Mitsubishi Electric Research Laboratories
201 Broadway, 8th Floor
Cambridge, MA 02139, USA
September 2016: The manuscript "Inference for Generalized Linear Models via Alternating Directions and Bethe Free Energy Minimization" was accepted to IEEE Transactions on Information Theory.
August 2016: The special sessing proposal for ICASSP 2017 on "Large-Scale Computational Imaging with Wave Models" was accepted. The session is organized jointly with Laura Waller and Brendt Wohlberg.
August 2016: Delivered two keynote talks at iTWIST 2016: "Learning MMSE Optimal Thresholds for FISTA" and "Minimizing Isotropic Total Variation without Subiterations."
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 and sensing with an emphasis on the development and analysis of large-scale computational techniques for applications in biomedical and industrial imaging. His research interests cover signal processing aspects of multimodal sensor fusion, tomographic imaging, machine learning, through-the-wall imaging, and distributed sensing. He has co-authored 14 journal and 27 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.
Development of new methods for computational imaging and sensing. The goal is to better understand all three important areas for computational imaging: (a) study of the physical measuremnt processes; (b) design of an inference algorithm that can turn the measurements into the desired image; (c) theoretical analysis of the algorithms. My work has been directly applied to various acquisition modalities such as optical tomographic microscopy, magnitic resonance imaging, radar, distributed sensing, etc.
I continuously study various theoretical tools that would allow me to perform inference on large dimensional data in a more efficient way. In particular, probabilistic inference algorithms, machine learning techniques, and optimization methods are my immediate interest areas.
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, 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]