
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Also, I would like to highlight two upcoming conferences, SLDS 2026 and STAI-X 2026. More information is available at:
SLDS 2026: https://asa-slds.github.io/slds2026/
STAI-X 2026: https://statsupai.org/STAIX2026/index.html
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Boxiang Wang
Associate Professor
Department of Statistics and Actuarial Science
University of Iowa
Iowa City, IA, United States
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