Mitsubishi Electric Research Laboratories (MERL)
201 Broadway, 8th Floor
Cambridge, MA 02139, USA
June 2017: Invited talk at OSA Mathematics in Imaging Meeting in San Francisco, CA, USA (26 June 2017 at 4:15 PM).
May 2017: New preprint "SEAGLE: Sparsity-Driven Image Reconstruction under Multiple Scattering".
May 2017: Paper accepted to OSA Advanced Photonics Congress 2017: "Acceleration of FDTD-based Inverse Design Using a Neural Network Approach."
May 2017: Invited talk at Allerton Conference 2017 at the University of Illinois at Urbana-Champaign, IL, USA, in 3-6 October 2017.
Ulugbek S. Kamilov is a Research Scientist in the Computational Sensing team at Mitsubishi Electric Research Laboratories (MERL), Cambridge, MA, USA. Dr. Kamilov obtained his B.Sc. and M.Sc. in Communication Systems, and Ph.D. in Electrical Engineering from the École Polytechnique Fédérale de Lausanne (EPFL), Switzerland, in 2008, 2011, and 2015, respectively. In 2007, he was an Exchange Student at Carnegie Mellon University (CMU), Pittsburgh, PA, USA, in 2010, a Visiting Student at Massachusetts Institute of Technology (MIT), Cambridge, MA, USA, and in 2013, a Visiting Student Researcher at Stanford University, Stanford, CA, USA. Dr. Kamilov is a member IEEE Special Interest Group on Computational Imaging since January 2016.
Dr. Kamilov's research focus is computational imaging with an emphasis on the development and analysis of large-scale computational techniques for biomedical and industrial applications. His research interests cover imaging through scattering media, multimodal imaging, distributed radar sensing, and through-the-wall imaging. He has co-authored 17 journal and 36 conference publications in these areas. His Ph.D. thesis work on Learning Tomography (LT) was selected as a finalist for EPFL Doctorate Awards 2016 and was featured in the "News and Views" section of the Nature magazine.
The goal is to develop new sensing systems for imaging and analyzing previously inaccessible information using large-scale computational imaging. Our research leverages the full power of numerical optimization, machine learning, and statistical inference for forming highest-quality images in the shortest amout of time. We aim to enable new applications by better understanding three important areas for computational imaging: (a) study of the physical measurement processes; (b) design of an inference algorithm that can turn the measurements into the desired image; (c) theoretical analysis of the algorithms.
Current interests include 3D imaging in scattering media, which is essential for the next generation biomedical and industrial sensing systems. Another active research area is improved undestanding of the stability and performance of nonlinear imaging algorithms.
U. S. Kamilov, I. N. Papadopoulos, M. H. Shoreh, A. Goy, C. Vonesch, M. Unser, and D. Psaltis, "Learning Approach to Optical Tomography," Optica, vol. 2, no. 6, pp. 517-522, June 2015. [link] [Nature "News & Views"]
U. S. Kamilov, I. N. Papadopoulos, M. H. Shoreh, A. Goy, C. Vonesch, M. Unser, and D. Psaltis, "Optical tomographic image reconstruction based on beam propagation and sparse regularization," IEEE Trans. Comput. Imag., vol. 2, no. 1, pp. 59-70, March 2016. [link]
U. S. Kamilov, "A Parallel Proximal Algorithm for Anisotropic Total Variation Minimization," IEEE Trans. Image Process., vol. 26, no. 2, pp. 539-548, February 2017. [link]
U. S. Kamilov, S. Rangan, A. K. Fletcher, and M. Unser, "Approximate Message Passing with Consistent Parameter Estimation and Applications to Sparse Learning," IEEE Trans. Inf. Theory., vol. 60, no. 5, pp. 2969-2985, May 2014. [link] [NIPS 2012] [pre-print] [code]
U. S. Kamilov, V. K. Goyal, and S. Rangan, "Message-Passing De-Quantization with Applications to Compressed Sensing," IEEE Trans. Signal Process., vol. 60, no. 12, pp. 6270-6281, December 2012. [link] [pre-print] [gamp]