| An application of deep learning in the analysis of stellar spectra S Fabbro, KA Venn, T O'Briain, S Bialek, CL Kielty, F Jahandar, S Monty Monthly Notices of the Royal Astronomical Society 475 (3), 2978-2993, 2018 | 107 | 2018 |
| The Pristine survey–X. A large population of low-metallicity stars permeates the Galactic disc F Sestito, NF Martin, E Starkenburg, A Arentsen, RA Ibata, N Longeard, ... Monthly Notices of the Royal Astronomical Society: Letters 497 (1), L7-L12, 2020 | 70 | 2020 |
| The Pristine survey–XII. Gemini-GRACES chemo-dynamical study of newly discovered extremely metal-poor stars in the Galaxy CL Kielty, KA Venn, F Sestito, E Starkenburg, NF Martin, DS Aguado, ... Monthly Notices of the Royal Astronomical Society 506 (1), 1438-1461, 2021 | 32 | 2021 |
| Cycle-starnet: Bridging the gap between theory and data by leveraging large data sets T O’Briain, YS Ting, S Fabbro, MY Kwang, K Venn, S Bialek The Astrophysical Journal 906 (2), 130, 2021 | 25 | 2021 |
| Assessing the performance of LTE and NLTE synthetic stellar spectra in a machine learning framework S Bialek, S Fabbro, KA Venn, N Kumar, T O’Briain, KM Yi Monthly Notices of the Royal Astronomical Society 498 (3), 3817-3834, 2020 | 18 | 2020 |
| A 3D printed modular phantom for quality assurance of image‐guided small animal irradiators: Design, imaging experiments, and Monte Carlo simulations DY Breitkreutz, S Bialek, B Vojnovic, A Kavanagh, CD Johnstone, ... Medical Physics 46 (5), 2015-2024, 2019 | 7 | 2019 |
| Starnet: A deep learning analysis of infrared stellar spectra CL Kielty, S Bialek, S Fabbro, KA Venn, T O'Briain, F Jahandar, S Monty Software and cyberinfrastructure for astronomy v 10707, 814-824, 2018 | 4 | 2018 |
| StarUnLink: identifying and mitigating signals from communication satellites in stellar spectral surveys S Bialek, S Lucatello, S Fabbro, KM Yi, KA Venn Monthly Notices of the Royal Astronomical Society 524 (1), 529-541, 2023 | 2 | 2023 |
| Interpreting Stellar Spectra with Unsupervised Domain Adaptation T O'Briain, YS Ting, S Fabbro, KM Yi, K Venn, S Bialek arXiv preprint arXiv:2007.03112, 2020 | 1 | 2020 |
| Deep learning analyses of synthetic spectral libraries with an application to the Gaia-ESO database S Bialek | 1 | 2019 |
| StarNet: An application of deep learning in the analysis of stellar spectra C Kielty, S Bialek, S Fabbro, K Venn, T O'Briain, F Jahandar, S Monty American Astronomical Society Meeting Abstracts# 232 232, 223.09, 2018 | 1 | 2018 |
| An Application of Deep Neural Networks in the Analysis of Stellar Spectra S Fabbro, K Venn, T O'Briain, S Bialek, C Kielty, F Jahandar, S Monty arXiv preprint arXiv:1709.09182, 2017 | 1 | 2017 |
| DanceCam: atmospheric turbulence mitigation in wide-field astronomical images with short-exposure video streams S Bialek, E Bertin, S Fabbro, H Bouy, JP Rivet, O Lai, JC Cuillandre Monthly Notices of the Royal Astronomical Society, stae1018, 2024 | | 2024 |
| VizieR Online Data Catalog: Galactic disc Pristine low-metallicity stars (Sestito+, 2020) F Sestito, NF Martin, E Starkenburg, A Arentsen, RA Ibata, N Longeard, ... VizieR Online Data Catalog, J/MNRAS/497/L7, 2023 | | 2023 |
| Skyward AI: Advancing Astronomy with Intelligent Machines S Bialek | | 2023 |
| Stellar Parameters with Deep Learning S Fabbro, K Venn, T O'Briain, S Bialek, C Kielty, F Jahandar, S Monty Astronomical Data Analysis Software and Systems XXVII 522, 393, 2020 | | 2020 |
| 3D Printed Phantom for MicroCT Imaging QA S Bialek, B Vojanovic, A Kavanagh, C Johnstone, T Kanesalingam, ... MEDICAL PHYSICS 45 (6), E421-E421, 2018 | | 2018 |
Kwang Moo YiAssistant Professor of Computer Science at the University of British ColumbiaVerified email at cs.ubc.ca
