Summary
Nathaniel Zuk is a senior lecturer and auditory neuroscientist with over a decade of experience translating electrophysiology and computational modeling into insights about how brains and AI interpret sound. He leads research on continuous EEG tracking and auditory learning, combining experiment design, biophysical modeling, and machine learning in Matlab and Python to decode neural responses during naturalistic tasks. His career spans top labs and institutions—including MIT, Trinity College Dublin, and the Edmond and Lily Safra Center—where he has moved projects from ideation to publication and taught statistics, research methods, and music perception. Nathaniel’s toolkit ranges from single-unit electrophysiology and microchip programming to online behavioral platforms (Gorilla, PsychoPy) and digital audio synthesis, reflecting a rare blend of wet-lab, computational, and engineering skills. He also investigates auditory processing differences in autism, highlighting a translational focus that bridges basic neuroscience and real-world perceptual outcomes.
7 years of coding experience
7 years of employment as a software developer
Doctor of Philosophy (PhD), Speech and Hearing Bioscience and Technology, Doctor of Philosophy (PhD), Speech and Hearing Bioscience and Technology at Massachusetts Institute of Technology
Bachelor of Science (B.S.), Biomedical Engineering, Bachelor of Science (B.S.), Biomedical Engineering at University of Rochester