Data Points showcases the work of Penn’s data science community through concise, engaging articles. Each post takes a complex idea and transforms it into accessible insights in creative and compelling ways—whether through a high-level walkthrough of a key figure, or an interactive, explorable explanation.
Interested in contributing? We can help with technical and editorial guidance so your post is as impactful and polished as the research it represents. Check out How to Contribute - we’re excited to collaborate and help bring your ideas to life!
Five Penn physicists — from HEP theorists to experimentalists to astrophysicists — share their real AI workflows in a May 2026 training session, covering symbolic computation, agentic coding, and the honest limits of trust
Postdoctoral researchers across two sessions compared model selection strategies, built MCP servers live, and debated what agentic AI means for peer review integrity and student learning
A narrative essay on how to keep sequence and evidence visible under polarization: turning fast-moving crises into structured, revisitable context
From Data to Documentation: Assisting Codebook Completion with LLMs
A visual guide on how foundation models enhance computer vision research with small datasets.
An interactive tutorial about decomposing variation into distinctions worth making.
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