Daniel Wang

Daniel Wang is currently a Research Assistant at The Johns Hopkins University, where a notable achievement includes the discovery of a computational biomarker from intracranial electroencephalography (iEEG) data that predicts surgical outcomes for patients with drug-resistant epilepsy, with a first-author journal publication pending. As President of the Johns Hopkins Undergraduate Brain-Computer Interface Society, Daniel developed a machine learning pipeline for translating neural and muscle signals into commands for a hybrid brain-computer interface, presenting findings at the 2024 BMES Conference. Previous roles include Software Engineer at the University of Washington, where a real-time EEG data streaming pipeline was engineered, and Research and Development Engineer at ThermOptik LLC, focusing on novel medical device components. Early work at the Massachusetts Institute of Technology involved mentoring students in designing assistive technologies for individuals with disabilities. Daniel is pursuing a Master of Science in Mathematics and Statistics at The Johns Hopkins University, building on a Bachelor of Science in the same field.