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Jabez J McClelland (Fed)

Group Leader

Jabez McClelland is the Group Leader of the Alternative Computing Group in the Nanoscale Device Characterization Division of the Physical Measurement Laboratory (PML). He received a B.A. in Physics and Music from Wesleyan University, and M.A. and Ph.D. degrees in Physics from the University of Texas at Austin. During his graduate studies he spent a year as a Fulbright Fellow at the Freie Universität Berlin. Jabez came to NIST as a postdoctoral research associate in the Electron Physics Group, where he subsequently served as a research physicist and became Group Leader in 2006.  In 2018 he assumed leadership of the Alternative Computing Group.  His research at NIST has covered several topics, including spin-polarized low energy electron scattering from laser excited atoms, laser focused atomic deposition, metastable-atom lithography, deterministic single-atom sources, cold-atom ion sources, and neuromorphic computing.  He has over 139 publications, is a frequent invited speaker at international meetings, holds nine patents, and is considered one of the world experts on atom optics. He currently leads projects on focused ion beam sources based on laser cooled atoms and measurements for neuromorphic information processing.

Projects

Selected Publications

Awards

Jacob Rabinow Applied Research Award (2019)

Microscopy Today Innovation Award (2015)

US Dept. of Commerce Gold Medal (2006)

Fellow, Optical Society of America (2004)

Fellow, American Physical Society (1998)

US Dept. of Commerce Silver Medal (1992)

NBS Chapter of Sigma Xi Award for Excellence in Science (1988)

Publications

Implementation of a Binary Neural Network on a Passive Array of Magnetic Tunnel Junctions

Author(s)
Jonathan Goodwill, Nitin Prasad, Brian Hoskins, Matthew Daniels, Advait Madhavan, Lei Wan, Tiffany Santos, Michael Tran, Jordan Katine, Patrick Braganca, Mark Stiles, Jabez J. McClelland
The increasing scale of neural networks and their growing application space have produced a demand for more energy and memory efficient artificial-intelligence

A System for Validating Resistive Neural Network Prototypes

Author(s)
Brian Hoskins, Mitchell Fream, Matthew Daniels, Jonathan Goodwill, Advait Madhavan, Jabez J. McClelland, Osama Yousuf, Gina C. Adam, Wen Ma, Muqing Liu, Rasmus Madsen, Martin Lueker-Boden
Building prototypes of heterogeneous hardware systems based on emerging electronic, magnetic, and photonic devices is an increasingly important area of research

Patents

DRY PRELITHIATION OF ELECTRODE MATERIALS FOR LITHIUM BATTERIES AND SUPERCAPACITORS BY CONTROLLED DOSE LOW ENERGY IMPLANTATION OF LITHIUM IONS

NIST Inventors
Christopher Soles , Vladimir Oleshko and Jabez J McClelland
patent description The invention is a circuit family useful for neuromorphic computing as well as single-photon imaging. The circuits perform multiple functions. One function is to transduce single photons to one or more fluxons for subsequent processing. This transducer circuit can be used to apply
Created October 9, 2019, Updated October 24, 2022