2021 - Jack S. Kilby Signal Processing Medal For groundbreaking contributions to compressed sensing.
2018 - IEEE Fellow For contributions to compressive sensing
His main research concerns Compressed sensing, Algorithm, Convex optimization, Signal reconstruction and Combinatorics. His research integrates issues of Probleme inverse, Orthogonal matrix and Iterative reconstruction, Computer vision in his study of Compressed sensing. His Algorithm study incorporates themes from Homotopy, Wavelet, Generalization error and Nonlinear system.
His Convex optimization research focuses on subjects like Data compression, which are linked to Image compression and Wavelet transform. His Combinatorics research includes themes of Discrete mathematics, Sign, Random matrix and Mathematical statistics. His studies deal with areas such as Fourier series and Piecewise as well as Free probability.
Justin Romberg focuses on Algorithm, Mathematical optimization, Compressed sensing, Convex optimization and Artificial intelligence. His study in Algorithm is interdisciplinary in nature, drawing from both Signal, Signal reconstruction, Signal processing and Communication channel. While the research belongs to areas of Compressed sensing, Justin Romberg spends his time largely on the problem of Bandwidth, intersecting his research to questions surrounding Mathematical analysis.
Justin Romberg has included themes like Linear matrix inequality, Combinatorics and Nonlinear system in his Convex optimization study. The concepts of his Combinatorics study are interwoven with issues in Discrete mathematics, Upper and lower bounds and Random matrix. His research in Artificial intelligence intersects with topics in Computer vision and Pattern recognition.
His primary areas of study are Reinforcement learning, Applied mathematics, Algorithm, Compressed sensing and Artificial neural network. His Reinforcement learning research incorporates themes from Mathematical optimization, Markov decision process, Markov process and Markov chain. His Applied mathematics study integrates concerns from other disciplines, such as Random matrix, Inverse problem, Blind deconvolution, Estimator and Series.
His Algorithm research is multidisciplinary, incorporating perspectives in Sampling and Array processing. Justin Romberg usually deals with Compressed sensing and limits it to topics linked to Regular polygon and Artificial intelligence. Within one scientific family, Justin Romberg focuses on topics pertaining to Computer hardware under Artificial neural network, and may sometimes address concerns connected to Shift register, Energy, Linear feedback shift register and Sparse matrix.
Justin Romberg mostly deals with Reinforcement learning, Applied mathematics, Artificial intelligence, Markov chain and Optimization problem. Reinforcement learning is closely attributed to Algorithm in his work. His Applied mathematics research is multidisciplinary, incorporating elements of Stochastic gradient descent, Markov process and Quantization.
His work on Range as part of general Artificial intelligence research is frequently linked to Baseline, bridging the gap between disciplines. His Markov chain research includes elements of Ergodic theory, Sampling, Sample and Trajectory. Justin Romberg has researched Optimization problem in several fields, including Point, Position and Control theory.
This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.
Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information
E.J. Candes;J. Romberg;T. Tao.
IEEE Transactions on Information Theory (2006)
Stable signal recovery from incomplete and inaccurate measurements
Emmanuel J. Candès;Justin K. Romberg;Terence Tao.
Communications on Pure and Applied Mathematics (2006)
Sparsity and incoherence in compressive sampling
Emmanuel Candès;Justin Romberg.
Inverse Problems (2007)
Imaging via Compressive Sampling
IEEE Signal Processing Magazine (2008)
Beyond Nyquist: Efficient Sampling of Sparse Bandlimited Signals
J.A. Tropp;J.N. Laska;M.F. Duarte;J.K. Romberg.
IEEE Transactions on Information Theory (2010)
Quantitative Robust Uncertainty Principles and Optimally Sparse Decompositions
Emmanuel J. Candes;Justin Romberg.
Foundations of Computational Mathematics (2006)
Compressive Sensing by Random Convolution
Siam Journal on Imaging Sciences (2009)
Practical Signal Recovery from Random Projections
Blind Deconvolution Using Convex Programming
Ali Ahmed;Benjamin Recht;Justin Romberg.
IEEE Transactions on Information Theory (2014)
Signal recovery from random projections
Emmanuel J. Candes;Justin K. Romberg.
electronic imaging (2005)
Profile was last updated on December 6th, 2021.
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