| The first super-Earth detection from the high cadence and high radial velocity precision Dharma Planet Survey B Ma, J Ge, M Muterspaugh, MA Singer, GW Henry, ... Monthly Notices of the Royal Astronomical Society 480 (2), 2411-2422, 2018 | 31 | 2018 |
| MIRKWOOD: fast and accurate SED modeling using machine learning S Gilda, S Lower, D Narayanan The Astrophysical Journal 916 (1), 43, 2021 | 26 | 2021 |
| Automatic Kalman-filter-based wavelet shrinkage denoising of 1D stellar spectra S Gilda, Z Slepian Monthly Notices of the Royal Astronomical Society 490 (4), 5249-5269, 2019 | 14 | 2019 |
| Uncertainty-aware learning for improvements in image quality of the Canada–France–Hawaii Telescope S Gilda, SC Draper, S Fabbro, W Mahoney, S Prunet, K Withington, ... Monthly Notices of the Royal Astronomical Society 510 (1), 870-902, 2022 | 5 | 2022 |
| Gamma-ray Bursts as distance indicators through a machine learning approach M Dainotti, V Petrosian, M Bogdan, B Miasojedow, S Nagataki, T Hastie, ... arXiv preprint arXiv:1907.05074, 2019 | 5 | 2019 |
| Unsupervised domain adaptation for constraining star formation histories S Gilda, A de Mathelin, S Bellstedt, G Richard arXiv preprint arXiv:2112.14072, 2021 | 3 | 2021 |
| mirkwood: SED modeling using machine learning S Gilda, S Lower, D Narayanan Astrophysics Source Code Library, ascl: 2102.017, 2021 | 3 | 2021 |
| Astronomical image quality prediction based on environmental and telescope operating conditions S Gilda, YS Ting, K Withington, M Wilson, S Prunet, W Mahoney, S Fabbro, ... arXiv preprint arXiv:2011.03132, 2020 | 3 | 2020 |
| Parameterization of marvels spectra using deep learning S Gilda, J Ge American Astronomical Society Meeting Abstracts# 231 231, 349.02, 2018 | 3 | 2018 |
| deep-REMAP: Parameterization of Stellar Spectra Using Regularized Multi-Task Learning S Gilda arXiv preprint arXiv:2311.03738, 2023 | 2 | 2023 |
| SED Fitting in the Modern Era: Fast and Accurate Machine-Learning Assisted Software D Narayanan, S Gilda, S Lower HST Proposal -- https://archive.stsci.edu/proposal_search.php?id=16626 …, 2021 | 2 | 2021 |
| SED Analysis using Machine Learning Algorithms S Gilda, S Lower, D Narayanan American Astronomical Society Meeting Abstracts 53 (6), 119.03, 2021 | 2 | 2021 |
| Feature Selection for Better Spectral Characterization or: How I Learned to Start Worrying and Love Ensembles S Gilda Astronomical Data Analysis Software and Systems XXVIII 523, 67, 2019 | 2 | 2019 |
| Adaptive Kalman Filter-based Wavelet Shrinkage Denoising of Stellar Spectra S Gilda American Astronomical Society Meeting Abstracts# 233 233, 420.08, 2019 | 1 | 2019 |
| tsbootstrap: Enhancing Time Series Analysis with Advanced Bootstrapping Techniques S Gilda, B Heidrich, F Kiraly arXiv preprint arXiv:2404.15227, 2024 | | 2024 |
| Beyond mirkwood: Enhancing SED Modeling with Conformal Predictions S Gilda Astronomy 3 (1), 14-20, 2024 | | 2024 |
| Robust Calibration For Improved Weather Prediction Under Distributional Shift S Gilda, N Bhandari, W Mak, A Panizza arXiv preprint arXiv:2401.04144, 2024 | | 2024 |
| Using machine learning to improve sleep habits in Dementia patients X Yang, J Sucevic, R Sahoo, T Martinelli, C Yuan Li, S Gilda, GN Domide, ... https://zenodo.org/records/6798769, 2022 | | 2022 |
| SED Analysis using Machine Learning Algorithms DN Sankalp Gilda, Sidney Lower Bulletin of the AAS 53 (6), 2021 | | 2021 |
| tsbootstrap S Gilda 10.5281/zenodo.8226495, 0 | | |
Andrew SheinisDirector of Engineering, Canada France Hawaii TelescopeEmail verificata su cfht.hawaii.edu
