| Random matrix theory A Edelman, NR Rao Acta numerica 14, 233-297, 2005 | 644 | 2005 |
| The eigenvalues and eigenvectors of finite, low rank perturbations of large random matrices F Benaych-Georges, RR Nadakuditi Advances in Mathematics 227 (1), 494-521, 2011 | 589 | 2011 |
| The singular values and vectors of low rank perturbations of large rectangular random matrices F Benaych-Georges, RR Nadakuditi Journal of Multivariate Analysis 111, 120-135, 2012 | 378 | 2012 |
| Graph spectra and the detectability of community structure in networks RR Nadakuditi, MEJ Newman Physical review letters 108 (18), 188701, 2012 | 361 | 2012 |
| Sample eigenvalue based detection of high-dimensional signals in white noise using relatively few samples RR Nadakuditi, A Edelman IEEE Transactions on Signal Processing 56 (7), 2625-2638, 2008 | 356 | 2008 |
| Optshrink: An algorithm for improved low-rank signal matrix denoising by optimal, data-driven singular value shrinkage RR Nadakuditi IEEE Transactions on Information Theory 60 (5), 3002-3018, 2014 | 186 | 2014 |
| Fundamental limit of sample generalized eigenvalue based detection of signals in noise using relatively few signal-bearing and noise-only samples RR Nadakuditi, JW Silverstein IEEE Journal of selected topics in Signal Processing 4 (3), 468-480, 2010 | 181 | 2010 |
| Mode control in a multimode fiber through acquiring its transmission matrix from a reference-less optical system M N’Gom, TB Norris, E Michielssen, RR Nadakuditi Optics letters 43 (3), 419-422, 2018 | 117 | 2018 |
| Spectra of random graphs with arbitrary expected degrees RR Nadakuditi, MEJ Newman Physical Review E—Statistical, Nonlinear, and Soft Matter Physics 87 (1 …, 2013 | 96 | 2013 |
| The polynomial method for random matrices NR Rao, A Edelman Foundations of Computational Mathematics 8, 649-702, 2008 | 93 | 2008 |
| Statistical eigen-inference from large Wishart matrices NR Rao, JA Mingo, R Speicher, A Edelman | 93 | 2008 |
| Low-rank and adaptive sparse signal (LASSI) models for highly accelerated dynamic imaging S Ravishankar, BE Moore, RR Nadakuditi, JA Fessler IEEE transactions on medical imaging 36 (5), 1116-1128, 2017 | 80 | 2017 |
| AngioNet: A convolutional neural network for vessel segmentation in X-ray angiography K Iyer, CP Najarian, AA Fattah, CJ Arthurs, SMR Soroushmehr, V Subban, ... Scientific Reports 11 (1), 18066, 2021 | 66 | 2021 |
| Spectra of random graphs with community structure and arbitrary degrees X Zhang, RR Nadakuditi, MEJ Newman Physical review E 89 (4), 042816, 2014 | 64 | 2014 |
| Panoramic robust pca for foreground–background separation on noisy, free-motion camera video BE Moore, C Gao, RR Nadakuditi IEEE Transactions on Computational Imaging 5 (2), 195-211, 2019 | 50 | 2019 |
| Efficient sum of outer products dictionary learning (SOUP-DIL) and its application to inverse problems S Ravishankar, RR Nadakuditi, JA Fessler IEEE transactions on computational imaging 3 (4), 694-709, 2017 | 50 | 2017 |
| Controlling light transmission through highly scattering media using semi-definite programming as a phase retrieval computation method M N’Gom, MB Lien, NM Estakhri, TB Norris, E Michielssen, RR Nadakuditi Scientific reports 7 (1), 2518, 2017 | 45 | 2017 |
| Multiplication of free random variables and the S-transform: The case of vanishing mean NR Rao, R Speicher | 44 | 2007 |
| Low-rank spectral learning A Kulesza, NR Rao, S Singh Artificial Intelligence and Statistics, 522-530, 2014 | 42 | 2014 |
| Passive radar detection with noisy reference channel using principal subspace similarity S Gogineni, P Setlur, M Rangaswamy, RR Nadakuditi IEEE Transactions on Aerospace and Electronic Systems 54 (1), 18-36, 2017 | 37 | 2017 |
