# Publications

Super resolution phase retrieval for sparse signals,” IEEE Transactions on Signal Processing, vol. 67, no. 18, 2019. arXiv:1808.01961 [cs.IT]Abstract

, “ MMSE Approximation For Sparse Coding Algorithms Using Stochastic Resonance,” IEEE Transactions on Signal Processing, vol. 67, no. 17, 2019. arXiv:1806.10171 [eess.SP]Abstract

, “ Subspace Estimation from Incomplete Observations: A High-Dimensional Analysis,” IEEE Journal of Selected Topics in Signal Processing, vol. 12, no. 6, 2018. arXiv:1805.06834 [cs.LG]Abstract subspace_estimation.pdf

, “ A Modern Perspective on Streaming PCA and Subspace Tracking: The Missing Data Case,” Proceedings of the IEEE, vol. 106, no. 8, pp. 1293-1310, 2018.Abstract procieee_tracking_final.pdf

, “ Sparse Representation in Fourier and Local Bases Using ProSparse: A Probabilistic Analysis,” IEEE Transactions on Information Theory, vol. 64, no. 4, pp. 2639-2647, 2018. arXiv:1611.07971 [cs.IT]Abstract

, “ Phase Retrieval via Linear Programming: Fundamental Limits and Algorithmic Improvements,” in 55th Annual Allerton Conference on Communication, Control, and Computing, 2017. arXiv:1710.05234 [cs.IT]Abstract

, “ The Scaling Limit of High-Dimensional Online Independent Component Analysis,” in Conference on Neural Information Processing Systems (NIPS), 2017.Abstract nips_2017.pdf

, “*(acceptance rate: 112/3240 = 3.5%)*

**Spotlight paper** Fundamental Limits of PhaseMax for Phase Retrieval: A Replica Analysis,” in the 7th IEEE Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP), 2017. arXiv:1708.03355Abstract

, “This paper won the Best Student Paper Award (First Prize) at the 2017 IEEE CAMSAP Workshop.

The predictions made in this paper via the non-rigorous replica method has since been rigorously established in our latest work.

Spectral Initialization for Nonconvex Estimation: High-Dimensional Limit and Phase Transitions,” in IEEE International Symposium on Information Theory (ISIT), 2017.Abstract sp_init_isit.pdf

, “ Subspace Estimation from Incomplete Observations: A Precise High-Dimensional Analysis,” in Signal Processing with Adaptive Structured Representatives (SPARS) Workshop, 2017.Abstract sparse17_ode.pdf

, “ A Tale of Two Bases: Local-Nonlocal Regularization on Image Patches with Convolution Framelets,” SIAM Journal on Imaging Sciences, vol. 10, no. 2, pp. 711-750, 2017.Abstract m109144.pdf

, “ Understanding Symmetric Smoothing Filters: A Gaussian Mixture Model Perspective,” IEEE Transactions on Image Processing, vol. 26, no. 11, pp. 5107-5121, 2017. arXiv:1601.00088Abstract

, “ Online Learning for Sparse PCA in High Dimensions: Exact Dynamics and Phase Transitions,” in IEEE Information Theory Workshop (ITW), 2016.Abstract spca.pdf

, “ ProSparse denoise: Prony's based sparsity pattern recovery in the presence of noise,” in Proc. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2016.

, “ Kaczmarz Method for Solving Quadratic Equations,” IEEE Signal Processing Letters, vol. 23, no. 9, pp. 1183-1187, 2016.Abstract kaczmarz_quadratic_final.pdf

, “ Decomposition space-variant blur in image deconvolution,” IEEE Signal Processing Letters, vol. 23, no. 3, pp. 346-350, 2016.Abstract main_rev.pdf

, “ Dynamics of Individual Perceptual Decisions,” Journal of Neurophysiology, vol. 115, no. 1, 2016. Publisher's VersionAbstract

, “ Matched Signal Detection on Graphs: Theory and Application to Brain Imaging Data Classification,” NeuroImage, vol. 125, pp. 587-600, 2016.Abstract

, “ A Distributed Gauss-Newton Method for Power System State Estimation,” IEEE Transactions on Power Systems, vol. 31, no. 5, pp. 3804-3815, 2016.Abstract

, “ Sampling Sparse Signals on the Sphere: Algorithms and Applications,” IEEE Transactions on Signal Processing, vol. 64, no. 1, pp. 189-202, 2016. arXiv:1502.07577Abstract

, “