Jordan Matelsky

Jordan Matelsky is a computational neuroscientist at the Johns Hopkins University Applied Physics Laboratory (JHU/APL), specializing in connectomics, large-scale brain mapping, and machine learning. His research explores how neural circuits process information and how insights from biological networks can advance artificial intelligence and brain emulation.

He earned his B.S. in Neuroscience from Johns Hopkins University in 2016 and his Ph.D. in Bioengineering from the University of Pennsylvania, where he conducted research under Professor Konrad Kording.

His work focuses on developing scalable computational tools for reconstructing, analyzing, and simulating complex neural networks. He has contributed to open-source neuroscience technologies, including BossDB and DotMotif, supporting the analysis of large brain-connectivity datasets. His broader interests include computational medicine, neural data infrastructure, and the development of technologies for understanding and emulating biological intelligence.