Ulugbek S. Kamilov

PhD candidate

Biomedical Imaging Group
École polytechnique fédérale de Lausanne

BM 4138
Station 17
CH-1015, Lausanne


June 2014: Our manuscript "Variational Justification of Cycle Spinning for Wavelet-Based Solutions of Inverse Problems" was accepted for a publication in IEEE Signal Processing Letter. It was co-authored with Emrah Bostan and Prof. Michael Unser.

May 2014: The paper "Approximate Message Passing with Consistent Parameter Estimation and Applications to Sparse Learning" is now available online. It was co-authored with Prof. Sundeep Rangan, Prof. Alyson Fletcher, and Prof. Michael Unser.

November 2013: My work on consistent cycle spinning and MMSE estimators for Levy processes was featured and acknowledged in the book An Introduction to Sparse Stochastic Processes by Prof. Michael Unser and Dr. Pouya Tafti.

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Ulugbek Kamilov received his M.Sc. degree in Communications Systems from the École Polytechnique Fédérale de Lausanne (EPFL), Switzerland, in 2011.

In 2007-08, he was an exchange student in Electrical and Computer Engineering at Carnegie Mellon University (CMU). In 2008, he worked as a research intern at the Telecommunications Research Center in Vienna, Austria. In 2009, he worked as a software engineering intern at Microsoft. In 2010-11, he was a visiting student in the Research Laboratory of Electronics at Massachusetts Institute of Technology (MIT). In 2013, he was a visiting student researcher in the Information Systems Laboratory at Stanford University.

Since 2011, he is with the Biomedical Imaging Group at EPFL where he is working toward his Ph.D. His research interest lies in signal reconstruction from incomplete or corrupt measurements.

My full CV can be found [here]. For my LinkedIn go here View Ulugbek Kamilov's profile on LinkedIn.

Google Scholar profile: [here]. Researcher ID profile:

Research Interests

I develop statistical models and algorithms for the reconstruction of biomedical images from highly undersampled or distorted measurements. Such problems are common in various image acquisition modalities such as magnitic resonance imaging (MRI), bio-microscopy, astronomy, etc.

I am interested in the development of iterative algorithms that allow to perform inference on large dimensional data in an efficient way. In particular, message-passing algorithms on graphical models are of particularly high interest to me.

The full list of my publications is available on the right.