Curiosity, put
into practice.

My work brings teaching, AI, and research computing together so that more people can learn by doing, with access to the tools their questions require. I'm focused on advancing AI-native classrooms and equitable access to the infrastructure that supports them.

I teach graduate courses in data science, machine learning, and AI, and help connect instructional computing with the needs of educators and learners. I also lead education and training for MESA, helping researchers turn shared computing resources into practical skills and workflows.

I'm advancing AI-native classrooms: learning environments where assignments, feedback, AI support, and computing resources are designed together. Through EduCloud, I'm building the infrastructure for that approach. Cairn supports classroom workflows; Belay brings peer tutoring into the learning environment; the wider ecosystem connects hosting, model access, and compute.

Equitable access is central to this work. A student's ability to experiment should not depend on an expensive laptop or a personal AI subscription. I work toward shared, adaptable infrastructure that helps educators use institutional and public research resources, make costs visible, and keep meaningful choices in their hands.

My path here began with studying collective behavior in social insects. That interest in how local interactions shape larger systems still informs how I think about classrooms, communities, and technology. It also runs through Steppe, the civic commons I'm building for Central Oregon, and Midniht, a personal book library.