| Technology readiness levels for machine learning systems A Lavin, CM Gilligan-Lee, A Visnjic, S Ganju, D Newman, S Ganguly, ... Nature Communications 13 (1), 6039, 2022 | 215 | 2022 |
| In-orbit demonstration of a re-trainable machine learning payload for processing optical imagery G Mateo-Garcia, J Veitch-Michaelis, C Purcell, N Longepe, S Reid, ... Scientific Reports 13 (1), 10391, 2023 | 43 | 2023 |
| NASA's asteroid grand challenge: strategy, results, and lessons learned JL Gustetic, V Friedensen, JL Kessler, S Jackson, J Parr Space Policy 44, 1-13, 2018 | 17 | 2018 |
| Improving thermospheric density predictions in low‐Earth orbit with machine learning G Acciarini, E Brown, T Berger, M Guhathakurta, J Parr, C Bridges, ... Space weather 22 (2), e2023SW003652, 2024 | 13 | 2024 |
| SpaceML: Distributed open-source research with citizen scientists for the advancement of space technology for NASA A Koul, S Ganju, M Kasam, J Parr arXiv preprint arXiv:2012.10610, 2020 | 9 | 2020 |
| A Foundation Model for the Solar Dynamics Observatory J Walsh, DG Gass, RR Pollan, PJ Wright, R Galvez, N Kasmanoff, ... arXiv preprint arXiv:2410.02530, 2024 | 7 | 2024 |
| Advancing Astrobiology Through Public/Private Partnership: The FDL Model NA Cabrol, WH Diamond, N Altaf, J Bishop, SL Cady, L Fenton, N Hinman, ... Lunar and Planetary Science Conference 49, 1275, 2018 | 6 | 2018 |
| Real-time quantitative and qualitative measurement of organizational culture G Huff, J Reynolds, D Gorman, MS Barnett, J Cole, M Oxley, J Parr US Patent App. 13/175,460, 2013 | 5 | 2013 |
| Live twinning: A vision of ml enabled assets in leo for rapid response to natural catastrophes J Parr, G Acciarini, C Bridges, G Mateo-Garcia, E Portales-Julia, C Purcell, ... IGARSS 2024-2024 IEEE International Geoscience and Remote Sensing Symposium …, 2024 | 3 | 2024 |
| Learnings from Frontier Development Lab and SpaceML--AI Accelerators for NASA and ESA S Ganju, A Koul, A Lavin, J Veitch-Michaelis, M Kasam, J Parr arXiv preprint arXiv:2011.04776, 2020 | 3 | 2020 |
| Application of machine learning for planetary defense, Three Case Studies J Parr, F Marchis, M Busch, P Jenniskens, J Galache, E Dahlstrom May, 2019 | 1 | 2019 |
| Novel Observing Strategies for a Changing Planet Poster B Smith, N Longepe, J Parr, MM Little AGU24, 2024 | | 2024 |
| ITI: An Instrument-to-Instrument translation tool for Heliophysics and Earth science R Jarolim, C Schirninger, JE Johnson, A Jungbluth, L Freischem, ... AGU24, 2024 | | 2024 |
| Shared Software Environment for Heliophysics BA Thomas, S Polson, R Ringuette, J Reerink, AK Antunes, J Parr, ... AGU Fall Meeting Abstracts 2024 (217), IN13B-217, 2024 | | 2024 |
| Aggregated AI: How Heliolab's commitment to Open Science is creating a critical mass of new AI capabilities. A Spalding, J Parr, C Purcell AGU Fall Meeting Abstracts 2024 (218), IN13C-218, 2024 | | 2024 |
| Beyond reproducibility at the Frontier Development Lab (FDL): Community driven continuous optimization for the SDO machine learning dataset (SDOML). P Wright, M Jin, CMM Cheung, J Parr AGU Fall Meeting Abstracts 2021, IN12A-01, 2021 | | 2021 |
| NASA Science Mission Directorate Artificial Intelligence Workshop Update M Maskey, M Ansdell, S Costes, M Guhathakurta, R Ojha, M Little, ... AGU Fall Meeting Abstracts 2021, IN11B-01, 2021 | | 2021 |
| Report on Workshop on Artificial Intelligence in Strategic Planning and Science Prioritization BA Thomas, LM Barbier, JA Crooke, H Thronson, A Lowndes, ... Workshop on Artificial Intelligence in Strategic Planning and Science …, 2021 | | 2021 |
| Ml Applications Supporting the Usability of Big Data in Earth Science J Parr AGU Fall Meeting Abstracts 2019, IN53C-0749, 2019 | | 2019 |
| NASA FDL: Accelerating Artificial Intelligence Applications in the Space Sciences. M Navas-Moreno, J Parr, EL Dahlstrom, SB Jennings 2017 AGU Fall Meeting, 2017 | | 2017 |
Atılım Güneş BaydinDiffractive LabsVerified email at robots.ox.ac.uk
