Mitsubishi Electric Research Laboratories
201 Broadway, 8th Floor
Cambridge, MA 02139, USA
June 2016: A talk on "Trainable iterative algorithms for computational sensing" at NYU Tandon School of Engineering.
June 2016: The manuscript "A Recursive Born Approach to Nonlinear Inverse Scattering" was accepted to IEEE Signal Processing Letters.
May 2016: Three interns joined the sensing team at MERL: Hsiou-Yuan Liu from UC Berkeley, Maxim Goukhshtein from University of Toronto, and Yanting Ma from North Carolina State University. Welcome!
Ulugbek S. Kamilov received his Ph.D. degree from the department of Electrical Engineering, École Polytechnique Fédérale de Lausanne (EPFL), Switzerland, in 2015.
Since 2015, he has been a Research Scientist with Mitsubishi Electric Research Laboratories (MERL), Cambridge, MA, USA. His research develops computational techniques for solving inverse problems for applications in biomedical or industrial imaging. Prior to joining MERL, Ulugbek was an Exchange Student with Carnegie Mellon University (CMU), Pittsburgh, PA, USA, in 2007, a Visiting Student at Massachusetts Institute of Technology (MIT), Cambridge, MA, USA, in 2010, and a Visiting Student Researcher at Stanford University, Stanford, CA, USA, in 2013. His research interests include statistical estimation, signal and image processing, computational imaging, and machine learning.
I develop new methods for computational sensing and imaging. Specifically, my work covers three areas: (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, etc.
I continuously study various 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 of high interest to me.
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]