David Brady

  • Faculty Affiliation: OSC Professor
  • Research Specialty: Image Science
J.W. and H.M. Goodman Endowed Chair in Optical Sciences
Professor of Optical Sciences
Email Address: 
djbrady@email.arizona.edu
Office Location: 
Meinel 429
Phone Number: 
520-626-1778
Education: 
  • Ph.D., California Institute of Technology, Pasadena, 1990
  • M.S., California Institute of Technology, Pasadena, 1986
  • B.S., Macalester College, Saint Paul, 1984
Employment: 
  • J.W. and H.M. Goodman Endowed Chair in Optical Sciences, University of Arizona, Wyant College of Optical Sciences, 2021-present
  • Michael J. Fitzpatrick Professor of Photonics: Duke University, Pratt School of Engineering, 2009-2020
  • Professor of Electrical and Computer Engineering: Duke Kunshan University, 2016-2019
  • Addy Family Professor of Electrical & Computer Engineering: Duke University, Pratt School of Engineering, 2002-2007
  • Director: Duke University, Fitzpatrick Institute for Photonics, 2001-2005
  • Professor of Electrical and Computer Engineering: Duke University, Pratt School of Engineering, 2001-2020
  • Professor of Electrical and Computer Engineering: University of Illinois, Grainger College of Engineering, 1990-2000
  • Co-Founder and Chairman: Aqueti, 2012-present
  • Chairman: Applied Quantum Technologies, 2008-present
  • Founder & Chief Scientist: Blue Angel Optics Corporation, 2006-2008
  • Founder & Chief Scientist: Centice Corporation, 2003-2013
Awards and Honors: 
  • SPIE: Dennis Gabor Award. 2013
  • IEEE: Fellow, 2009
  • SPIE: Fellow, 2007
  • OSA: Fellow, 2003
Research Summary: 

Brady focuses on computational imaging. Brady led the joint Duke University and University of Arizona team that built the world’s first gigapixel camera in 2012. His subsequent work has focused on reducing the size, weight, power and cost of gigapixel cameras while also improving depth of field, color fidelity, frame frame rate and other measures of image quality. Brady’s current work focuses on aperture synthesis and interferometric super-resolution techniques to continue to push the physical limits of optical sensing. His lab relies heavily on artificial neural networks for camera system control, data management and image estimation. He has also pioneered compressive tomographic imaging systems for efficient hyperspectral, high frame rate, x-ray and millimeter wave imaging.

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