Datapalooza, our annual signature event in the University of Virginia School of Data Science, is this Friday.
I’ll be on a panel (along with SDS board members Laura Ipsen and Kane Geyer) called What Do You Really Want to Know About Working in Data Science? Here’s what we sent out to students.
This is your chance to challenge data science academic and industry leaders to give candid answers and real-world perspectives about what’s happening in the field—as well as their personal journey to and through data science. They will be put in the “hot seat” by our very own students. How did they get here? What trends, tools, challenges, and opportunities are emerging now and in the future? How is industry/higher education thinking about and addressing AI ethics and safety concerns? And where will people be in all of this? Come curious and ready to engage.
Students are moderating and I don’t know exactly where the conversation might go, but based on the session title and description, I have an idea about some topics that might come up. This post is a collection of previous essays I’ve published here that I might refer to in the panel this Friday.
You Need a Personal Website
This Friday I’ll be a panelist on a session about AI and the future of work here at Datapalooza, our signature data science conference (register free). The panel will be run by students, taking questions from students on career prep and advice.
Moving from academia to biotech
I originally wrote and published this essay at The Connected Ideas Project, an excellent newsletter by my good friend and colleague Alexander Titus. If you’re not reading TCIP you’re missing out.
Fewer Rungs on the Ladder
A few days into my AI Dry July, I finally read this new perspective paper arguing that AI will not replace bioinformaticians, but will move them up to higher value work: experimental design, interpretation, governance, deciding what to do next.
My relationship with AI is changing
Personal essay, weekend edition. Starting with a familiar quote often attributed to English economist John Maynard Keynes:
AI Dry July: Aborted
Back in June I wrote about going the entire month of July without using AI. I wrote about why I’m doing it and what I hoped to achieve.
AI Dry July
For the last few years I’ve done a Sober October where I abstain from any alcohol for the entire month. I don’t drink much these days anyway, but a solid month of teetotaling abstinence is a nice reset button that turns something habitual into something I mindfully enjoy with friends and family.
Technical blogging for growth and learning
I’ve been blogging about genetics, statistics, computational biology, data science, and science in general for over 15 years. I published my first blog post on Getting Genetics Done in 2009, and started this blog last year after taking a few years off.
Learning in Public
Cite: Stephen Turner. “Learning in Public.” Paired Ends (2024). DOI: https://doi.org/10.59350/xwgsf-nj906.
What Counts as Impact?
Before anything else: I write this as a tenured professor who loves this job. Academia has been good to me, and I plan to spend the rest of my career here. So this essay isn’t a criticism or indictment. Quite the contrary. I only bother critiquing the people and institutions I act…
Students Think AI Hurts Their Thinking. They Use It Anyway.
A recent RAND report from surveyed over 1,200 students from middle school through college in December 2025 on how they’re using AI for schoolwork. The headline numbers: 62% of students now use AI for homework help, up from 48% just a few months earlier. And 67% of students agreed that using AI for schoolwork harms critical thinking, up from 54% in Febru…
Expertise Before Augmentation
Arjun Krishnan (lab, Bluesky), is a biomedical informatics researcher and co-director of PhD training programs at the University of Colorado Anschutz, has published a pair of complementary pieces that articulate something I’ve been thinking about for a while but
AI advice and expert judgment
Anthropic published a paper and a blog post a few days ago. Seems like every tech podcast I listen to talked about this paper this week (Hard Fork, Machine Gods, others). In the extra hour we got here in the US falling back to standard time I got a chance to read over the paper and accompanying blog.
Returning to Academia: UVA Data Science
I started my academic career in biomedical research as faculty in the University of Virginia (UVA) School of Medicine. After eight years I jumped to industry & government consulting to tackle problems in public health and biosecurity for a few years before joining