Random matrix theory A Edelman, NR Rao Acta numerica 14, 233-297, 2005 | 535 | 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 | 410 | 2011 |

Graph spectra and the detectability of community structure in networks RR Nadakuditi, MEJ Newman Physical review letters 108 (18), 188701, 2012 | 312 | 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 | 295 | 2008 |

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 | 281 | 2012 |

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 | 147 | 2010 |

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 | 124 | 2014 |

Statistical eigen-inference from large Wishart matrices NR Rao, JA Mingo, R Speicher, A Edelman The Annals of Statistics 36 (6), 2850-2885, 2008 | 85 | 2008 |

The polynomial method for random matrices NR Rao, A Edelman Foundations of Computational Mathematics 8 (6), 649-702, 2008 | 83 | 2008 |

Spectra of random graphs with arbitrary expected degrees RR Nadakuditi, MEJ Newman Physical Review E 87 (1), 012803, 2013 | 80 | 2013 |

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 | 52 | 2017 |

Spectra of random graphs with community structure and arbitrary degrees X Zhang, RR Nadakuditi, MEJ Newman Physical Review E 89 (4), 042816, 2014 | 52 | 2014 |

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 | 48 | 2018 |

Low-rank spectral learning A Kulesza, NR Rao, S Singh Artificial Intelligence and Statistics, 522-530, 2014 | 43 | 2014 |

Multiplication of free random variables and the S-transform: The case of vanishing mean NR Rao, R Speicher Electronic Communications in Probability 12, 248-258, 2007 | 41 | 2007 |

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 | 39 | 2017 |

On hard limits of eigen-analysis based planted clique detection RR Nadakuditi 2012 IEEE Statistical Signal Processing Workshop (SSP), 129-132, 2012 | 28 | 2012 |

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), 1-9, 2017 | 23 | 2017 |

Improved robust PCA using low-rank denoising with optimal singular value shrinkage BE Moore, RR Nadakuditi, JA Fessler 2014 IEEE workshop on statistical signal processing (SSP), 13-16, 2014 | 23 | 2014 |

A channel subspace post-filtering approach to adaptive least-squares estimation R Nadakuditi, JC Preisig IEEE transactions on signal processing 52 (7), 1901-1914, 2004 | 21 | 2004 |