Keith Dillon, Ph.D.
Ph.D. Electrical Engineering, University of California, San Diego
M.S. Electrical Engineering, Rice University
B.S. Electrical Engineering and Physics, Tulane University
Dr. Keith Dillon has over ten years of experience in industry, where he has brought powerful new data science technology to new and exotic imaging and sensing applications. He started out in reconnaissance imaging at Hughes Aircraft Company, before moving into embedded and medical devices at a series of startups. In 2008 he co-founded Formulens, which licenses data-driven algorithms to produce personalized spectacle lenses, and which continues to be used by manufacturers worldwide. Dr. Dillon received his doctorate from UCSD and did Postdoctoral research at Columbia University and Tulane University.
My research focus is on exploiting data from emerging imaging and sensing systems, to address complex disease. Conventional research methods, targeting common features in group studies, are poorly-equipped to handle the heterogeneity of such diseases. The result is high variability in diagnoses, low repeatability of study results, and disparities in care. I extend data science methods to real sensor data from new sources which offer the capability to address disease on an individual basis.
K Dillon, YP Wang, “A regularized clustering approach to brain parcellation from functional MRI data”, Wavelets and Sparsity XVII 10394, 103940E, 2017
K Dillon, V Calhoun, YP Wang , “A robust sparse-modeling framework for estimating schizophrenia biomarkers from fMRI”, Journal of Neuroscience Methods 276, 46-55, 2017
K Dillon, Y Fainman, “Element-wise uniqueness, prior knowledge, and data-dependent resolution”, Signal, Image and Video Processing 11 (1), 41-48, 2017
K Dillon, “Fast and robust estimation of ophthalmic wavefront aberrations”, Journal of biomedical optics 21 (12), 121511, 2016
K Dillon, YP Wang , “An image resolution perspective on functional activity mapping”, Engineering in Medicine and Biology Society (EMBC), 2016 IEEE 38th Annual Conference, 201
K Dillon, YP Wang , “On efficient meta-filtering of big data”, Engineering in Medicine and Biology Society (EMBC), 2016 IEEE 38th Annual Conference, 2016
K Dillon, Y Fainman, YP Wang, “Computational estimation of resolution in reconstruction techniques utilizing sparsity, total variation, and nonnegativity”, Journal of Electronic Imaging 25 (5), 053016, 2016
K Dillon, YP Wang, “Imposing uniqueness to achieve sparsity”, Signal processing 123, 1-8, 2016
“Method of designing progressive addition lenses”, 2015, US Patent 8,992,013
“Ophthalmic diagnostic instrument”, 2012, US Patent 8,684,527
“Method and system for testing algorithm compliancy”, 2005, US Patent 6,856,953