Jonathan Zia
Neurology Resident at Stanford University
Palo Alto, California, United States
Summary
👤
Senior🎓
Top SchoolJonathan Zia is a neurology resident at Stanford Healthcare who blends clinical training with a decade of engineering and research experience at the intersection of medicine, AI, and biomedical devices. With a PhD in Electrical Engineering from Georgia Tech and an MD from Emory’s MSTP, he has applied machine learning to noninvasive cardiovascular monitoring and worked on neural prosthetics and human-centered healthcare systems. He’s also consulted for a Silicon Valley startup accelerator, bringing product and business sensibility to translational research. A self-described "professional tinkerer," Jonathan is comfortable moving between bedside care, lab experiments, and prototype development, and is fluent in Mandarin—an asset for cross-cultural collaborations.
10 years of coding experience
5 years of employment as a software developer
Barrett, the Honors College, B.S.E. Electrical Engineering; Pre-Medical Studies; Chinese Language Flagship Program, Barrett, the Honors College, B.S.E. Electrical Engineering; Pre-Medical Studies; Chinese Language Flagship Program at Arizona State UniversityMandarin Chinese, Mandarin Chinese at East China Normal UniversityBiomedical Engineering, Biomedical Engineering at National University of SingaporeDoctor of Medicine (M.D.), Medical Scientist Training Program, Doctor of Medicine (M.D.), Medical Scientist Training Program at Emory University School of MedicineDoctor of Philosophy (Ph.D.), Electrical Engineering, Doctor of Philosophy (Ph.D.), Electrical Engineering at Georgia Institute of Technology
Github Skills (12)
lstm10
autoencoder10
nonlinear-dynamics9
time-series9
unsupervised-learning9
sequence-to-sequence8
cocoapods8
tensorflow8
matlab6
factor-analysis5
reinforcement-learning2
online-game1
Programming languages (3)
Github contributions (5)
jonzia/WordnetDataCapture
Jun 2016 - Mar 2021
Contributions:10 commits, 8 pushes, 1 branch in 4 years 9 months
jonzia/Recurrent_Autoencoder
Mar 2018 - Dec 2021
Sequence-to-sequence autoencoder for unsupervised learning of nonlinear dynamics (Tensorflow).
Contributions:32 commits, 29 pushes, 1 branch in 3 years 10 months
autoencodernonlinear-dynamicssequence-to-sequencetensorflowunsupervised-learning
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