Pritesh N. Parmar

University of Illinois Chicago, Shirley Ryan AbilityLab

Papers

Total Citations

H-Index

About

Pritesh N. Parmar investigates the computational principles underlying human motor learning, with a focus on how sensory feedback shapes skill acquisition and neurorehabilitation. His research integrates control theory, machine learning, and experimental psychophysics to develop data-driven models that optimize training protocols. In his most cited work, "Optimal gain schedules for visuomotor skill training using error-augmented feedback" (2015, 6 citations), Parmar introduced a model-based framework for determining when and how to amplify movement errors to accelerate learning—a paradigm that directly informs robotic and virtual-reality rehabilitation design. He further advanced the field with "Sparse Identification Of Motor Learning Using Proxy Process Models" (2019, 3 citations), which provides a computationally efficient method to track learning dynamics in real time, enabling adaptive training environments. His 2022 study, "Sensory-Motor Interactions and the Manipulation of Movement Error," deepens our understanding of how the nervous system integrates conflicting sensory signals. Though early in his career, Parmar’s work bridges theoretical motor control and practical rehabilitation engineering, offering clinicians and engineers actionable algorithms for personalized therapy. His contributions are particularly notable for translating complex sensorimotor interactions into tractable, optimization-ready models.