Summary
Jonathan Kadmon is an assistant professor and theoretical physicist specializing in neural networks, high-dimensional inference, and the statistical-physics foundations of machine learning. With a Ph.D. in physics and theoretical neuroscience from Hebrew University, postdoctoral and fellowship experience at Stanford and a visiting scholar stint at Harvard, he bridges rigorous theory and data-driven algorithm development to interpret large-scale biological and artificial neural data. His work focuses on understanding how brain networks compute, using mathematical frameworks that yield practical inference tools for increasingly rich neural recordings. Early experience as a C/C++ software developer and in fraud analysis gives him a pragmatic edge in turning theoretical insights into robust, implementable code. Based in Israel, he combines deep interdisciplinary training in physics and biology with a decade of research experience at top institutions.
10 years of coding experience
3 years of employment as a software developer
Bachelor of Science (B.Sc.), Physics and Biology, Bachelor of Science (B.Sc.), Physics and Biology at Tel Aviv University
Doctor of Philosophy (PhD), Physics and theoretical neuroscience, Doctor of Philosophy (PhD), Physics and theoretical neuroscience at The Hebrew University