Projects
Code, models, and other things I’m working on. This page is a work in progress.
PracticeLab
Motor learning, data science, and practice design
PracticeLab is an early-stage project I'm building to explore how research on motor learning and skill acquisition can be used to design better practice.
The idea grew out of a simple observation: we have decades of research on how people learn motor skills, but translating that knowledge into decisions about what and how to practise isn't straightforward. I'm interested in whether we can use computational methods to help bridge that gap.
Right now, I'm working on the underlying algorithm in Python. The goal is to estimate a learner's current state from noisy performance data and use those estimates to inform what they should practice next. I’m interested in how ideas from motor learning research, including contextual interference, practice variability, and the relationship between practice and real-world performance, can inform better practice schedules. The goal is to design practice that reflects the demands of the actual task and promotes long-term learning and transfer, rather than simply improving performance during practice.
It's still very early, and much of the work involves figuring out how to formalize these ideas mathematically. For me, it's an interesting intersection of motor learning, statistics, and programming.
For now, the writing page has some practical Python notes.