Summary
Heejung Jung is a cognitive neuroscientist and research data scientist based in Palo Alto with nine years of experience mapping how expectations shape perception and pain using neuroimaging, intracranial EEG, and machine learning. At Stanford she builds LLM-based neural encoding pipelines to predict in-vivo brain signals from epilepsy recordings and has shown broadband dynamics better capture context modulation than traditional oscillatory approaches. Previously at Dartmouth she led one of the largest multimodal neuroimaging datasets (400 fMRI), developed reproducible automated pipelines now shared on OpenNeuro, and demonstrated that anticipatory signals recruit pain networks before stimuli occur. She combines rigorous statistical training, hands-on ML pipeline engineering, and open-science practices to push novel methods for studying social bias and future-oriented neural states. An underappreciated strength is her track record of translating complex experimental designs into public, reusable datasets and encoding models that challenge prevailing theories of vision and pain.
9 years of coding experience
5 years of employment as a software developer
Master of Science (M.S.), Cognitive Psychology; Social Decision Neuroscience, Master of Science (M.S.), Cognitive Psychology; Social Decision Neuroscience at Korea University
Doctor of Philosophy - PhD, Cognitive Neuroscience, Doctor of Philosophy - PhD, Cognitive Neuroscience at Dartmouth College
Psychology and Neuroscience PhD program; Cognitive Neuroscience; Institute of Cognitive Science, Psychology and Neuroscience PhD program; Cognitive Neuroscience; Institute of Cognitive Science at University of Colorado Boulder