I research data-driven and numerically intensive techniques for emerging sensor data technologies.

Google Scholar: https://scholar.google.com/citations?user=jQ4cGy0AAAAJ&hl=en

Preprints & Reports

  • A first-order optimization method for learning to reconstruct opacity in computational imaging (pdf)
  • Calculus for Deep Learning, with Vectors, Matrices, and a few Tuples (pdf)
  • Focus optimization in a Computational Confocal Microscope (pdf)
  • Feature Level Malware Obfuscation in Deep Learning https://arxiv.org/pdf/2002.05517 


  1. K. Dillon and Y.-P. Wang, “Resolution-based spectral clustering for brain parcellation using functional MRI,”Journal of Neuroscience Methods 335, 108628, 2020.
  2. K. Dillon, V. Calhoun, and Y.-P. Wang, “A robust sparse-modeling framework for estimating schizophrenia biomarkers from fMRI,”Journal of Neuroscience Methods, vol. 276, pp. 46–55, Jan. 2017. (pdf)
  3. K. Dillon, “Fast and robust estimation of ophthalmic wavefront aberrations,” J. Biomed. Opt, vol. 21, no. 12, pp. 121511–121511, 2016. (pdf)
  4. K. Dillon, Y. Fainman, and Y.-P. Wang, “Computational estimation of resolution in reconstruction techniques utilizing sparsity, total variation, and nonnegativity,” J. Electron. Imaging, vol. 25, no. 5, pp. 053016–053016, 2016. (pdf)
  5. K. Dillon and Y.-P. Wang, “Imposing uniqueness to achieve sparsity,” Signal Processing, vol. 123, pp. 1–8, Jun. 2016. (pdf)
  6. K. Dillon and Y. Fainman, “Element-wise uniqueness, prior knowledge, and data-dependent resolution,” SIViP, pp. 1–8, Apr. 2016. (pdf)
  7. K. Dillon and Y. Fainman, “Bounding pixels in computational imaging,” Appl. Opt., vol. 52, no. 10, pp. D55–D63, Apr. 2013. (pdf)
  8. K. Dillon and Y. Fainman, “Depth sectioning of attenuation,” J. Opt. Soc. Am. A, vol. 27, no. 6, pp. 1347–1354, Jun. 2010. (pdf)
  9. K. Dillon and Y. Fainman, “Computational confocal tomography for simultaneous reconstruction of objects, occlusions, and aberrations,” Appl. Opt., vol. 49, no. 13, pp. 2529–2538, May 2010. (pdf)
  10. K. Dillon, “Bilinear wavefront transformation,” J. Opt. Soc. Am. A, vol. 26, no. 8, pp. 1839–1846, 2009. (pdf)


  1. Keith Dillon, “Model-based machine learning for computational reconstruction of opacity and missing information“, Proc. SPIE 12675, Applications of Machine Learning 2023, 126750Z (2023)
  2. Keith Dillon and Jeffrey Chomyn, “Optimization-of-freeform-spectacle-lenses-based-on-high-order-aberrations“, Proc. SPIE 12666, Current Developments in Lens Design and Optical Engineering XXIV, 1266604 (2023) (pdf)
  3. Keith Dillon, “Efficient Partitioning of Partial Correlation Networks“. In: Torsello A., Rossi L., Pelillo M., Biggio B., Robles-Kelly A. (eds) Structural, Syntactic, and Statistical Pattern Recognition. S+SSPR 2021. Lecture Notes in Computer Science, vol 12644. Springer, Cham. 2021 (pdf)
  4. A. Ambrose, K. Dillon, “Robust neural network for wavefront reconstruction using Zernike coefficients“, Applications of Machine Learning 2020 11511, 115110N
  5. Keith Dillon, Yu-Ping Wang, ” A regularized clustering approach to brain parcellation from functional MRI data“, Proc. SPIE 10394, Wavelets and Sparsity XVII, 103940E (2017/08/24) (pdf)
  6. K. Dillon and Y.-P. Wang, “On efficient meta-filtering of big data,” in Engineering in Medicine and Biology Society (EMBC), 2016 IEEE 38th Annual International Conference of the, 2016, pp. 2958–2961. (pdf)
  7. K. Dillon and Y.-P. Wang, “An image resolution perspective on functional activity mapping,” in Engineering in Medicine and Biology Society (EMBC), 2016 IEEE 38th Annual International Conference of the, 2016, pp. 1139–1142. (pdf)
  8. K. J. Dillon and Y. Fainman, “Computational Lightcurve Imaging,” in Computational Optical Sensing and Imaging, 2012, p. CTu4B.3.
  9. K. J. Dillon and Y. Fainman, “Nonlinear Tomographic Imaging of Scattering and Attenuation,” in Frontiers in Optics, 2011, p. FTuL2.
  10. Keith J. Dillon and Yeshaiahu Fainman, “Rejecting out-of-Focus Attenuation,” in Imaging Systems, 2010, p. IWA3.
  11. K. J. Dillon and Y. Fainman, “Computational Confocal Scanning Tomography,” in Frontiers in Optics 2009/Laser Science XXV/Fall 2009 OSA Optics & Photonics Technical Digest, 2009, p. JTuC7.Y.
  12. Liu, L. Warden, K. J. Dillon, G. Mills, and A. W. Dreher, “A novel high-resolution and large-range diffractive wavefront sensor,” Proceedings of SPIE, vol. 6306, no. 1, p. 63060J–63060J–6, Aug. 2006.
  13. Y. Liu, L. Warden, K. Dillon, G. Mills, and A. Dreher, “Z-View diffractive wavefront sensor: principle and applications,” Proceedings of SPIE, vol. 6018, no. 1, pp. 601809-601809–9, Dec. 2005.
  14. K. J. Dillon, B. S. Denney, and R. J. P. de Figueiredo, “Estimating 3D orientation from 1D projections with applications to radar,” Proceedings of SPIE, vol. 5095, no. 1, pp. 216–223, Sep. 2003. (pdf)
  15. B. S. Denney, K. Estabridis, R. J. P. de Figueiredo, and K. J. Dillon, “Some results from scattering-based tomography for HRR and SAR prediction,” presented at the Algorithms for Synthetic Aperture Radar Imagery X, 2003, vol. 5095, pp. 194–205.


Optimization in Computational Imaging and Inverse Problems University of California San Diego 2014