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Keith Jenkins

Professor of Electrical and Computer Engineering


  • Doctoral Degree, Electrical Engineering, University of Southern California
  • Master's Degree, Electrical Engineering, University of Southern California
  • Bachelor's Degree, Physics, California Institute of Technology


B. Keith Jenkins received the B.S. degree with honors in Applied Physics from the California Institute of Technology (1977), and the M.S. (1979) and Ph.D. (1984) degrees in Electrical Engineering from the University of Southern California. He is currently a Professor of Electrical and Computer Engineering at USC, and Program Director for the MS Electrical and Computer Engineering (Machine Learning and Data Science) degree. After receiving the NSF Presidential Young Investigator Award (1988-1993) for work applying photonics to computing systems, his research activities expanded to also include miniaturization of 3-D photonic computing systems, optical and computer holography, neural networks, object and pattern recognition, biologically inspired vision algorithms, and machine learning techniques applied to optical and medical domains.

Research Summary

Experimental and theoretical research in the areas of: parallel computing systems incorporating electronics, photonics, and optics; optical interconnections, including computer generated holographic elements, diffractive optical elements, and volume holographic elements; neural networks for information processing, including optical and photonic implementations, adaptive volume holographic systems, neural learning algorithms and neural-network models for optical implementation; 3-D photonic multichip modules, including multilayer hardware components, neurobiologically inspired vision algorithms for parallel implementation, and techniques for mapping algorithms to hardware; other topics, including modeling of volume holographic recording processes in photopolymer materials, parallel computation models for photonic computing systems, and advanced multidimensional displays; neurobiologically inspired models for early vision algorithms and for learning sparse representations; pattern recognition and biologically inspired techniques for object recognition; machine learning techniques for modeling propagation of optical beams through the atmosphere; and machine learning models for medical applications including predicting patient adherence to treatments for sleep disorders.


  • 2014 Hughes Aircraft Co. Hughes Aircraft Co. Fellowship (1977 - 1979)
  • 2014 Schlumberger Schlumberger Fellowship in Electrical Engineering (1979 - 1980)
  • 2014 Northrop Northrop Assistant Professor of Electrical Engineering (Fall 1987 - Spring 1990)
  • 2014 NSF NSF Presidential Young Investigator Award (Fall 1988 - Spring 1993)
  • Director, Director of the Machine Learning and Data Science (MS MLDS) Program
  • Ming Hsieh Department of Electrical and Computer Engineering - Systems
  • EEB 404A
  • Hughes Aircraft Electrical Engineering Center
  • 3740 McClintock Ave., Los Angeles, CA 90089
  • USC Mail Code: 2564
Contact Information
  • (213) 740-4149