Synaptic Connectivity Neuron Classification | JAIST NTAC | AcademicJobs
JAIST researchers introduce NTAC, classifying neurons solely via synaptic wiring with 90%+ accuracy on fly connectomes, advancing connectomics.

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Gregory Schwartzman is an Associate Professor in the School of Information Science at the Japan Advanced Institute of Science and Technology (JAIST). He earned his Ph.D. from the Technion-Israel Institute of Technology. Prior to joining JAIST, he served as a postdoctoral researcher at the National Institute of Informatics (NII) in Tokyo.
Schwartzman’s research focuses on graph algorithms and distributed computing within theoretical computer science. His work addresses algorithmic challenges in models such as streaming and distributed networks, with applications to big data and decentralized systems. He has authored numerous publications in leading venues including PODC, DISC, SODA, SPAA, ESA, ICLR, and ICML, covering topics such as distributed approximations for vertex cover and maximum independent set, semi-streaming matching algorithms, and dynamic network algorithms. Notable contributions include papers on optimal distributed covering algorithms and fast distributed algorithms for testing graph properties. Schwartzman has received best paper and best student paper awards at conferences such as PODC and SODA. He serves on program committees for conferences including PODC, DISC, and OPODIS, and as a reviewer for journals and venues in algorithms and distributed computing. He is supported by a JSPS KAKENHI grant and maintains active collaborations in the field.
JAIST researchers introduce NTAC, classifying neurons solely via synaptic wiring with 90%+ accuracy on fly connectomes, advancing connectomics.