2017 - Fellow of the American Academy of Arts and Sciences
2016 - Fellow, National Academy of Inventors
2009 - Fellow of the American Association for the Advancement of Science (AAAS)
The scientist’s investigation covers issues in Compressed sensing, Algorithm, Artificial intelligence, Computer vision and Signal processing. His Compressed sensing research integrates issues from Signal reconstruction, Quantization, Sparse matrix, Signal and Iterative reconstruction. His Algorithm research incorporates elements of Information theory, Speech recognition, Theoretical computer science and Mathematical optimization.
His work investigates the relationship between Artificial intelligence and topics such as Pattern recognition that intersect with problems in Greedy algorithm and Contextual image classification. His Signal processing research also works with subjects such as
Richard G. Baraniuk focuses on Artificial intelligence, Algorithm, Compressed sensing, Wavelet and Computer vision. His research in Artificial intelligence intersects with topics in Machine learning and Pattern recognition. In his research, Time–frequency analysis is intimately related to Signal processing, which falls under the overarching field of Algorithm.
The various areas that Richard G. Baraniuk examines in his Compressed sensing study include Sampling, Signal, Signal reconstruction, Quantization and Electronic engineering. His Wavelet study frequently draws connections between adjacent fields such as Mathematical analysis. His study in Data compression and Image is carried out as part of his Computer vision studies.
His scientific interests lie mostly in Artificial intelligence, Algorithm, Machine learning, Compressed sensing and Spline. The concepts of his Artificial intelligence study are interwoven with issues in Computer vision and Pattern recognition. His study in Computer vision is interdisciplinary in nature, drawing from both Lens and Sampling.
Richard G. Baraniuk studies Computation which is a part of Algorithm. The Machine learning study combines topics in areas such as Variety and Representation. His Compressed sensing study incorporates themes from Signal processing, Convolutional neural network, Random projection and Convex optimization.
Richard G. Baraniuk mostly deals with Algorithm, Artificial intelligence, Compressed sensing, Convolutional neural network and Deep learning. His studies deal with areas such as Theoretical computer science, Training set, Tangent, Phase retrieval and Bandwidth as well as Algorithm. The Artificial intelligence study combines topics in areas such as Machine learning, Key and Computer vision.
His Computer vision research incorporates themes from Sampling, Signal and Signal processing. His work deals with themes such as Quantization, Thresholding, Convex optimization, Transformation and Pattern recognition, which intersect with Compressed sensing. His Deep learning study combines topics in areas such as Overfitting, Metric, Spline, Cluster analysis and Wavelet.
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.
Compressive Sensing [Lecture Notes]
IEEE Signal Processing Magazine (2007)
conference on information sciences and systems (2008)
Single-Pixel Imaging via Compressive Sampling
M.F. Duarte;M.A. Davenport;D. Takhar;J.N. Laska.
IEEE Signal Processing Magazine (2008)
The dual-tree complex wavelet transform
I.W. Selesnick;R.G. Baraniuk;N.C. Kingsbury.
IEEE Signal Processing Magazine (2005)
A Simple Proof of the Restricted Isometry Property for Random Matrices
Richard G. Baraniuk;Mark A. Davenport;Ronald A. DeVore;Michael B. Wakin.
Constructive Approximation (2008)
Wavelet-based statistical signal processing using hidden Markov models
M.S. Crouse;R.D. Nowak;R.G. Baraniuk.
IEEE Transactions on Signal Processing (1998)
Model-Based Compressive Sensing
R.G. Baraniuk;V. Cevher;M.F. Duarte;C. Hegde.
IEEE Transactions on Information Theory (2010)
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)
Compressive Radar Imaging
R. Baraniuk;P. Steeghs.
ieee radar conference (2007)
pathChirp: Efficient available bandwidth estimation for network paths
Vinay J. Ribeiro;Jiri Navratil;Rudolf H. Riedi;Richard G. Baraniuk.
Proc. Passive and Active Measurements Workshop, Apr. 2003 (2003)
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