Ulugbek S. Kamilov

Research Scientist

Computational Sensing
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.

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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.

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