Publications
Preprints
- Z. Qin, M. B. Wakin, and Z. Zhu, Guaranteed Nonconvex Factorization Approach for Tensor Train Recovery, preprint, 2023.
- Z. Qin, C. Jameson, Z. Gong, M. B. Wakin, and Z. Zhu, Stable Tomography for Structured Quantum States, preprint, 2023.
- A. Lidiak, C. Jameson, Z. Qin, G. Tang, M. B. Wakin, Z. Zhu, and Z. Gong, Quantum state tomography with tensor train cross approximation, preprint, 2022.
- S. Li and M. B. Wakin, Recovery Guarantees for Time-varying Pairwise Comparison Matrices with Non-transitivity, preprint, 2021.
- S. Li, P. Nayeri, and M. B. Wakin, Digital Beamforming Robust to Time-Varying Carrier Frequency Offset, preprint, 2021. (code)
Book Chapters
- M. B. Wakin, Compressive Sensing Fundamentals, in M. Amin (Ed.), Compressive Sensing for Urban Radar, CRC Press, 2014.
Journal Papers
- D. Rosen and M. B. Wakin, Bivariate Retrieval from Intensity of Cross-Correlation, Signal Processing, vol. 215, 109267, February 2024.
- A. Titova, M. B. Wakin, and A. C. Tura, Achieving Robust Compressive Sensing Seismic Acquisition with a Two-Step Sampling Approach, Sensors, vol. 23, no. 23, 9519, 2023.
- Z. Zhu and M. B. Wakin, Time-Limited Toeplitz Operators on Abelian Groups: Applications in Information Theory and Subspace Approximation, Pure and Applied Functional Analysis, vol. 8, no. 2, pp. 775-808, 2023. (authors’ copy)
- Y. Wu, M. B. Wakin, and P. Gerstoft, Gridless DOA Estimation with Multiple Frequencies, IEEE Transactions on Signal Processing, vol. 71, pp. 417-432, 2023. (authors’ copy)
- J. Jayne, M. B. Wakin, and R. Snieder, Green’s function estimation by seismic interferometry from limited frequency samples, Signal Processing, vol. 205, 108863, April 2023.
- M. A. Valdez, A. J. Yuffa, and M. B. Wakin, Compressive Sensing with Wigner D-functions on Subsets of the Sphere, IEEE Transactions on Signal Processing, vol. 70, pp. 5652-5667, 2022. (authors’ copy)
- R. Snieder and M. B. Wakin, When Randomness Helps in Undersampling, SIAM Review, vol. 64, no. 4, pp. 1062-1080, November 2022. (code)
- M. A. Valdez, A. J. Yuffa, and M. B. Wakin, On-grid compressive sampling for spherical field measurements in acoustics, Journal of the Acoustical Society of America, vol. 152, no. 4, October 2022. (authors’ copy)
- S. Li, G. Tang, and M. B. Wakin, Landscape Correspondence of Empirical and Population Risks in the Eigendecomposition Problem, IEEE Transactions on Signal Processing, vol. 70, pp. 2985-2999, 2022. (authors’ copy)
- S. Li, H. Mansour, and M. B. Wakin, Recovery analysis of damped spectrally sparse signals and its relation to MUSIC, Information and Inference: A Journal of the IMA, vol. 11, no. 1, pp. 355-383, March 2022. (authors’ copy, code)
- I. Pawelec, M. Wakin, and P. Sava, Missing trace reconstruction for 2D land seismic data with randomized sparse sampling, Geophysics, vol. 86, no. 3, pp. 1MJ-WA152, May 2021.
- Z. Zhu, Q. Li, G. Tang, and M. B. Wakin, The Global Optimization Geometry of Low-Rank Matrix Optimization, IEEE Transactions on Information Theory, vol. 67, no. 2, pp. 1308-1331, February 2021. (authors’ copy)
- S. Li, D. Gaydos, P. Nayeri, and M. B. Wakin, Adaptive Interference Cancellation Using Atomic Norm Minimization and Denoising, IEEE Antennas and Wireless Propagation Letters, vol. 19, no. 12, pp. 2349-2353, December 2020.
- S. Li, Q. Li, Z. Zhu, G. Tang, and M. B. Wakin, The Global Geometry of Centralized and Distributed Low-rank Matrix Recovery without Regularization, IEEE Signal Processing Letters, vol. 27, pp. 1400-1404, July 15, 2020. (authors’ copy)
- S. Li, M. B. Wakin, and G. Tang, Atomic Norm Denoising for Complex Exponentials with Unknown Waveform Modulations, IEEE Transactions on Information Theory, vol. 66, no. 6, pp. 3893-3913, June 2020. (authors’ copy)
- Y. Xie, M. B. Wakin, and G. Tang, Support Recovery for Sparse Signals With Unknown Non-Stationary Modulation, IEEE Transactions on Signal Processing, vol. 68, February 24, 2020. (authors’ copy, code)
- Y. Xie, M. B. Wakin, and G. Tang, Simultaneous Sparse Recovery and Blind Demodulation, IEEE Transactions on Signal Processing, vol. 67, no. 19, pp. 5184-5199, October 1, 2019. (authors’ copy, code)
- Z. Zhu, D. Soudry, Y. C. Eldar, and M. B. Wakin, The Global Optimization Geometry of Shallow Linear Neural Networks, Journal of Mathematical Imaging and Vision, May 31, 2019. (authors’ copy)
- A. Eftekhari, G. Ongie, L. Balzano, and M. B. Wakin, Streaming Principal Component Analysis From Incomplete Data, Journal of Machine Learning Research, vol. 20, no. 86, pp. 1-62, 2019. (authors’ copy)
- S. Karnik, Z. Zhu, M. B. Wakin, J. Romberg, and M. A. Davenport, The Fast Slepian Transform, Applied and Computational Harmonic Analysis, vol. 46, no. 3, pp. 624-652, May 2019. (authors’ copy, code)
- A. Eftekhari, M. B. Wakin, P. Li, and P. G. Constantine, Randomized Learning of the Second-moment Matrix of a Smooth Function, Foundations of Data Science, vol. 1, no. 3, pp. 329-387, 2019. (authors’ copy)
- Z. Zhu, S. Karnik, M. B. Wakin, M. A. Davenport, and J. Romberg, ROAST: Rapid Orthogonal Approximate Slepian Transform, IEEE Transactions on Signal Processing, vol. 66, no. 22, pp. 5887-5901, November 15, 2018. (authors’ copy)
- A. Eftekhari, M. B. Wakin, and R. A. Ward, MC^2: A Two-Phase Algorithm for Leveraged Matrix Completion, Information and Inference: A Journal of the IMA, vol. 7, no. 3, pp. 581-604, 19 September 2018. (authors’ copy)
- Z. Zhu, Q. Li, G. Tang, and M. B. Wakin, Global Optimality in Low-rank Matrix Optimization, IEEE Transactions on Signal Processing, vol. 66, no. 13, pp. 3614-3628, July 2018. (authors’ copy)
- A. Eftekhari, D. Yang, and M. B. Wakin, Weighted Matrix Completion and Recovery with Prior Subspace Information, IEEE Transactions on Information Theory, vol. 64, no. 6, pp. 4044–4071, June 2018. (authors’ copy)
- S. Li, D. Yang, G. Tang, and M. B. Wakin, Atomic Norm Minimization for Modal Analysis from Random and Compressed Samples, IEEE Transactions on Signal Processing, vol. 66, no. 7, pp. 1817-1831, April 1, 2018. (authors’ copy, code)
- A. Eftekhari, H. L. Yap, M. B. Wakin, and C. J. Rozell, Stabilizing Embedology: Geometry-Preserving Delay-Coordinate Maps, Physical Review E, vol. 97, no. 2, pp. 022222, February 2018. (authors’ copy)
- Z. Zhu, S. Karnik, M. A. Davenport, J. Romberg, and M. B. Wakin, The Eigenvalue Distribution of Discrete Periodic Time-Frequency Limiting Operators, IEEE Signal Processing Letters, vol. 25, no. 1, pp.95–99, January 2018. (authors’ copy)
- Z. Pang, M. Yuan, and M. B. Wakin, A random demodulation architecture for sub-sampling acoustic emission signals in structural health monitoring, Journal of Sound and Vibration, vol. 431, pp. 390-404, 2018.
- Z. Zhu and M. B. Wakin, Approximating Sampled Sinusoids and Multiband Signals Using Multiband Modulated DPSS Dictionaries, Journal of Fourier Analysis and Applications, vol. 23, no. 6, pp. 1263-1310, December 2017. (authors’ copy)
- A. Eftekhari, L. Balzano, and M. B. Wakin, What to Expect When You Are Expecting on the Grassmannian, IEEE Signal Processing Letters, vol. 24, no. 6, pp. 872-876, June 2017. (authors’ copy)
- Z. Zhu and M. B. Wakin, On the Asymptotic Equivalence of Circulant and Toeplitz Matrices, IEEE Transactions on Information Theory, vol. 63, no. 5, pp. 2975-2992, May 2017. (authors’ copy)
- A. Eftekhari and M. B. Wakin, What Happens to a Manifold Under a Bi-Lipschitz Map?, Discrete & Computational Geometry, vol. 57, no. 3, pp. 641-673, April 2017. (authors’ copy)
- L. Wiencke, V. Rizi, M. Will, C. Allen, A. Botts, M. Calhoun, B. Carande, J. Claus, M. Coco, L. Emmert, S. Esquibel, A. F. Grillo, L. Hamilton, T. J. Heid, M. Iarlori, H.-O. Klages, M. Kleifges, B. Knoll, J. Koop, H.-J. Mathes, A. Menshikov, S. Morgan, L. Patterson, S. Petrera, S. Robinson, C. Runyan, J. Sherman, D. Starbuck, M. Wakin, and O. Wolf, Joint elastic side-scattering LIDAR and Raman LIDAR measurements of aerosol optical properties in south east Colorado, Journal of Instrumentation, vol. 12, March 2017.
- R. G. Baraniuk, T. Goldstein, A. C. Sankaranarayanan, C. Studer, A. Veeraraghavan, and M. B. Wakin, Compressive Video Sensing: Algorithms, Architectures, and Applications, in IEEE Signal Processing Magazine, vol. 34, no. 1, pp. 52-66, January 2017.
- D. Yang, G. Tang, and M. B. Wakin, Super-Resolution of Complex Exponentials from Modulations with Unknown Waveforms, IEEE Transactions on Information Theory, vol. 62, no. 10, pp. 5809-5830, October 2016. (authors’ copy)
- M. Babakmehr, M. G. Simoes, M. B. Wakin, A. Al Durra, and F. Harirchi, Smart-Grid Topology Identification Using Sparse Recovery, IEEE Transactions on Industry Applications, vol. 52, no. 5, pp. 4375-4384, September-October 2016.
- M. Babakmehr, M. G. Simoes, M. B. Wakin, and F. Harirchi, Compressive Sensing-Based Topology Identification for Smart Grids, IEEE Transactions on Industrial Informatics, vol. 12, no. 2, pp. 532-543, April 2016.
- M. J. Rubin, M. B. Wakin, and T. Camp, Lossy Compression for Wireless Seismic Data Acquisition, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing (JSTARS), vol. 9, no. 1, pp. 236-252, January 2016.
- A. Eftekhari and M. B. Wakin, New Analysis of Manifold Embeddings and Signal Recovery from Compressive Measurements, Applied and Computational Harmonic Analysis, vol. 39, no. 1, pp. 67-109, July 2015. (authors’ copy)
- C. W. Lim and M. B. Wakin, Compressive Temporal Higher Order Cyclostationary Statistics, IEEE Transactions on Signal Processing, vol. 63, no. 11, pp. 2942-2956, June 2015. (See also companion technical report)
- A. Eftekhari, H. L. Yap, C. J. Rozell, and M. B. Wakin, The Restricted Isometry Property for Random Block Diagonal Matrices, Applied and Computational Harmonic Analysis, vol. 38, no. 1, pp. 1-31, January 2015. (authors’ copy)
- B. M. Sanandaji, M. B. Wakin, and T. L. Vincent, Observability with Random Observations, IEEE Transactions on Automatic Control, vol. 59, no. 11, pp. 3002-3007, October 2014. (See also companion technical report)
- J. Y. Park, M. B. Wakin, and A. C. Gilbert, Modal Analysis with Compressive Measurements, IEEE Transactions on Signal Processing, vol. 62, no. 7, pp. 1655-1670, April 2014. (authors’ copy)
- M. A. Davenport, D. Needell, and M. B. Wakin, Signal Space CoSaMP for Sparse Recovery with Redundant Dictionaries, IEEE Transactions on Information Theory, vol. 59, no. 10, pp. 6820-6829, October 2013. (authors’ copy, code)
- H. L. Yap, M. B. Wakin, and C.J. Rozell, Stable Manifold Embeddings with Structured Random Matrices, IEEE Journal of Selected Topics in Signal Processing, vol. 7, no. 4, pp. 720-730, August 2013. (authors’ copy)
- M. F. Duarte, M. B. Wakin, D. Baron, S. Sarvotham, and R. G. Baraniuk, Measurement Bounds for Sparse Signal Ensembles via Graphical Models, IEEE Transactions on Information Theory, vol. 59, no. 7, pp. 4280-4289, July 2013. (authors’ copy)
- A. Eftekhari, J. Romberg, and M. B. Wakin, Matched Filtering from Limited Frequency Samples, IEEE Transactions on Information Theory, vol. 59, no. 6, pp. 3475-3496, June 2013. (authors’ copy)
- J. Y. Park and M. B. Wakin, A Multiscale Algorithm for Reconstructing Videos from Streaming Compressive Measurements, Journal of Electronic Imaging, vol. 22, no. 2, pages 021001, 2013. (authors’ copy) (See also: companion technical report.)
- B. M. Sanandaji, T. L. Vincent, and M. B. Wakin, Concentration of Measure Inequalities for Toeplitz Matrices with Applications, IEEE Transactions on Signal Processing, vol. 61, no. 1, pp. 109-117, January 2013. (authors’ copy) (See also companion technical report.)
- A. J. Weinstein and M. B. Wakin, Recovering a Clipped Signal in Sparseland, Sampling Theory in Signal and Image Processing, vol. 12, no. 1, pp. 55-69, 2013. (authors’ copy)
- M. A. Davenport and M. B. Wakin, Compressive Sensing of Analog Signals Using Discrete Prolate Spheroidal Sequences, Applied and Computational Harmonic Analysis, vol. 33, no. 3, pp. 438-472, November 2012. (authors’ copy, code)
- M. Wakin, S. Becker, E. Nakamura, M. Grant, E. Sovero, D. Ching, J. Yoo, J. Romberg, A. Emami-Neyestanak, and E. Candès, A Non-Uniform Sampler for Wideband Spectrally-Sparse Environments, IEEE Journal on Emerging and Selected Topics in Circuits and Systems, vol. 2, no. 3, pp. 516-529, September 2012. (authors’ copy)
- J. Yoo, C. Turnes, E. Nakamura, C. Le, S. Becker, E. Sovero, M. Wakin, M. Grant, J. Romberg, A. Emami-Neyestanak, and E. Candès, A Compressed Sensing Parameter Extraction Platform for Radar Pulse Signal Acquisition, IEEE Journal on Emerging and Selected Topics in Circuits and Systems, vol. 2, no. 3, pp. 626-638, September 2012. (authors’ copy)
- J. Y. Park and M. B. Wakin, A Geometric Approach to Multi-view Compressive Imaging, EURASIP Journal on Advances in Signal Processing, vol. 2012, article 37, 2012.
- J.Y. Park, H.L. Yap, C.J. Rozell, and M.B. Wakin, Concentration of Measure for Block Diagonal Matrices with Applications to Compressive Signal Processing, IEEE Transactions on Signal Processing, vol. 59, no. 12, pp. 5859-5875, December 2011.
- L. Carin, R.G. Baraniuk, V. Cevher, D. Dunson, M.I. Jordan, G. Sapiro, and M.B. Wakin, Learning Low-Dimensional Signal Models: A Bayesian Approach Based on Incomplete Measurements, IEEE Signal Processing Magazine, vol. 28, no. 2, pp. 39-51, March 2011.
- M. A. Davenport and M. B. Wakin, Analysis of Orthogonal Matching Pursuit using the Restricted Isometry Property, IEEE Transactions on Information Theory, vol. 56, no. 9, pp. 4395–4401, September 2010. (authors’ copy)
- R. G. Baraniuk, V. Cevher, and M. B. Wakin, Low-dimensional models for dimensionality reduction and signal recovery: A geometric perspective, Proceedings of the IEEE, vol. 98, no. 6, pp. 959-971, June 2010.
- M. A. Davenport, P. T. Boufounos, M. B. Wakin, and R. G. Baraniuk, Signal processing with compressive measurements, IEEE Journal of Selected Topics in Signal Processing, vol. 4, no. 2, pp. 445-460, April 2010.
- R. A. Frazin, M. Jacob, W. B. Manchester, H. Morgan, and M. B. Wakin, Toward Reconstruction of Coronal Mass Ejection Density from Only Three Points of View, Astrophysical Journal, vol. 695, no. 1, pp. 636-641, April 2009.
- R. G. Baraniuk and M. B. Wakin, Random Projections of Smooth Manifolds, Foundations of Computational Mathematics, vol. 9, no. 1, pp. 51-77, February 2009.
- V. Chandrasekaran, M. B. Wakin, D. Baron, and R. G. Baraniuk, Representation and Compression of Multi-Dimensional Piecewise Functions Using Surflets, IEEE Transactions on Information Theory, vol. 55, no. 1, pp. 374-400, January 2009.
- E. J. Candès, M. B. Wakin, and S. P. Boyd, Enhancing Sparsity by Reweighted L1 Minimization, Journal of Fourier Analysis and Applications, vol. 14, no. 5, pp. 877–905, December 2008.
- R. Baraniuk, M. Davenport, R. DeVore, and M. Wakin, A Simple Proof of the Restricted Isometry Property for Random Matrices, Constructive Approximation, vol. 28, no. 3, pp. 253–263, December 2008.
- E. J. Candès and M. B. Wakin, An introduction to compressive sampling, in IEEE Signal Processing Magazine, vol. 25, no. 2, pp. 21–30, March 2008.
- M. B. Wakin, J. K. Romberg, H. Choi, and R. G. Baraniuk, Wavelet-domain Approximation and Compression of Piecewise Smooth Images, in IEEE Transactions on Image Processing, Vol. 15, No. 5, May 2006.
Conference Papers
- Z. Qin, A. Lidiak, Z. Gong, G. Tang, M. B. Wakin, and Z. Zhu, Error Analysis of Tensor-Train Cross Approximation, Conference on Neural Information Processing Systems (NeurIPS), 2022.
- I. Pawelec, M. Wakin, and P. Sava, Multichannel compressive sensing for seismic data reconstruction using joint sparsity, Second International Meeting for Applied Geoscience & Energy (IMAGE), August 2022.
- Y. Wu, M.B. Wakin, and P. Gerstoft, Gridless DOA Estimation Under the Multi-Frequency Model, in IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), May 2022.
- Y. Xie, M. B. Wakin, and G. Tang, Data-driven Support Recovery for Sparse Signals with Non-stationary Modulation, IEEE 2021 International Conference on Machine Learning and Applications (ICMLA), Pasadena, California (virtual), December 2021.
- Y. Xie, M. B. Wakin, and G. Tang, Data-driven Parameter Estimation Of Contaminated Damped Exponentials, 55th Asilomar Conference on Signals, Systems, and Computers, Pacific Grove, California (virtual), October 2021.
- A. Titova, M. B. Wakin, and A. Tura, Two-stage sampling–A novel approach for compressive sensing seismic acquisition, First International Meeting for Applied Geoscience & Energy Expanded Abstracts, Denver, Colorado, September 2021, pp. 110-114.
- A. Titova, M. B. Wakin, and A. Tura, Empirical analysis of compressive sensing reconstruction using the curvelet transform: SEAM Barrett model, First International Meeting for Applied Geoscience & Energy Expanded Abstracts, Denver, Colorado, September 2021, pp. 115-119.
- Y. Xie, M. B. Wakin, and G. Tang, Contaminated Multiband Signal Identification Via Deep Learning, IEEE Statistical Signal Processing Workshop, Rio de Janeiro, Brazil (virtual), July 2021.
- S. Li, S. Becker, and M. B. Wakin, Nuclear Norm Based Spectrum Estimation for Molecular Dynamic Simulations, 54th Asilomar Conference on Signals, Systems, and Computers, Pacific Grove, California (virtual), November 2020.
- S. Li, D. Gaydos, P. Nayeri, and M. Wakin, Adaptive Interference Cancellation Using Atomic Norm Minimization, in 2020 International Applied Computational Electromagnetics Society (ACES) Symposium, Monterey, California, March 2020.
- Z. Zhu, Q. Li, X. Yang, G. Tang, and M. B. Wakin, Distributed Low-rank Matrix Factorization with Exact Consensus, Thirty-third Conference on Neural Information Processing Systems (NeurIPS), Vancouver, Canada, December 2019. (authors’ copy)
- S. Li, G. Tang, and M. B. Wakin, The Landscape of Non-convex Empirical Risk with Degenerate Population Risk, Thirty-third Conference on Neural Information Processing Systems (NeurIPS), Vancouver, Canada, December 2019. (authors’ copy)
- S. Li, Q. Li, G. Tang, and M. B. Wakin, Geometry Correspondence between Empirical and Population Games, Bridging Game Theory and Deep Learning Workshop at NeurIPS, Vancouver, Canada, December 2019.
- Q. Li, X. Yang, Z. Zhu, G. Tang, and M. Wakin, “The Geometric Effects of Distributing Constrained Nonconvex Optimization Problems,” IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP), Guadeloupe, December 2019.
- J. Helland, M. Wakin, and G. Tang, “A Super-Resolution Algorithm for Extended Target Localization,” IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP), Guadeloupe, December 2019.
- Q. Li, Z. Zhu, M. Wakin, and G. Tang, “The Geometry of Orthogonal Dictionary Learning using L1 Minimization,” 53nd Asilomar Conference on Signals, Systems and Computers, Pacific Grove, California, November 2019.
- Y. Xie, M. Wakin, and G. Tang, “Support Recovery for Sparse Recovery and Non-stationary Blind Demodulation,” 53nd Asilomar Conference on Signals, Systems and Computers, Pacific Grove, California, November 2019.
- A. Titova, M. B. Wakin, and A. Tura, Mutual coherence in compressive sensing seismic acquisition, SEG Technical Program Expanded Abstracts, San Antonio, Texas, September 2019.
- I. Pawelec, P. Sava, and M. Wakin, Wavefield reconstruction using wavelet transform, SEG Technical Program Expanded Abstracts, San Antonio, Texas, September 2019.
- Q. Li, Z. Zhu, G. Tang, and M. B. Wakin, The Geometry of Equality-Constrained Global Consensus Problems, IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2019), Brighton, UK, May 2019.
- S. Li, G. Tang, and M. B. Wakin, Simultaneous Blind Deconvolution and Phase Retrieval with Tensor Iterative Hard Thresholding, IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2019), Brighton, UK, May 2019.
- Y. Xie, M. B. Wakin, and G. Tang, Sparse Recovery and Non-Stationary Blind Demodulation, IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2019), Brighton, UK, May 2019.
- F. Pourkamali-Anaraki, S. Becker, and M. B. Wakin, Randomized Clustered Nystrom for Large-Scale Kernel Machines, AAAI Conference on Artificial Intelligence (AAAI-18), New Orleans, Louisiana, February 2018.
- Z. Zhu, M. Lopez-Santillana, and M. Wakin, Super-Resolution of Complex Exponentials from Modulations with Known Waveforms, IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP), Curaçao, December 2017.
- Z. Zhu, Q. Li, G. Tang, M. Wakin, “Global Optimality in Low-rank Matrix Optimization,” IEEE Global Conference on Signal and Information Processing (GlobalSIP), Montreal, Canada, November 2017.
- Z. Zhu, D. Yang, M. B. Wakin, and G. Tang, A Super-Resolution Algorithm for Multiband Signal Identification, 51st Asilomar Conference on Signals, Systems and Computers, Pacific Grove, California, October 2017.
- A. Eftekhari, M. B. Wakin, P. Li, P. G. Constantine, and R. A. Ward, “Learning the Second-Moment Matrix of a Smooth Function From Point Samples,” 51st Asilomar Conference on Signals, Systems and Computers, Pacific Grove, California, October 2017.
- Y. Xie, S. Li, G. Tang, and M. B. Wakin, Radar signal demixing via convex optimization, International Conference on Digital Signal Processing (DSP), London, August 2017.
- S. Li, D. Yang, and M. Wakin, Atomic Norm Minimization for Modal Analysis With Random Spatial Compression, IEEE 2017 International Conference on Acoustics, Speech, and Signal Processing — ICASSP 2017, New Orleans, Louisiana, March 2017.
- Z. Zhu, S. Karnik, M. Wakin, M. Davenport, and J. Romberg, Fast Orthogonal Approximations of Sampled Sinusoids and Bandlimited Signals, IEEE 2017 International Conference on Acoustics, Speech, and Signal Processing — ICASSP 2017, New Orleans, Louisiana, March 2017.
- Q. Li, S. Li, H. Mansour, M. Wakin, D. Yang, and Z. Zhu, JAZZ: A Companion to MUSIC for Frequency Estimation With Missing Data, IEEE 2017 International Conference on Acoustics, Speech, and Signal Processing — ICASSP 2017, New Orleans, Louisiana, March 2017.
- S. Karnik, Z. Zhu, M. B. Wakin, J. K. Romberg, M. A. Davenport, Fast Computations for Approximation and Compression in Slepian Spaces, IEEE Global Conference on Signal and Information Processing (GlobalSIP), Greater Washington, D.C., December 2016.
- Z. Zhu and M. B. Wakin, On the Dimensionality of Wall and Target Return Subspaces in Through-the-Wall Radar Imaging, 4th International Workshop on Compressed Sensing Theory and its Applications to Radar, Sonar and Remote Sensing (CoSeRa), Aachen, Germany, September 2016.
- Z. Zhu, G. Tang, P. Setlur, S. Gogineni, M. Wakin, and M. Rangaswamy, Super-Resolution in SAR Imaging: Analysis With the Atomic Norm, IEEE Sensor Array and Multichannel Signal Processing (SAM) Workshop, Rio de Janeiro, Brazil, July 2016.
- D. Yang, G. Tang, and M. B. Wakin, Non-Stationary Blind Super-Resolution, IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), Shanghai, China, March 2016.
- P. G. Constantine, A. Eftekhari, and M. B. Wakin, Computing Active Subspaces Efficiently with Gradient Sketching, IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP), Cancun, Mexico, December 2015. (authors’ copy)
- M. Babakmehr, M. G. Simoes, M. B. Wakin, A. Al Durra, and F. Harirchi, Smart grid topology identification using sparse recovery, IEEE Industry Applications Society (IAS) Annual Meeting, Addison, TX, October 2015.
- Z. Zhu and M. B. Wakin, Wall Clutter Mitigation and Target Detection Using Discrete Prolate Spheroidal Sequences, 3rd International Workshop on Compressed Sensing Theory and its Applications to Radar, Sonar and Remote Sensing (CoSeRa), Pisa, Italy, June 2015.
- D. Yang and M. B. Wakin, Modeling and Recovering Non-Transitive Pairwise Comparison Matrices, 11th International Conference on Sampling Theory and Applications (SampTA), Washington, DC, May 2015.
- Z. Zhu and M. B. Wakin, Detection of Stationary Targets Using Discrete Prolate Spheroidal Sequences, International Review of Progress in Applied Computational Electromagnetics (ACES), Williamsburg, Virginia, March 2015.
- J. Y. Park, M. B. Wakin, and A. C. Gilbert, Sampling Considerations for Modal Analysis with Damping, Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems at SPIE Smart Structures/NDE, San Diego, California, March 2015.
- C. W. Lim and M. B. Wakin, Recovery of Periodic Clustered Sparse Signals From Compressive Measurements, IEEE Global Conference on Signal and Information Processing (GlobalSIP), Atlanta, Georgia, December 2014.
- H. L. Yap, A. Eftekhari, M. B. Wakin, and C. J. Rozell, A First Analysis of the Stability of Takens’ Embedding, IEEE Global Conference on Signal and Information Processing (GlobalSIP), Atlanta, Georgia, December 2014.
- J. Y. Park, A. C. Gilbert, and M. B. Wakin, Compressive Measurement Bounds for Wireless Sensor Networks in Structural Health Monitoring, World Conference on Structural Control and Monitoring (WCSCM), Barcelona, Spain, July 2014.
- M. J. Rubin, M. B. Wakin, and T. Camp, A Comparison of On-Mote Lossy Compression Algorithms for Wireless Seismic Data Acquisition, IEEE International Conference on Distributed Computing in Sensor Systems (DCOSS), Marina Del Rey, California, May 2014.
- M. Rubin, M. Wakin, and T. Camp, “Sensor Node Compressive Sampling in Wireless Seismic Sensor Networks,” 1st IEEE/ACM Workshop on Signal Processing Advances in Sensor Networks (SPASN), Philadelphia, Pennsylvania, April 2013.
- B. M. Sanandaji, T. L. Vincent, K. Poolla, and M. Wakin, A Tutorial on Recovery Conditions for Compressive System Identification of Sparse Channels, IEEE 2012 Conference on Decision and Control — CDC 2012, Maui, Hawaii, December 2012.
- M. Davenport, D. Needell, and M. Wakin, CoSaMP with Redundant Dictionaries, 46th Asilomar Conference on Signals, Systems and Computers, Pacific Grove, California, November 2012. (authors’ copy)
- A.J. Weinstein and M.B. Wakin, Online Search Orthogonal Matching Pursuit, in IEEE Statistical Signal Processing Workshop — SSP 2012, Ann Arbor, Michigan 2012.
- B.M. Sanandaji, T.L. Vincent, and M.B. Wakin, A Review of Sufficient Conditions for Structure Identification in Interconnected Systems, invited to 16th IFAC Symposium on System Identication — SYSID 2012, Brussels, Belgium, July 2012.
- V. Rizi, Pierre Auger Collaboration, A. Botts, C. Allen, M. Calhoun, B. Carande, M. Coco, J. Claus, L. Emmert, L. Hamilton, T.J. Heid, F. Honecker, M. Iarlori, S. Morgan, S. Robinson, D. Starbuck, J. Sherman, M. Wakin, and O. Wolf, “UV Raman Lidar and Side Scattering Detector for the Monitoring of Aerosol Optical Transmission at the Pierre Auger Observatory,” 26th International Laser Radar Conference — ILRC 26, Porto Heli, Greece, June 2012.
- C.W. Lim and M.B. Wakin, Automatic Modulation Recognition for Spectrum Sensing using Nonuniform Compressive Samples, in IEEE International Conference on Communications — ICC 2012, Ottawa, Canada, June 2012.
- C.W. Lim and M.B. Wakin, CHOCS: A Framework for Estimating Compressive, Higher-order Cyclostationary Statistics, in SPIE Defense, Security, and Sensing Symposium — DSS 2012, Baltimore, Maryland, April 2012.
- B.M. Sanandaji, T.L. Vincent, and M.B. Wakin, Compressive Topology Identification of Interconnected Dynamic Systems via Clustered Orthogonal Matching Pursuit, IEEE 2011 Conference on Decision and Control and European Control Conference — CDC-ECC, Orlando, Florida, December 2011.
- B.M. Sanandaji, T.L. Vincent, M.B. Wakin, R. Toth, and K. Poolla, Compressive System Identification of LTI and LTV ARX Models, IEEE 2011 Conference on Decision and Control and European Control Conference — CDC-ECC, Orlando, Florida, December 2011.
- L. Wiencke, for the Pierre Auger Collaboration, A. Botts, C. Allan, M. Calhoun, B. Carande, M. Coco, J. Claus, L. Emmert, L. Hamilton, T.J. Heid, F. Honecker, M. Iarlori, S. Morgan, S. Robinson, D. Starbuck, J. Sherman, M. Wakin, and O. Wolf, Atmospheric “Super Test Beam” for the Pierre Auger Observatory, 32nd International Cosmic Ray Conference, Beijing, August 2011.
- B.M. Sanandaji, T.L. Vincent, and M.B. Wakin, Exact Topology Identification of Large-Scale Interconnected Dynamical Systems from Compressive Observations, 2011 American Control Conference — ACC 2011, San Francisco, CA, June 2011.
- A. Eftekhari, J. Romberg, and M.B. Wakin, A Probabilistic Analysis of the Compressive Matched Filter, 9th International Conference on Sampling Theory and Applications (SampTA 2011), Singapore, May 2011.
- H.L. Yap, M.B. Wakin, and C.J. Rozell, Stable Manifold Embeddings with Operators Satisfying the Restricted Isometry Property, 45th Annual Conference on Information Sciences and Systems — CISS 2011, Baltimore, Maryland, March 2011.
- H.L. Yap, A. Eftekhari, M.B. Wakin, and C.J. Rozell, The Restricted Isometry Property for Block Diagonal Matrices, 45th Annual Conference on Information Sciences and Systems — CISS 2011, Baltimore, Maryland, March 2011.
- M.B. Wakin, B.M. Sanandaji, and T.L. Vincent, On the Observability of Linear Systems from Random, Compressive Measurements, in IEEE 2010 Conference on Decision and Control — CDC 2010, Atlanta, Georgia, December 2010.
- B.M. Sanandaji, T.L. Vincent, and M.B. Wakin, Concentration of Measure Inequalities for Compressive Toeplitz Matrices with Applications to Detection and System Identification, in IEEE 2010 Conference on Decision and Control — CDC 2010, Atlanta, Georgia, December 2010.
- M.A. Davenport, S.R. Schnelle, J.P. Slavinsky, R.G. Baraniuk, M.B. Wakin, and P.T. Boufounos, A Wideband Compressive Radio Receiver, in Military Communications Conference (MILCOM), San Jose, California, October 2010.
- M.B. Wakin, J.Y. Park, H.L. Yap, and C.J. Rozell, Concentration of measure for block diagonal measurement matrices, in IEEE 2010 International Conference on Acoustics, Speech, and Signal Processing — ICASSP 2010, Dallas, TX, March 2010.
- C.J. Rozell, H.L. Yap, J.Y. Park, and M.B. Wakin, Concentration of measure for block diagonal matrices with repeated blocks, in 44th Annual Conference on Information Sciences and Systems — CISS 2010, Princeton, New Jersey, March 2010.
- M.B. Wakin, A Manifold Lifting Algorithm for Multi-View Compressive Imaging, in Picture Coding Symposium (PCS 2009), Chicago, Illinois, May 2009.
- J.Y. Park and M.B. Wakin, A Multiscale Framework for Compressive Sensing of Video, in Picture Coding Symposium (PCS 2009), Chicago, Illinois, May 2009.
- M.F. Duarte, S. Sarvotham, D. Baron, M.B. Wakin, and R.G. Baraniuk, Performace Limits for Jointly Sparse Signals via Graphical Models, in Sensor, Signal and Information Processing Workshop — SenSIP, Sedona, AZ, May 2008.
- M.F. Duarte, M.B. Wakin, and R.G. Baraniuk, Wavelet-domain Compressive Signal reconstruction using a Hidden Markov Tree Model, in IEEE 2008 International Conference on Acoustics, Speech, and Signal Processing — ICASSP 2008, Las Vegas, Nevada, March 2008.
- C. Hegde, M. Wakin, and R. Baraniuk, Random Projections for Manifold Learning, in Neural Information Processing Systems — NIPS, Vancouver, Canada, December 2007. See also: supplemental tech report.
- M. Duarte, M. Davenport, M. Wakin, J. Laska, D. Takhar, K. Kelly, and R. Baraniuk, Multiscale random projections for compressive classification, in IEEE Int. Conf. on Image Processing (ICIP), San Antonio, Texas, September 2007.
- E. Candès, N. Braun, and M. Wakin, “Sparse Signal and Image Recovery from Compressive Samples,” in IEEE 2007 International Symposium on Biomedical Imaging, Washington, D.C., April 2007.
- M. Davenport, M. Duarte, M. Wakin, J. Laska, D. Takhar, K. Kelly, and R. Baraniuk, The Smashed Filter for Compressive Classification and Target Recognition, in Computational Imaging V at IS&T/SPIE Electronic Imaging, San Jose, California, January 2007.
- S. Kirolos, J. Laska, M. Wakin, M. Duarte, D. Baron, T. Ragheb, Y. Massoud, and R. Baraniuk, Analog-to-Information Conversion via Random Demodulation, in IEEE Dallas Circuits and Systems Workshop (DCAS), Dallas, TX, 2006.
- M. B. Wakin, J. N. Laska, M. F. Duarte, D. Baron, S. Sarvotham, D. Takhar, K. F. Kelly, and R. G. Baraniuk, An Architecture for Compressive Imaging, in IEEE 2006 International Conference on Image Processing — ICIP 2006, Atlanta, GA, Oct. 2006.
- M. B. Wakin and R. G. Baraniuk, Random Projections of Signal Manifolds, in IEEE 2006 International Conference on Acoustics, Speech, and Signal Processing — ICASSP 2006, Toulouse, France, May 2006.
- J. A. Tropp, M. B. Wakin, M. F. Duarte, D. Baron, and R. G. Baraniuk, Random Filters for Compressive Sampling and Reconstruction, in IEEE 2006 International Conference on Acoustics, Speech, and Signal Processing — ICASSP 2006, Toulouse, France, May 2006.
- M. F. Duarte, M. A. Davenport, M. B. Wakin, and R. G. Baraniuk, Sparse Signal Detection from Incoherent Projections, in IEEE 2006 International Conference on Acoustics, Speech, and Signal Processing — ICASSP 2006, Toulouse, France, May 2006.
- M. F. Duarte, M. B. Wakin, D. Baron, and R. G. Baraniuk, Universal Distributed Sensing via Random Projections, in International Conference on Information Processing in Sensor Networks — IPSN 2006, Nashville, TN, April, 2006.
- M. B. Wakin, J. N. Laska, M. F. Duarte, D. Baron, S. Sarvotham, D. Takhar, K. F. Kelly, and R. G. Baraniuk, Compressive Imaging for Video Representation and Coding in Picture Coding Symposium — PCS 2006, Beijing, China, April 2006.
- D. Takhar, J. N. Laska, M. B. Wakin, M. F. Duarte, D. Baron, S. Sarvotham, K. F. Kelly, and R. G. Baraniuk, A New Compressive Imaging Camera Architecture using Optical-Domain Compression, in Computational Imaging IV at IS&T/SPIE Electronic Imaging, San Jose, California, January 2006.
- M. B. Wakin, M. F. Duarte, S. Sarvhotam, D. Baron, and R. G. Baraniuk, Recovery of Jointly Sparse Signals from Few Random Projections, in Neural Information Processing Systems — NIPS, Vancouver, Canada, December 2005.
- M. F. Duarte, S. Sarvotham, D. Baron, M. B. Wakin, and R. G. Baraniuk, Distributed Compressed Sensing of Jointly Sparse Signals, in Asilomar Conference on Signals, Systems, and Computers, Pacific Grove, California, November 2005.
- M. F. Duarte, M. B. Wakin, and R. G. Baraniuk, Fast Reconstruction of Piecewise Smooth Signals from Random Projections, in online proceedings of Workshop on Signal Processing with Adaptive Sparse Structured Representations — SPARS’05, Rennes, France, November 2005.
- M. F. Duarte, S. Sarvotham, M. B. Wakin, D. Baron, and R. G. Baraniuk, Joint Sparsity Models for Distributed Compressed Sensing, in online proceedings of Workshop on Signal Processing with Adaptive Sparse Structured Representations — SPARS’05, Rennes, France, November 2005.
- D. Baron, M. F. Duarte, S. Sarvotham, M. B. Wakin, and R. G. Baraniuk, An Information-Theoretic Approach to Distributed Compressed Sensing, in 43rd Allerton Conference on Communication, Control, and Computing, September 2005.
- M. B. Wakin, D. L. Donoho, H. Choi, and R. G. Baraniuk, The Multiscale Structure of Non-Differentiable Image Manifolds, in SPIE Wavelets XI, San Diego, California, July 2005.
- M. B. Wakin, D. L. Donoho, H. Choi, and R. G. Baraniuk, High-Resolution Navigation on Non-Differentiable Image Manifolds, in IEEE 2005 International Conference on Acoustics, Speech, and Signal Processing — ICASSP 2005, Philadelphia, Pennsylvania, March 2005.
- V. Chandrasekaran, M. B. Wakin, D. Baron, and R. G. Baraniuk, Surflets: A Sparse Representation for Multidimensional Functions Containing Smooth Discontinuities, in IEEE 2004 International Symposium on Information Theory — ISIT 2004, Chicago, Illinois, June 2004.
- F. C. A. Fernandes, M. B. Wakin, and R. G. Baraniuk, Non-Redundant, Linear-Phase, Semi-Orthogonal, Directional Complex Wavelets, in IEEE 2004 International Conference on Acoustics, Speech, and Signal Processing — ICASSP 2004, Montreal, Quebec, Canada, May 2004.
- V. Chandrasekaran, M. B. Wakin, D. Baron, and R. G. Baraniuk, Compression of Higher Dimensional Functions Containing Smooth Discontinuities, in 38th Annual Conference on Information Sciences and Systems — CISS 2004, Princeton, New Jersey, March 2004. See also: supplemental tech report.
- M. B. Wakin, M. T. Orchard, R. G. Baraniuk, and V. Chandrasekaran, Phase and Magnitude Perceptual Sensitivities in Nonredundant Complex Wavelet Representations, in Asilomar Conference on Signals, Systems, and Computers, Pacific Grove, California, November 2003.
- J.K. Romberg, M.B. Wakin, and R.G. Baraniuk, Approximation and Compression of Piecewise Smooth Images Using a Wavelet/Wedgelet Geometric Model, in IEEE 2003 International Conference on Image Processing — ICIP-2003, Barcelona, Spain, September 2003.
- M. B. Wakin, J. K. Romberg, H. Choi, and R. G. Baraniuk, Geometric Methods for Wavelet-Based Image Compression, in SPIE Wavelets X, San Diego, California, August 2003.
- J. K. Romberg, M. B. Wakin, H. Choi, and R. G. Baraniuk, A Geometric Hidden Markov Tree Wavelet Model, in SPIE Wavelets X, San Diego, California, August 2003.
- J.K. Romberg, M.B. Wakin, and R.G. Baraniuk, Multiscale Geometric Image Processing, in SPIE Visual Communications and Image Processing, Lugano, Switzerland, July 2003.
- M.B. Wakin, J.K. Romberg, H. Choi, and R.G. Baraniuk, Geometric Tools for Image Compression, in Asilomar Conference on Signals, Systems, and Computers, Pacific Grove, California, November 2002.
- R.M. Castro, M.B. Wakin, and M.T. Orchard, On the Problem of Simultaneous Encoding of Magnitude and Location Information, in Asilomar Conference on Signals, Systems, and Computers, Pacific Grove, California, November 2002.
- M.B. Wakin, J.K. Romberg, H. Choi, and R.G. Baraniuk, Rate-Distortion Optimized Image Compression Using Wedgelets, in IEEE 2002 International Conference on Image Processing — ICIP-2002, Rochester, New York, September 2002.
- J.K. Romberg, M.B. Wakin, and R.G. Baraniuk, Multiscale Wedgelet Image Analysis: Fast Decompositions and Modeling, in IEEE 2002 International Conference on Image Processing — ICIP-2002, Rochester, New York, September 2002.
- M.B. Wakin, J.K. Romberg, H. Choi, and R.G. Baraniuk, Image Compression Using an Efficient Edge Cartoon + Texture Model, in IEEE Data Compression Conference, DCC, Snowbird, Utah, April 2002.
Ph.D. Thesis
- M. B. Wakin, The Geometry of Low-dimensional Signal Models, Ph.D. thesis, Rice University, August 2006.
Selected Presentations
- “Modal Analysis from Random and Compressed Samples”, NDT4Industry webinar-series hosted by RECENDT, the Research Center for Non-Destructive Testing, Austria, July 2022. (video) (slides)
- “Spectral Properties of Time-limited Toeplitz Operators and Applications in Signal Processing,” joint work with Z. Zhu et al., One World MINDS Seminar, April 2021. (video)
- “Compressive Sensing,” tutorial at Center for Wave Phenomena (CWP) Project Review Meeting, Colorado Springs, Colorado, May 2016. (slides)
- “Matched Filtering from Limited Frequency Samples”, joint work with A. Eftekhari and J. Romberg, Joint Applied Mathematics and Statistics/Computer Science Colloquium, Colorado School of Mines, January 2012. (slides) (paper)
- “An Efficient Dictionary for Reconstruction of Sampled Multiband Signals”, joint work with M. A. Davenport, Duke Workshop on Sensing and Analysis of High-Dimensional Data, Duke University, July 2011. (slides) (video) (paper)
- “The Multiscale Structure of Non-Differentiable Image Manifolds”, joint work with R. Baraniuk, H. Choi, D. Donoho, Computational Optical Sensing and Imaging (COSI) Seminar, University of Colorado at Boulder, August 2010. (slides) (video) (paper)
- “Compressed Sensing: A Tutorial,” presented with J. K. Romberg, IEEE Statistical Signal Processing Workshop, August 2007. (slides)
Technical Reports
- Q. Li, Z. Zhu, G. Tang, and M. B. Wakin, Provable Bregman-divergence based Methods for Nonconvex and Non-Lipschitz Problems, arXiv preprint arXiv:1904.09712, 2019.
- F. Pourkamali-Anaraki and M. B. Wakin, The Effectiveness of Variational Autoencoders for Active Learning, arXiv preprint arXiv:1911.07716, 2019.
- A. Eftekhari, L. Balzano, D. Yang, and M. B. Wakin, SNIPE for Memory-Limited PCA From Incomplete Data, Arxiv preprint arXiv:1612.00904v1, 2016. (Replaced by this updated manuscript.)
- C. W. Lim and M. B. Wakin, Reconstruction of Frequency Hopping Signals From Multi-Coset Samples, Arxiv preprint arXiv:1603.06886, 2016.
- A. Eftekhari and M. B. Wakin, Greed is Super: A Fast Algorithm for Super-Resolution, Arxiv preprint arXiv:1511.03385, 2015. (code)
- B. M. Sanandaji, T. L. Vincent, and M. B. Wakin, Concentration of Measure Inequalities for Toeplitz Matrices with Applications, Arxiv preprint arXiv:1112.1968, 2012. (supplement to TSP paper)
- A. C. Gilbert, J. Y. Park, and M. B. Wakin, Sketched SVD: Recovering Spectral Features from Compressive Measurements, Arxiv preprint arXiv:1211.0361, 2012.
- M. B. Wakin, A Study of the Temporal Bandwidth of Video and its Implications in Compressive Sensing, Colorado School of Mines Technical Report 2012-08-15, August 2012.
- M. B. Wakin, Manifold-Based Signal Recovery and Parameter Estimation from Compressive Measurements, 2008.
- M. F. Duarte, S. Sarvotham, M. B. Wakin, D. Baron, and R. G. Baraniuk, Theoretical Performance Limits for Jointly Sparse Signals via Graphical Models, Technical Report TREE-0802, Electrical and Computer Engineering Department, Rice University, July 2008.
- C. Hegde, M. Wakin, and R. Baraniuk, Random Projections for Manifold Learning: Proofs and Analysis, Rice University ECE Technical Report TREE0710, October 2007. (supplement to NIPS 2007 paper)
- D. Baron, M. B. Wakin, M. F. Duarte, S. Sarvotham, and R. G. Baraniuk, Distributed Compressed Sensing, Technical Report ECE06-12, Electrical and Computer Engineering Department, Rice University, November 2006. Updated version: Distributed Compressive Sensing, Arxiv preprint arXiv:0901.3403, 2009.
- M.A. Davenport, M.B. Wakin, and R.G. Baraniuk, Detection and Estimation with Compressive Measurements, Rice University ECE Technical Report TR-0610, Houston, TX, November 2006.
- S. Sarvotham, M. B. Wakin, D. Baron, M. F. Duarte, and R. G. Baraniuk, Analysis of the DCS one-stage Greedy Algorothm for Common Sparse Supports, Rice University ECE Technical Report TR-0503, Houston, TX, November 2005.
- V. Chandrasekaran, M. Wakin, D. Baron, and R. Baraniuk, Compressing Piecewise Smooth Multidimensional Functions Using Surflets: Rate-Distortion Analysis, Rice University ECE Technical Report, Houston, TX, March 2004. (supplement to CISS 2004 paper)
- M. B. Wakin and C. J. Rozell, A Markov Chain Analysis of Blackjack Strategy, 2004. (code)
Educational Materials
- R. Snieder and M. B. Wakin, When Randomness Helps in Undersampling, undergraduate-level education article in SIAM Review, vol. 64, no. 4, pp. 1062-1080, November 2022. (code)
- A. Drgac, M. Wakin, and D. Yang, Algorithms and Everyday Life, K-12 Outreach Lesson, TeachEngineering Digital Library, 2020.
- A. Drgac, M. Wakin, and D. Yang, Acting Like an Algorithm, K-12 Outreach Activity, TeachEngineering Digital Library, 2020.
- M. B. Wakin, D. Yang, and K. R. Feaster, Filtering: Extracting What We Want from What We Have, K-12 Outreach Lesson, TeachEngineering Digital Library, 2015.
- C. McKay, C. Light, A. Adekola, M. B. Wakin, D. Yang, and K. R. Feaster, Filtering: Removing Noise from a Distress Signal, K-12 Outreach Activity, TeachEngineering Digital Library, 2015.
- M.B. Wakin, Concise Signal Models, Connexions modules endorsed by the IEEE Signal Processing Society.
Interviews
Software
- Random Spectral Sampling Toolbox
This toolbox contains the MATLAB code necessary to reproduce the figures in the paper When Randomness Helps in Undersampling by Roel Snieder and Michael B. Wakin, published in SIAM Review, vol. 64, no. 4, pp. 1062-1080, November 2022. This code estimates various signals from randomly undersampled frequency spectra. Whereas uniform subsampling would introduce structural artifacts in the time series, random subsampling introduces a type of noise whose behavior we quantify in the paper.
- Digital Beamforming Robust to Time-Varying Carrier Frequency Offset
Adaptive interference cancellation is rapidly becoming a necessity for our modern wireless communication systems, due to the proliferation of wireless devices that interfere with each other. To cancel interference, digital beamforming algorithms adaptively adjust the weight vector of the antenna array, and in turn its radiation pattern, to minimize interference while maximizing the desired signal power. While these algorithms are effective in ideal scenarios, they are sensitive to signal corruptions. In this work, we consider the case when the transmitter and receiver in a communication system cannot be synchronized, resulting in a carrier frequency offset that corrupts the signal. We present novel beamforming algorithms that are robust to signal corruptions arising from this time-variant carrier frequency offset. In particular, we bring in the Discrete Prolate Spheroidal Sequences (DPSS’s) and propose two atomic-norm-minimization (ANM)-based methods in both 1D and 2D frameworks to design a weight vector that can be used to cancel interference when there exist unknown time-varying frequency drift in the pilot and interferer signals. Both algorithms do not assume a pilot signal is known. Noting that solving ANM optimization problems via semi-definite programs can be a computational burden, we also present a novel fast algorithm to approximately solve our 1D ANM optimization problem. Finally, we confirm the benefits of our proposed algorithms and show the advantages over existing approaches with a series of experiments.
This software will reproduce the figures in the paper Digital Beamforming Robust to Time-Varying Carrier Frequency Offset by S. Li, P. Nayeri, and M. B. Wakin.
- Simultaneous Sparse Recovery and Blind Demodulation
The task of finding a sparse signal decomposition in an overcomplete dictionary is made more complicated when the signal undergoes an unknown modulation (or convolution in the complementary Fourier domain). Such simultaneous sparse recovery and blind demodulation problems appear in many applications including medical imaging, super resolution, self-calibration, etc. In this paper, we consider a more general sparse recovery and blind demodulation problem in which each atom comprising the signal undergoes a distinct modulation process. Under the assumption that the modulating waveforms live in a known common subspace, we employ the lifting technique and recast this problem as the recovery of a column-wise sparse matrix from structured linear measurements. In this framework, we accomplish sparse recovery and blind demodulation simultaneously by minimizing the induced atomic norm, which in this problem corresponds to the block L1 norm minimization.
This software will reproduce the figures in the paper Simultaneous Sparse Recovery and Blind Demodulation by Y. Xie, M. B. Wakin, and G. Tang.
- Support Recovery for Sparse Signals With Unknown Non-Stationary Modulation
The problem of estimating a sparse signal from low dimensional noisy observations arises in many applications, including super resolution, signal deconvolution, and radar imaging. In this paper, we consider a sparse signal model with non-stationary modulations, in which each dictionary atom contributing to the observations undergoes an unknown, distinct modulation. By applying the lifting technique, under the assumption that the modulating signals live in a common subspace, we recast this sparse recovery and non-stationary blind demodulation problem as the recovery of a column-wise sparse matrix from structured linear observations, and propose to solve it via block L1 -norm regularized quadratic minimization. Due to observation noise, the sparse signal and modulation process cannot be recovered exactly. Instead, we aim to recover the sparse support of the ground truth signal and bound the recovery errors of the signal’s non-zero components and the modulation process. In particular, we derive sufficient conditions on the sample complexity and regularization parameter for exact support recovery and bound the recovery error on the support. Numerical simulations verify and support our theoretical findings, and we demonstrate the effectiveness of our model in the application of single molecule imaging.
This software will reproduce the figures in the paper Support Recovery for Sparse Signals With Unknown Non-Stationary Modulation by Y. Xie, M. B. Wakin, and G. Tang.
- S. Li, H. Mansour, and M. B. Wakin, Recovery analysis of damped spectrally sparse signals and its relation to MUSIC.
This software package contains algorithms for identifying spectral parameters (frequencies and damping ratios) using nuclear norm minimization, specifically in settings where only partial samples are available.
In particular, as detailed in the file Readme.txt, this software will reproduce the figures in the paper Recovery analysis of damped spectrally sparse signals and its relation to MUSIC by S. Li, H. Mansour, and M. B. Wakin.
- S. Li, D. Yang, G. Tang, and M. B. Wakin, Atomic Norm Minimization for Modal Analysis from Random and Compressed Samples.
This software package contains algorithms for performing modal analysis (joint sparse frequency estimation) using atomic norm minimization, specifically in settings where only partial samples or randomly compressed signal measurements are available.
In particular, as detailed in the file readme.txt, this software will reproduce the figures in the paper Atomic Norm Minimization for Modal Analysis from Random and Compressed Samples by S. Li, D. Yang, G. Tang, and M. B. Wakin.
- S. Karnik, Z. Zhu, M. B. Wakin, J. Romberg, and M. A. Davenport, The Fast Slepian Transform.
This software package contains a collection of tools for implementing fast alorithms for working with the Slepian basis, also known as discrete prolate spheroidal sequences. See the included readme file for a detailed description of the contents and for usage instructions.
For further details, see the paper The Fast Slepian Transform by S. Karnik, Z. Zhu, M. B. Wakin, J. Romberg, and M. A. Davenport.
- A. Eftekhari and M. B. Wakin, Greedy Super-Resolution Toolbox.
This code demonstrates a fast two-phase algorithm for super-resolution. Given the low-frequency part of the spectrum of a sequence of impulses, Phase I consists of a greedy algorithm that roughly estimates the impulse positions. These estimates are then refined by local optimization in Phase II. The backbone of our work is the fundamental work of Slepian et al. involving discrete prolate spheroidal wave functions and their unique properties.
The function TwoPhaseAlg.m (called with no input arguments) will reproduce Figure 1 in the manuscript “Greed is Super: A Fast Algorithm for Super-Resolution” by A. Eftekhari and M. B. Wakin.
- M. A. Davenport, D. Needell, and M. B. Wakin, Signal Space CoSaMP Toolbox.
The bulk of the Compressive sensing (CS) literature has focused on the case where the acquired signal has a sparse or compressible representation in an orthonormal basis. In practice, however, there are many signals that cannot be sparsely represented or approximated using an orthonormal basis, but that do have sparse representations in a redundant dictionary. Standard results in CS can sometimes be extended to handle this case provided that the dictionary is sufficiently incoherent or well-conditioned, but these approaches fail to address the case of a truly redundant or overcomplete dictionary.
This software package implements a variant of the iterative reconstruction algorithm CoSaMP for this more challenging setting. In contrast to prior approaches, the method is “signal-focused”; that is, it is oriented around recovering the signal rather than its dictionary coefficients.
For further details, see the paper Signal Space CoSaMP for Sparse Recovery with Redundant Dictionaries, by M.A. Davenport, D. Needell, and M.B. Wakin.
- M. A. Davenport and M. B. Wakin, DPSS Approximation and Recovery Toolbox (DART).
Compressive sensing (CS) has recently emerged as a framework for efficiently capturing signals that are sparse or compressible in an appropriate basis. While often motivated as an alternative to Nyquist-rate sampling, there remains a gap between the discrete, finite-dimensional CS framework and the problem of acquiring a continuous-time signal.
This software package provides a set of tools for bridging this gap through the use of Discrete Prolate Spheroidal Sequences (DPSS’s), a collection of functions that trace back to the seminal work by Slepian, Landau, and Pollack on the effects of time-limiting and bandlimiting operations. DPSS’s form a highly efficient basis for sampled bandlimited functions; by modulating and merging DPSS bases, we obtain a dictionary that offers high-quality sparse approximations for most sampled multiband signals. This multiband modulated DPSS dictionary can be readily incorporated into the CS framework.
For further details, see the paper Compressive Sensing of Analog Signals Using Discrete Prolate Spheroidal Sequences, by M.A. Davenport and M.B. Wakin.
DART contains all of the software necessary to reproduce the results presented in this paper. It can be downloaded here. Please e-mail markad-at-stanford-dot-edu if you find any bugs or have any questions.
- C. J. Rozell and M. B. Wakin, Blackjack Markov Chain Toolbox, v1.0.
This Matlab package computes the player advantage in blackjack using two strategies: (1) basic strategy and (2) Thorp’s complete point count system for card counting. The analysis uses Markov chains, as described in the manuscript: M. B. Wakin and C. J. Rozell, A Markov Chain Analysis of Blackjack Strategy, 2004. Installation: Download and unzip code, then see the main Matlab script BJMCmainScript.m. This software is released under a Creative Commons license (Attribution 3.0 Unported).
This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author’s copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.