115 citations · 166 across the 7 of their papers we have counts for
8 papers
Ambitious Data Science Can Be Painless
Hatef Monajemi, Riccardo Murri, Eric Jonas +3
Modern data science research can involve massive computational experimentation; an ambitious PhD in computational fields may do experiments consuming several million CPU hours. Tra…
Neural Proximal Gradient Descent for Compressive Imaging
Morteza Mardani, Qingyun Sun, Shreyas Vasawanala +4
Recovering high-resolution images from limited sensory data typically leads to a serious ill-posed inverse problem, demanding inversion algorithms that effectively capture the prio…
Recurrent Generative Adversarial Networks for Proximal Learning and Automated Compressive Image Recovery
Morteza Mardani, Hatef Monajemi, Vardan Papyan +3
Recovering images from undersampled linear measurements typically leads to an ill-posed linear inverse problem, that asks for proper statistical priors. Building effective priors i…
Nonparametric estimation of galaxy cluster's emissivity and point source detection in astrophysics with two lasso penalties
Jairo Diaz-Rodriguez, Dominique Eckert, Hatef Monajemi +2
Astrophysicists are interested in recovering the 3D gas emissivity of a galaxy cluster from a 2D image taken by a telescope. A blurring phenomenon and presence of point sources mak…
Incoherence of Partial-Component Sampling in multidimensional NMR
Hatef Monajemi, David L. Donoho, Jeffrey C. Hoch +1
In NMR spectroscopy, undersampling in the indirect dimensions causes reconstruction artifacts whose size can be bounded using the so-called {\it coherence}. In experiments with mul…
Sparsity/Undersampling Tradeoffs in Anisotropic Undersampling, with Applications in MR Imaging/Spectroscopy
Hatef Monajemi, David L. Donoho
We study anisotropic undersampling schemes like those used in multi-dimensional NMR spectroscopy and MR imaging, which sample exhaustively in certain time dimensions and randomly i…