| Enabling real-time multi-messenger astrophysics discoveries with deep learning EA Huerta, G Allen, I Andreoni, JM Antelis, E Bachelet, GB Berriman, ... Nature Reviews Physics 1 (10), 600-608, 2019 | 74 | 2019 |
| Deep learning at scale for the construction of galaxy catalogs in the Dark Energy Survey A Khan, EA Huerta, S Wang, R Gruendl, E Jennings, H Zheng Physics Letters B 795, 248-258, 2019 | 58 | 2019 |
| Deep learning ensemble for real-time gravitational wave detection of spinning binary black hole mergers W Wei, A Khan, EA Huerta, X Huang, M Tian Physics Letters B 812, 136029, 2021 | 46 | 2021 |
| Accelerated, scalable and reproducible AI-driven gravitational wave detection EA Huerta, A Khan, X Huang, M Tian, M Levental, R Chard, W Wei, ... Nature Astronomy 5 (10), 1062-1068, 2021 | 44 | 2021 |
| Convergence of artificial intelligence and high performance computing on NSF-supported cyberinfrastructure EA Huerta, A Khan, E Davis, C Bushell, WD Gropp, DS Katz, ... Journal of Big Data 7, 1-12, 2020 | 36 | 2020 |
| Physics-inspired deep learning to characterize the signal manifold of quasi-circular, spinning, non-precessing binary black hole mergers A Khan, EA Huerta, A Das Physics Letters B 808, 135628, 2020 | 26 | 2020 |
| 280 GHz Focal Plane Unit Design and Characterization for the Spider-2 Suborbital Polarimeter AS Bergman, PAR Ade, S Akers, M Amiri, JA Austermann, JA Beall, ... Journal of Low Temperature Physics 193, 1075-1084, 2018 | 18 | 2018 |
| Inference-optimized AI and high performance computing for gravitational wave detection at scale P Chaturvedi, A Khan, M Tian, EA Huerta, H Zheng Frontiers in Artificial Intelligence 5, 828672, 2022 | 14 | 2022 |
| Interpretable AI forecasting for numerical relativity waveforms of quasicircular, spinning, nonprecessing binary black hole mergers A Khan, EA Huerta, H Zheng Physical Review D 105 (2), 024024, 2022 | 13 | 2022 |
| Deep Learning for Multi-Messenger Astrophysics: A Gateway for Discovery in the Big Data Era G Allen, I Andreoni, E Bachelet, GB Berriman, FB Bianco, R Biswas, ... arXiv preprint arXiv:1902.00522, 2019 | 7 | 2019 |
| AI and extreme scale computing to learn and infer the physics of higher order gravitational wave modes of quasi-circular, spinning, non-precessing black hole mergers A Khan, EA Huerta, P Kumar Physics Letters B 835, 137505, 2022 | 6 | 2022 |
| Unsupervised learning and data clustering for the construction of Galaxy Catalogs in the Dark Energy Survey A Khan, EA Huerta, S Wang, R Gruendl arXiv preprint arXiv:1812.02183, 2018 | 4 | 2018 |
| Interpreting a Machine Learning Model for Detecting Gravitational Waves M Safarzadeh, A Khan, EA Huerta, M Wattenberg arXiv preprint arXiv:2202.07399, 2022 | 1 | 2022 |
| Deep Learning at Scale for Gravitational Wave Parameter Estimation from Surrogate Model Waveforms of Spinning Black Hole Mergers A Khan, A Das, E Huerta Bulletin of the American Physical Society 65, 2020 | 1 | 2020 |
| Inference-optimized AI and high performance computing for gravitational wave detection at scale E Huerta, P Chaturvedi, A Khan, M Tian, H Zheng Frontiers in Artificial Intelligence, 10, 0 | | |
Eliu HuertaArgonne National Laboratory and The University of ChicagoVerified email at anl.gov
