معرفی
John E. Parker is a Visiting Assistant Professor in the Department of Computer Science at Williams College, where he teaches courses including Data Science and Foundations of Computational Neuroscience. His research bridges computer science, mathematics, and neuroscience to develop computational models of brain function and dysfunction.
Education:
- Ph.D. in Applied Mathematics, University of New Hampshire (2021)
- B.S. in Mathematics, Elon University (2014)
Dr. Parker's research focuses on computational neuroscience, particularly using mathematical and computational approaches to understand neural dynamics in health and disease. His work spans nonlinear dynamics and chaos theory applied to neural systems, mathematical modeling of Parkinson's Disease mechanisms, and development of computational tools for neural data analysis. He investigates how computational methods can better understand brain function, with recent work centering on modeling mechanisms underlying disease-like circuit dysfunction and approaches to correct pathological neural behavior.
His publication record demonstrates a clear trajectory from foundational work on chaotic neural models to increasingly translational research on Parkinson's Disease mechanisms. Parker has developed innovative computational approaches to model neural dynamics, particularly focusing on the basal ganglia circuitry affected in Parkinson's. His research integrates mathematical modeling, computational neuroscience, and experimental data to address questions about neural oscillations, pathway interactions, and potential therapeutic interventions.
Scientific Awards:
- UCR Contributed Talk Award for Mathematical Neuroscience
Dr. Parker serves as a mentor to undergraduate researchers in computational neuroscience projects. His collaborative research involves partnerships with neuroscientists at institutions including the University of Pittsburgh, where he completed postdoctoral work. His work has been supported by research grants focused on computational approaches to understanding neural circuit dysfunction in movement disorders.
He is actively involved in developing computational tools for neuroscience research, most notably the STReaC (Spike Train Response Classification) toolbox for automated analysis of neural responses to stimulation. His laboratory work combines mathematical modeling with analysis of electrophysiological data to investigate neural dynamics in both healthy and disease states.
