| Dynamical sampling A Aldroubi, C Cabrelli, U Molter, S Tang Applied and Computational Harmonic Analysis 42 (3), 378-401, 2017 | 127 | 2017 |
| Nonparametric inference of interaction laws in systems of agents from trajectory data F Lu, M Zhong, S Tang, M Maggioni Proceedings of the National Academy of Sciences 116 (29), 14424-14433, 2019 | 109 | 2019 |
| Learning interaction kernels in heterogeneous systems of agents from multiple trajectories F Lu, M Maggioni, S Tang Journal of machine learning research 22 (32), 1-67, 2021 | 57 | 2021 |
| On the identifiability of interaction functions in systems of interacting particles Z Li, F Lu, M Maggioni, S Tang, C Zhang Stochastic Processes and their Applications 132, 135-163, 2021 | 31 | 2021 |
| Dynamical sampling in hybrid shift invariant spaces R Aceska, S Tang, V Furst Operator Methods in Wavelets, Tilings, and Frames 626, 149, 2014 | 23 | 2014 |
| Sensor calibration for off-the-grid spectral estimation YC Eldar, W Liao, S Tang Applied and Computational Harmonic Analysis 48 (2), 570-598, 2020 | 22 | 2020 |
| System identification in dynamical sampling S Tang Advances in Computational Mathematics 43, 555-580, 2017 | 22 | 2017 |
| Multidimensional signal recovery in discrete evolution systems via spatiotemporal trade off R Aceska, A Petrosyan, S Tang Sampling Theory in Signal and Image Processing 14, 153-169, 2015 | 14 | 2015 |
| Learning theory for inferring interaction kernels in second-order interacting agent systems J Miller, S Tang, M Zhong, M Maggioni arXiv preprint arXiv:2010.03729, 2020 | 11 | 2020 |
| Phaseless reconstruction from space–time samples A Aldroubi, I Krishtal, S Tang Applied and Computational Harmonic Analysis 48 (1), 395-414, 2020 | 10 | 2020 |
| Learning interaction kernels in stochastic systems of interacting particles from multiple trajectories F Lu, M Maggioni, S Tang Foundations of Computational Mathematics, 1013–1067, 2021 | 9 | 2021 |
| Data-driven discovery of interacting particle systems using Gaussian processes J Feng, Y Ren, S Tang arXiv preprint arXiv:2106.02735, 2021 | 9 | 2021 |
| Higher-order error estimates for physics-informed neural networks approximating the primitive equations R Hu, Q Lin, A Raydan, S Tang Partial Differential Equations and Applications 4 (4), 34, 2023 | 7 | 2023 |
| An interpretable hybrid predictive model of COVID-19 cases using autoregressive model and LSTM Y Zhang, S Tang, G Yu Scientific reports 13 (1), 6708, 2023 | 6 | 2023 |
| Scalable marginalization of correlated latent variables with applications to learning particle interaction kernels M Gu, X Liu, X Fang, S Tang arXiv preprint arXiv:2203.08389, 2022 | 5 | 2022 |
| Estimate the spectrum of affine dynamical systems from partial observations of a single trajectory data J Cheng, S Tang Inverse Problems 38 (1), 015004, 2021 | 5 | 2021 |
| Phase retrieval of evolving signals from space-time samples A Aldroubi, I Krishtal, S Tang 2017 International Conference on Sampling Theory and Applications (SampTA …, 2017 | 5 | 2017 |
| Learning theory for inferring interaction kernels in second-order interacting agent systems J Miller, S Tang, M Zhong, M Maggioni Sampling Theory, Signal Processing, and Data Analysis 21 (1), 21, 2023 | 4 | 2023 |
| Robust recovery of bandlimited graph signals via randomized dynamical sampling L Huang, D Needell, S Tang arXiv preprint arXiv:2109.14079, 2021 | 4 | 2021 |
| Universal spatiotemporal sampling sets for discrete spatially invariant evolution processes S Tang IEEE Transactions on Information Theory 63 (9), 5518-5528, 2017 | 3 | 2017 |
Mauro MaggioniBloomberg Distinguished Professor of Mathematics, and Applied Mathematics and StatisticsBestätigte E-Mail-Adresse bei jhu.edu
