Jonathan Zia M.D., Ph.D.
Professional Tinkerer
I am a neurology resident at Stanford University. I graduated from Emory University School of Medicine (M.D.) in 2022 and earned my Ph.D. in Electrical and Computer Engineering from Georgia Tech in 2020. Previously, I graduated from Arizona State University in 2016 with a BSE in Electrical Engineering. I am interested in medical applications of electrical engineering and computer science.
Key Experience
July 2022 - Present
Resident Physician
Neurology resident at Stanford University, currently completing internship in internal medicine.
Jan 2018 - Aug 2020
(Oct 2021 - May 2022)
GRA
(Post-Doc Researcher)
Graduate research assistant (GRA) in the Inan Research Lab of the Department of Electrical and Computer Engineering. Research focused on using machine learning to create next-generation devices for physiological monitoring.
Oct 2013 - May 2016
Undergraduate Researcher
Undergraduate researcher in the Center for Cognitive Ubiquitous Computing (CUbiC) of the Ira A. Fulton Schools of Engineering. Research focused on artificial intelligence for freezing of gait (FoG) prediction in Parkinson's patients.
Key Education
July 2016 - May 2022
MSTP Program
Earned MD from Emory University as part of the Medical Scientist Training Program (MSTP), an NIH-funded program for training physician-scientists.
Jan 2018 - Aug 2020
PhD
Earned PhD from the Georgia Institute of Technology in the lab of Dr. Omer Inan. Research focused on applications of machine learning to cardio-mechanical signal analysis.
Aug 2012 - May 2016
Undergraduate
BSEE in Electrical Engineering from Arizona State University. Also completed pre-medical coursework and the Chinese Language Flagship Program non-diploma track.
Key Publications
07/2020
Enabling the assessment of trauma-induced hemorrhage via smart wearable systems
Science Advances
09/2021
US 2021/0275058 A1
Systems and methods for automated localization of wearable cardiac monitoring systems and sensor position-independent hemodynamic inference
08/2020
On the assessment of cardiomechanical function via wearable sensing systems