My overarching goal is to understand cognition in naturalistic environments using computational models. I focus on sensorimotor integration, examining how the brain generates predictions, detects prediction errors, and uses these errors to update behaviour. To investigate these processes, I combine virtual reality, mobile EEG, motion tracking, and computational modeling.

My dissertation focuses on multisensory integration during naturalistic navigation in virtual reality, under the supervision of Bob Wilson. This work investigates how people combine visual and body-based cues, and how they transform spatial estimates into actions as a closed-loop control. Read more in my review in Nature Reviews Psychology, our paper in Neuropsychologia, and our recent preprint on bioRxiv.

I also study how people learn to control robotic arms during teleoperation with the da Vinci Surgical System. This project uses control theory to model motor skill learning in human–robot interaction. Read more in our work in npj Science of Learning.

My work has been recognized by the Link Fellowship Honorable Mention, the University of Arizona Outstanding Research Award in Cognitive Science, and the Society for Neuroscience Trainee Professional Development Award. My research has been supported by the University of Arizona Research and Project (ReaP) Grant, the SensorLab Seed Grant, and the OpenAI Researcher Access Program.