Summary
David Blair is a computational neuroscientist and postdoctoral researcher with eight years of experience applying machine learning, dynamical systems, and network science to neuroimaging and physiological time-series. He develops and validates dynamical models (Hopf oscillators, Kalman filters, dynamic mode decomposition) and entropy-based metrics to uncover biomarkers of psychiatric disorders, with peer-reviewed publications demonstrating increased network entropy in patient groups. Comfortable across MATLAB, Python, and deep learning toolchains, he has built end-to-end analysis pipelines combining ICA/eigendecomposition, supervised and unsupervised learning, and biophysical modeling. Based at Georgia State’s TReNDS center, he is now exploring behavioural domains in psychiatry to bridge mechanistic models with clinical phenotypes—a direction that hints at translating abstract network dynamics into actionable clinical stratifications.
8 years of coding experience
7 years of employment as a software developer
Master of Science (M.Sc.), Biomedical/Medical Engineering, 5.1 / 6.0, Master of Science (M.Sc.), Biomedical/Medical Engineering, 5.1 / 6.0 at Eidgenössische Technische Hochschule Zürich
Doctor of Science, Neuroscience, Doctor of Science, Neuroscience at Universitat Pompeu Fabra
Bachelor of Arts (B.A.), Physics, 3.3 / 4.0, Bachelor of Arts (B.A.), Physics, 3.3 / 4.0 at University of Chicago
English, Spanish, German