Mitsubishi Electric Research Laboratories (MERL)
201 Broadway, 8th Floor
Cambridge, MA 02139, USA
March 2017: Two papers were accepted to SPARS 2017: "Learning Convolutional Proximal Filters" (with D. Liu and H. Mansour) and "A Kaczmarz Method for Low Rank Matrix Recovery" (with H. Mansour and O. Yilmaz).
February 2017: A new internship position "MM1077: Large-scale Computational Radar Imaging". Come work with our Computational Sensing team in Cambridge, MA, USA, to develop novel methods for large-scale computational radar imaging.
February 2017: The manuscript "Compressive Imaging with Iterative Forward Models" is the Best Student Paper Award finalist at IEEE ICASSP 2016.
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'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 30 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. Since 2016, Dr. Kamilov is a member IEEE Special Interest Group on Computational Imaging.
My objective is to develop new sensing systems that can image and analyze previously inaccessible information by using large-scale computational imaging. Specifically, I leverage the full power of numerical optimization, machine learning, and statistical inference to get the highest-quality images in the shortest amout of time. The goal is to better understand all 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.
I am currently interested in 3D imaging in scattering media, which I consider essential for the next generation biomedical and industrial sensing systems.
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]