James Vincent Candy

James Vincent Candy, PhD, is the Chief Scientist for Engineering, a Distinguished Member of the Technical Staff, founder and former Director of the Center for Advanced Signal & Image Sciences (CASIS) at the Lawrence Livermore National Laboratory and an Adjunct Full-Professor at the University of California, Santa Barbara. He received his his BSEE from the University of Cincinnati along with his MSE and PhD in Electrical Engineering from the University of Florida. Dr. Candy is a Life-Fellow of...See more

James Vincent Candy, PhD, is the Chief Scientist for Engineering, a Distinguished Member of the Technical Staff, founder and former Director of the Center for Advanced Signal & Image Sciences (CASIS) at the Lawrence Livermore National Laboratory and an Adjunct Full-Professor at the University of California, Santa Barbara. He received his his BSEE from the University of Cincinnati along with his MSE and PhD in Electrical Engineering from the University of Florida. Dr. Candy is a Life-Fellow of the IEEE and a 25-Year-Fellow of the Acoustical Society of America (ASA). He was elected as a Life Member at the University of Cambridge (Clare Hall College). Dr. Candy has been awarded the Interdisciplinary Helmholtz-Rayleigh Silver Medal in Signal Processing/Underwater Acoustics by the Acoustical Society of America, the IEEE Distinguished Technical Achievement Award for the development of model-based signal processing as well as an elected IEEE Distinguished Lecturer in Oceanic Signal Processing. He also received the R&D100 award for his innovative invention in radiation threat detection. He has published over 250 journal articles, book chapters, and technical reports as well as written six texts in signal processing: Signal Processing: the Model-Based Approach, (McGraw-Hill, 1986), Signal Processing: the Modern Approach, (McGraw-Hill, 1988), Model-Based Signal Processing, (Wiley/IEEE Press, 2006), Bayesian Signal Processing: Classical, Modern and Particle Filtering (Wiley/IEEE Press, 2009), Bayesian Signal Processing: Classical, Modern and Particle Filtering, 2nd Ed. (Wiley/IEEE Press, 2016), and Model-Based Processing: An Applied Subspace Identification Approach (Wiley, 2019). See less