| Scalable Gaussian process computations using hierarchical matrices CJ Geoga, M Anitescu, ML Stein Journal of Computational and Graphical Statistics 29 (2), 227-237, 2020 | 52 | 2020 |
| Fitting Matérn smoothness parameters using automatic differentiation CJ Geoga, O Marin, M Schanen, ML Stein Statistics and Computing 33 (2), 48, 2023 | 12 | 2023 |
| Frequency–wavenumber spectral analysis of spatio-temporal flows CJ Geoga, CL Haley, AR Siegel, M Anitescu Journal of Fluid Mechanics 848, 545-559, 2018 | 12 | 2018 |
| On automatic differentiation for the Mat\'ern covariance O Marin, C Geoga, M Schanen arXiv preprint arXiv:2201.00262, 2022 | 4 | 2022 |
| Flexible nonstationary spatiotemporal modeling of high‐frequency monitoring data CJ Geoga, M Anitescu, ML Stein Environmetrics 32 (5), e2670, 2021 | 3 | 2021 |
| Scalable computations for nonstationary Gaussian processes PG Beckman, CJ Geoga, ML Stein, M Anitescu Statistics and Computing 33 (4), 84, 2023 | 2 | 2023 |
| Multitaper. jl: A Julia package for frequency domain analysis of time series CL Haley, CJ Geoga Journal of Open Source Software 5 (55), 2463, 2020 | 2 | 2020 |
| A scalable method to exploit screening in gaussian process models with noise CJ Geoga, ML Stein Journal of Computational and Graphical Statistics, 1-11, 2023 | 1 | 2023 |
| Models and Computation for Nonstationary Processes at Scale CJ Geoga Rutgers The State University of New Jersey, School of Graduate Studies, 2023 | | 2023 |
| Spectral analysis for comparison of second-order flow structure in DNS simulations CL Haley, CJ Geoga, A Siegel, M Anitescu | | 2017 |
| Adaptive Nonparametric Spectra applied to Uncertainty Propagation and Kernel Learning CL Haley, CJ Geoga, M Anitescu | | |
