| 2D empirical transforms. Wavelets, ridgelets, and curvelets revisited J Gilles, G Tran, S Osher SIAM Journal on Imaging Sciences 7 (1), 157-186, 2014 | 221 | 2014 |
| Exact recovery of chaotic systems from highly corrupted data G Tran, R Ward Multiscale Modeling & Simulation 15 (3), 1108-1129, 2017 | 205 | 2017 |
| Extracting sparse high-dimensional dynamics from limited data H Schaeffer, G Tran, R Ward SIAM Journal on Applied Mathematics 78 (6), 3279-3295, 2018 | 192 | 2018 |
| NON-LOCAL RETINEX–A UNIFYING FRAMEWORK AND BEYOND D Zosso, G Tran, SJ Osher SIAM Journal on Imaging Sciences 8 (2), 787–826, 2015 | 107 | 2015 |
| Fiber orientation and compartment parameter estimation from multi-shell diffusion imaging G Tran, Y Shi IEEE transactions on medical imaging 34 (11), 2320-2332, 2015 | 70 | 2015 |
| Extracting structured dynamical systems using sparse optimization with very few samples H Schaeffer, G Tran, R Ward, L Zhang Multiscale Modeling & Simulation 18 (4), 1435-1461, 2020 | 65 | 2020 |
| Generalization bounds for sparse random feature expansions A Hashemi, H Schaeffer, R Shi, U Topcu, G Tran, R Ward Applied and Computational Harmonic Analysis, 2023, 2021 | 61 | 2021 |
| A unifying retinex model based on non-local differential operators D Zosso, G Tran, S Osher Computational Imaging XI 8657, 865702, 2013 | 56 | 2013 |
| An Penalty Method for General Obstacle Problems G Tran, H Schaeffer, WM Feldman, SJ Osher SIAM Journal on Applied Mathematics 75 (4), 1424-1444, 2015 | 43 | 2015 |
| Defect-tolerant aligned dipoles within two-dimensional plastic lattices JC Thomas, JJ Schwartz, JN Hohman, SA Claridge, HS Auluck, ... ACS nano 9 (5), 4734-4742, 2015 | 40 | 2015 |
| Learning dynamical systems and bifurcation via group sparsity H Schaeffer, G Tran, R Ward arXiv preprint arXiv:1709.01558, 2017 | 34 | 2017 |
| PDEs with Compressed Solutions RE Caflisch, SJ Osher, H Schaeffer, G Tran Communications in Mathematical Sciences 13 (8), 2155--2176, 2015 | 26 | 2015 |
| Recovery guarantees for polynomial coefficients from weakly dependent data with outliers LST Ho, H Schaeffer, G Tran, R Ward Journal of Approximation Theory 259, 105472, 2020 | 19* | 2020 |
| SRMD: Sparse random mode decomposition N Richardson, H Schaeffer, G Tran Communications on Applied Mathematics and Computation 6 (2), 879-906, 2024 | 17 | 2024 |
| Fast local trust region technique for diffusion tensor registration using exact reorientation and regularization J Li, Y Shi, G Tran, I Dinov, DJJ Wang, A Toga IEEE transactions on medical imaging 33 (5), 1005-1022, 2013 | 17 | 2013 |
| HARFE: hard-ridge random feature expansion E Saha, H Schaeffer, G Tran Sampling Theory, Signal Processing, and Data Analysis 21 (2), 27, 2023 | 16 | 2023 |
| Identifying perceptually salient features on 2d shapes LJ Larsson, G Morin, A Begault, R Chaine, J Abiva, E Hubert, M Hurdal, ... Research in Shape Modeling: Los Angeles, July 2013, 129-153, 2015 | 12 | 2015 |
| SPADE4: Sparsity and delay embedding based forecasting of epidemics E Saha, LST Ho, G Tran Bulletin of Mathematical Biology 85 (8), 71, 2023 | 7 | 2023 |
| Adaptively constrained convex optimization for accurate fiber orientation estimation with high order spherical harmonics G Tran, Y Shi Medical Image Computing and Computer-Assisted Intervention–MICCAI 2013: 16th …, 2013 | 6 | 2013 |
| Adaptive group Lasso neural network models for functions of few variables and time-dependent data LST Ho, N Richardson, G Tran Sampling Theory, Signal Processing, and Data Analysis 21 (2), 28, 2023 | 4 | 2023 |
S osherVerified email at math.ucla.edu
