Digital Science
Synthetic electronic health records and computational methods for clinical data.
Trustworthy AI
for healthcare.
The Computational Thinking Lab at Indiana University studies how AI can make care safer, more private, and more efficient.
What we work on
Research across healthcare AI.
Three research directions, from synthetic clinical data to human-centered and efficient models for healthcare.
Human-Centered AI
Health coaches grounded in behavior science, built around the person.
Green AI
Small, efficient language models that cut the compute cost of healthcare ML.
Featured
From the lab.
Trustworthy evaluation, in the open.
[Placeholder] A unified evaluation suite for high-stakes AI — stress tests, calibration metrics, and a public leaderboard. Built so that "state of the art" means ready for the real world.
Read more ›How we work
Built for impact and trust.
Work with a cross-functional team of researchers turning hard questions into reliable systems.
Rigorous evaluation.
We measure what matters — calibration, robustness, and abstention — not just top-line accuracy.
Real-world stakes.
We work on problems where reliability is non-negotiable, from medicine to safety-critical systems.
Open science.
We release benchmarks, code, and findings so the community can build on and scrutinize our work.
Cross-disciplinary.
A team spanning ML, statistics, and domain expertise that takes ideas from question to result.
Own your AI future