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John G. Harris

Dean | College of Engineering and Science

Professor | College of Engineering and Science: Department of Electrical Engineering and Computer Science

Contact Information

Expertise

bio-inspired computation, neuromorphic engineering, natural language processing, machine learning, spike-based hardware.

Personal Overview

Dr. Harris joined Florida Tech as the Dean of the College of Engineering and Science on July 1, 2022. Previously, he was at the University of Florida (UF) for 29 years and Chair of the Electrical and Computer Engineering (ECE) Department for the last 13 years. The UF ECE Department had unprecedented success under his leadership. Department research expenditures more than doubled, reaching a historic high of $25.6M last fiscal year. Faculty numbers grew by 50%, to 55 tenured/tenure-track faculty with a significant improvement in faculty diversity. The EE graduate program ranking rose by 10 places to #25 among all universities. For his research, Dr. Harris develops biologically inspired circuits, architectures, and algorithms for signal processing. He has published over 200 research papers and patents in this area, and graduated 36 PhD students and 21 MS students during his career.

Educational Background

Ph.D. in Computation & Neural Systems, Caltech, 1991.

M.S. in Electrical Engineering, MIT, 1986

B.S. in Electrical Engineering, MIT, 1983

 

Selected Publications

 

Representative Research Publications:

1.X. Guo, X. Qi, and J.G. Harris. A time-to-first-spike imager. IEEE Sensors Journal, 7(8):1165–1175, Aug 2007.
 
2.S.C. Liu, J.G. Harris, M. Elhilali, and M. Slaney. Editorial for bio-inspired audio processing, models and systems. Frontiers in neuroscience, 13:978, 2019.
 
3.E. Latorre-Navarro and J.G. Harris. An intelligent natural language conversational system for academic advising. International Journal of Advanced Computer Science and Applications, 6, 02 2015.

Research

There is growing interest in using biological-inspiration to improve the design of computation systems, particularly in the areas of sensory processing and pattern recognition where biological systems outperform the best man-made devices. Biological computation systems exhibit amazing performance and are compact, ultra-low power and incredibly fault tolerant.

We have designed many biologically-inspired engineering systems including methods for improving cell-phone speech quality, a new type of digital camera with much wider dynamic range, an implant for wirelessly recording neural signals for brain-machine interfaces and a general computer architecture for spike-based computation. In each case, the study of biology leads to natural and effective engineering solutions that can be implemented in software, FPGAs, or in special-purpose analog-mixed signal VLSI circuits.

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