PUBLICATIONS
-
2024
Instrument-To-Instrument translation: Instrumental advances drive restoration of solar observation series via deep learning, Nature Communications
DOI: 10.21203/rs.3.rs-1021940/v1Machine learning discovery of cost-efficient dry cooler designs for concentrated solar power plants - paper, Nature Journal
DOI 10.1038/s41598-024-67346-6SuNeRF: 3D Reconstruction of the Solar EUV Corona Using Neural Radiance Fields - Astrophysical Journal Letters (FDL USA 2022)
DOI 10.3847/2041-8213/ad12d2
Arxiv 2401.16388Improving Thermospheric Density Predictions in Low-Earth Orbit With Machine Learning- AGU Space Weather Volume 22, Issue 2
DOI 10.1029/2023SW003652Physically Motivated Deep Learning to Superresolve and Cross Calibrate Solar Magnetograms - The Astrophysical Journal
DOI 10.3847/1538-4365/ad12c2Virtual EVE: a Deep Learning Model for Solar Irradiance Prediction - Paper
Arxiv 2408.17430v1Multiscale Geoeffectiveness Forecasting using SHEATH and DAGGER - Poster, Astronomical Society of India 2024
SDO-FM is building a foundation model (FM) using Solar Dynamics Observatory (SDO) data, Software for the NASA Science Mission Directorate Workshop 2024
Training a Foundation Model for the Sun, US-RSE 2024
Instrument-to-Instrument translation: An AI tool to intercalibrate, enhance and super-resolve solar observations, EGU General Assembly 2024
DOI: 10.5194/egusphere-egu24-15813Instrument to Instrument (ITI) Translation, NASA AI Conference 2024
Instrument-to-Instrument translation: An AI tool to enhance, intercalibrate and super-resolve solar observations, Seventh Parker Heliophysics Scholars 2024
Spectral Irradiance of the 3D Sun on Mars, European Space Weather Week 2024 (FDL-X Heliolab 2024)
ITI: An Instrument-to-Instrument translation tool for Heliophysics and Earth science, AGU 2024
3D Cloud Reconstruction through Geospatially-aware Masked Autoencoders, NeurIPS 2024 Workshop on Machine Learning for Physical Sciences
Deep Learning image burst stacking to reconstruct high-resolution ground-based solar observations, 17th European Solar Physics Meeting ESPM-17
SuNeRF: AI enables 3D reconstruction of the solar EUV corona, (in prep).
2023
A Scientific Cloud Computing Platform for Ingestion and Processing of SDO Data - 4th Eddy Cross-Disciplinary Symposium (FDL-X 2023)Leveraging Artificial Intelligence to Enhance the Science Return of 4𝛑 Solar Constellations - Paper, Bulletin of the AAS Vol 55, Issue 3 2023 (FDL-X 2022)
DOI: 10.3847/25c2cfeb.aa5f09f6AIA is All You Need: SDO MEGS A&B virtualization via Convolutional Deep Learning - 4th Eddy Cross-Disciplinary Symposium (FDL-X 2023)
A Novel Synthesis of SDO EVE MEGS A&B Spectral Irradiance Data through Convolutional Deep Learning - AGU 2023 (FDL-X 2023)
A Scientific Cloud Computing Platform for Ingestion and Processing of SDO Data - DASH 2023 (FDL-X 2023)
Virtual EVE: a Deep Learning Model for Solar Irradiance Prediction - NeurIPS 2023 (FDL-X 2023)
AI Inference Products, Foundation Models and multi-domain approaches to NASA Heliophysics - 4th Eddy Cross-Disciplinary Symposium (FDL-X Helio)
Multiscale Geoeffectiveness Forecasting using SHEATH and DAGGER- 4th Eddy Cross-Disciplinary Symposium (FDL-X 2023)
Multiscale Geoeffectiveness Forecasting: Upgrading the DAGGER Pipeline- AGU 2023 (FDL-X 2023)
Improving thermospheric drag modeling with EUV images: an FDL-X 2023 project - 4th Eddy Cross-Disciplinary Symposium (FDL-X 2023)
Incorporating Direct EUV Irradiance from Solar Images into Thermospheric Density Modelling with Machine Learning- AGU 2023 (FDL-X 2023)
High-Cadence Thermospheric Density Estimation enabled by Machine Learning on Solar Imagery- NeurIPS 2023 (FDL-X 2023)
Karman - a Machine Learning Software Package for Benchmarking Thermospheric Density Models - AMOS 2023 (FDL-X 2023)
A Federated Distributed Learning Benchmark for Solar Wind Speed Forecasting Using Solar EUV Images - Space Weather Workshop 2023 (FDLUSA 2022)
2022
Global Geomagnetic Perturbation Forecasting Using Deep Learning - AGU Space Weather Volume 20, Issue 6
DOI 10.1029/2022SW003045
Arxiv 2205.12734v1
2021
Modeling and forecasting ground geomagnetic perturbations using deep learning on spherical harmonics - COSPAR2021 Machine Learning for Space Sciences (You Tube Link) (FDL USA 2020)
Multichannel autocalibration for the Atmospheric Imaging Assembly using machine learning - Astronomy & Astrophysics Journal (FDL USA 2019)
DOI 10.1051/0004-6361/202040051Arxiv 2012.14023v4
Auto-Calibration and High-Fidelity Virtual Observations of Remote Sensing Solar Telescopes with Deep Learning - JPL AI and Data Science Workshop 2021 (FDL USA 2019) [I]
Correlation of Auroral Dynamics and GNSS Scintillation with an Autoencoder - JPL AI and Data Science Workshop 2021 (FDL USA 2019) [I]
Autonomous deep-space missions: can deep learning be used to optimize data transmission - COSPAR 2021 (FDL USA 2019)
Automating the Calibration of the Atmospheric Imaging Assembly - COSPAR 2021 (FDL USA 2019)
Forecasting Ground Magnetic Perturbation Using Deep Learning on Spherical Harmonics- American Meteorological Society 101th annual meeting 2021 (FDL USA 2020)
2020
Multi-Channel Auto-Calibration for the Atmospheric Imaging Assembly using Machine Learning- Paper at AGU 2020 (FDL USA 2019)
Determining new representations of “Geoeffectiveness” using deep learning - AGU 2020 (FDL USA 2020)
Multi-Channel Auto-Calibration for the Atmospheric Imaging Assembly instrument with Deep Learning- AGU 2020 (FDL USA 2019)
RotNet: Fast and Scalable Estimation of StellarRotation Periods Using Convolutional NeuralNetworks- NeurIPS 2020 ML4PS (Physical Sciences) Workshop (FDL USA 2020)
Global Earth Magnetic Field Modeling and Forecasting with Spherical Harmonics Decomposition- NeurIPS 2020 ML4PS (Physical Sciences) Workshop (FDL USA 2020)
2019
Using U-Nets to create high-fidelity virtual observations of the solar corona - NeurIPS Workshop 2019 (FDL USA 2019)
Arxiv 1911.04006v1A deep-learning based approach for predicting high latitude ionospheric scintillations using geospace data and auroral imagery - JPL NASA Abstract (FDL USA 2019)
Auto-Calibration of Remote Sensing Solar Telescopes with Deep Learning- NeurIPS Workshop 2019 (FDL USA 2019)
Arxiv 1911.04008v1Single-Frame Super-Resolution of Solar Magnetograms: Investigating Physics-Based Metrics & Losses- NeurIPS Workshop 2019 (FDL USA 2019)
Arxiv 1911.01490v1Correlation of Auroral Dynamics and GNSS Scintillation with an Autoencoder- NeurIPS Workshop 2019 (FDL USA 2019)
Arxiv 1910.03085v1Prediction of GNSS Phase Scintillations: A Machine Learning Approach - NeurIPS Workshop 2019 (FDL USA 2019)
Arxiv 1910.01570v1A deep learning Approach to forecast Tomorrow's Solar Wind Parameters- AGU 2019 (FDL USA 2019)
Enhancing the Predictability of GNSS Scintillations - AGU 2019 (FDL USA 2019) [I]
Auto-calibration and reconstruction of SDO’s Atmospheric Imaging Assembly channels with Deep Learning- AGU 2019 (FDL USA 2019)
A deep learning virtual instrument for monitoring extreme UV solar spectral irradiance - Science Advances 2019 (FDL USA 2018)
DOI 10.1126/sciadv.aaw6548A machine learning dataset prepared for NASA’s Solar Dynamics Observatory- Astrophysical Journal 2019 (FDL USA 2018)
DOI 10.3847/1538-4365/ab1005Arxiv 1903.04538v1
-
2021
Best practices in sharing enhanced data products and machine learning algorithms -learnings from NASA Frontier Development Lab - JPL Data Science & AI Workshop 2021
Space ML: Distributed Open-source Research with Citizen Scientists for the Advancement of Space Technology for NASA - COSPAR 2021 Workshop on Cloud Computing for Space Sciences
Space ML: Distributed Open-Source Research with Citizen-Scientists for Advancing Space Technology for NASA- Nvidia GTC21- Accepted
2020
Learnings from Frontier Development Lab and SpaceML- AI Accelerators for NASA and ESA
Technology Readiness Levels for Machine Learning Systems
2018
NASA's Asteroid Grand Challenge: Strategy, Results and Lessons Learned - Science Direct Journal, 2018
NASA's Asteroid Grand Challenge: Strategy, Results and Lessons Learned- Space Policy Journal 2018 (open access link)
Advancing Astrobiology Through Public/Private Partnerships -The FDL Model - 49th Lunar and Planetary Science Conference 2018
-
CAMS
Recovery of meteorites using an autonomous drone and machine learning - Meteoritics & Planetary Science 2021 (FDL USA 2016) - ArxivMeteorite recovery using a drone and machine learning - Lunar and Planetary Science 2017 (FDL USA 2017)
The Deflector Selector: A Machine Learning Framework for Prioritizing Hazardous Object Deflection Technology Development - Acta Astronautica 2018 - Arxiv (FDL USA 2017)
Artificial Intelligence Techniques applied to Automating Meteor Validation and Trajectory Quality Control to Direct the Search for Long Period Comets - International Meteor Conference 2017 (FDL USA 2017)
A survey of southern hemisphere meteor showers - Planetary and Space Science Journal 2018 (FDL USA 2017)
Using Bayesian Optimization to Find Asteroids' Pole Directions - American Astronomical Society, DPS meeting #50 2018 (FDL USA 2017)
Machine learning tools to develop 3D shape models of near Earth asteroids from radar observations - EPSC-DPS Joint Meeting 2019 (FDL USA 2017) (BIB)
-
2024
Absolute Localization Through Orbital Maps and Surface Perspective Imagery - 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (FDL USA 2018)
Unsupervised learning for thermal anomaly detection on the lunar surface - NeurIPS Workshop 2019 (FDL USA 2019)
Absolute Localisation for surface robotics in GPS denied locations using a Neural Network - IEEE Xplore 2019 (FDL USA 2019)
Unsupervised learning for thermal anomaly detection on the lunar surface- 51st Lunar and Planetary Science Conference - 2020 (FDL USA 2019)
Unsupervised learning for thermal anomaly detection on the lunar surface - The Planetary Science Journal 2020 (FDL USA 2019)
Unsupervised Distribution Learning for Lunar Anomaly Detection- NeurIPS 2019 (FDL USA 2019)
Absolute Localisation for surface robotics in GPS denied locations using a Neural Network- JPL AI and Data Science 2021 Workshop (FDL USA 2018)
Low-Light Image Enhancement of Permanently Shadowed Lunar Regions with Physics-Based Machine Learning - University of Luxembourg Library / CVPR conference - NVIDIA GTC21 - Accepted (FDL USA 2020)
Low-light image enhancement of permanently shadowed lunar regions with physics-based machine learning - University of Luxembourg Library (FDL USA 2020)
A Big Data and AI-Driven Approach for Anomaly Detection on the Lunar Surface - Lunar and Planetary Institute (FDL USA 2020)
Peering into lunar permanently shadowed regions with deep learning- Nature Communications (FDL USA 2020)
-
Item descriptionINARA: Intelligent exoplaNet Atmospheric RetrievAl A Machine Learning Retrieval Framework with a Data Set of 3 Million Simulated Exoplanet Atmospheric Spectra - Astrobiology Science Conference 2019 (FDL USA 2018)
Using machine learning to study E.T. biospheres - NeurIPS Workshop 2019 (FDL USA 2018)
EXO-ATMOS: A Scalable Grid of Hypothetical Planetary Atmospheres - Astrobiology Science Conference 2019 (FDL USA 2018)
INARA: A Bayesian Deep Learning Framework for Exoplanet Atmospheric Retrieval - JPL AI and Data Science Workshop 2021 (FDL USA 2018)
Advancing Space Science with Machine Learning: Frontier Development Lab Projects with NASA-Nvidia GTC21- Accepted (FDL USA 2018, FDL USA 2019, FDL USA 2020)
-
Scientific Domain Knowledge Improves Exoplanet Transit Classification with Deep Learning - Astrophysical Journal 2018 - Arvix (FDL USA 2018)
Bayesian Deep Learning for Exoplanet Atmospheric Retrieval - NeurIPS Workshop 2018 (FDL USA 2018)
An Ensemble of Bayesian Neural Networks for Exoplanetary Atmospheric Retrieval - Astrophysical Journal 2019 -Arxiv (FDL USA 2018)
The NASA FDL Exoplanet Challenge: Transit Classification with Convolutional Neural Networks - Astrobiology Science Conference 2019 (FDL USA 2018)
Rapid Classification of TESS Planet Candidates with Convolutional Neural Networks - Astronomy & Astrophysics Journal 2020-Arvix (FDL USA 2018)
-
Prototyping CRISP: A Causal Relation and Inference Search Platform applied to Colorectal Cancer Data - IEEE LifeTech 2021 / NASA HRP IWS 2021 (FDL USA 2020) - research paper award winners for IEEE Lifetech 2021
Generative Models for Synthesizing Symptomatic ECG Astronaut Health Data for Future Deep Space Missions - JPL Data Science & AI Workshop 2021 (FDL USA 2020)
A Generative Machine Learning Framework for Synthesizing Symptomatic ECG Astronaut Health Data - NASA HRP Workshop 2021 (FDL USA 2020)
Advancing Space Science with Machine Learning: Frontier Development Lab Projects with NASA - Nvidia GTC21- Accepted (FDL USA 2019, FDL USA 2019, FDL USA 2020)
Learning Invariant Representations for non-i.i.dFederated Settings - NeurIPS 2021 (FDL USA 2021)
Federated causal inference for out-of-distribution generalization in predicting physiological effects of radiation exposure - AGU 2021 (FDL USA 2021)
Invariant Risk Minimisation for Cross-Organism Inference: Substituting Mouse Data for Human Data in Human Risk Factor Discovery - ArXiv (FDL USA 2021)
-
SAR-based landslide classification pretraining leads to better segmentation - NeurIPS 2022 (FDL 2022) - ArXiv
Deep Learning for Rapid Landslide Detection using Synthetic Aperture Radar (SAR) Datacubes - NeurIPS 2022 (FDL 2022) - ArXiv
Deep learning based landslide density estimation on SAR data for rapid response (FDL 2022) - ArXiv
Short-term Prediction of Severe Thunderstorm Hazards with Machine Learning and the Geostationary Lightning Mapper - American Meteorological Society Conference 2020 (FDL USA 2020)
Machine Learning for Generalizable Prediction of Flood Susceptibility - NeurIPS Workshop 2019 / DeepAI (FDL USA 2019)
Leveraging Lightning with Convolutional Recurrent AutoEncoder and ROCKET for Severe Weather Detection - NeurIPS 2020 - AI for Earth Science and AI for Earth Science Workshop (FDL USA 2020)
Dynamic Hydrology Maps from Satellite-LiDAR Fusion - NeurIPS 2020 - AI for Earth Science Workshop - Video (FDL USA 2020)
Knowledge Discovery Framework: Deep Learning Applications for Remote Sensing - AGU 2020 (FDL USA 2020)
Severe Weather Prediction Using Lightning Data - COSPAR 2021 (FDL USA 2020)
Severe Weather Prediction Using Lightning Data - NeurIPS Workshop 2020 - LatinX in AI (FDL USA 2020)
Severe Weather Prediction Using Lightning Data - NVIDIA GTC21 - Accepted (FDL USA 2020)
Where Are the Earth's Streams Flowing Right Now? Dynamic Hydrology Maps from Satellite-Lidar Fusion - NVIDIA GTC21 - Submitted (FDL USA 2020)
Water monitoring with Very High Resolution satellite imagery - EGU'21 - Submitted (FDL USA 2020)
Physics-informed GANs for Coastal Flood Visualization - Under Review (FDL USA 2020)
Physically-Consistent Generative Adversarial Networks for Coastal Flood Visualization - IEEE Transactions on Neural Networks and Learning Systems - in review (FDL USA 2020)
Detecting Spatiotemporal Lightning Patterns: An Unsupervised Graph-Based Approach - ML4PS - (FDL USA 2021) - Submitted
Artificial Intelligence for the Advancement of Lunar and Planetary Science and Exploration - Planetary Science and Astrobiology Decadal Survey 2023-2032 (FDL USA 2021)
Coastal Digital Twin: Learning a fast and physics-informed surrogate model for coastal floods via neural operators - AGU 2021 - (FDL USA 2021) - Submitted
Generating informative and accurate descriptions of natural hazards and phenomena using large transformer-based models - AGU 2021 (FDL USA 2021) - Submitted
-
Multi-spectral multi-image super-resolution of Sentinel-2 with radiometric consistency losses and its effect on building delineation - ISPRS Journal of Photogrammetry and Remote Sensing (2022)
Multi3Net: Segmenting Flooded Buildings via Fusion of Multiresolution, Multisensor, and Multitemporal Satellite Imagery - NeurIPS & AAAI (FDL Europe 2018)
Rapid Computer Vision-Aided Disaster Response via Fusion of Multiresolution, Multisensor, and Multitemporal Satellite Imagery - NeurIPS (FDL Europe 2018)
UNICEF-ESA-FDL - AI to automate disaster impact assessment - Concept Note - UNICEF (FDL Europe 2018)
Mapping Informal Settlements in Developing Countries with Multi-resolution, Multi-spectral Data - NeurIPS & AAAI (FDL Europe 2018)
Mapping Informal Settlements in Developing Countries using Machine Learning and Low Resolution Multi-spectral Data - AAAI/ACM Conference (FDL Europe 2018)
Generating Material Maps to Map Informal Settlements - NeurIPS & AAAI arXiv (FDL Europe 2018)
Mapping Informal Settlements in Developing Countries with Multi-resolution, Multi-spectral Data - NeurIPS & AAAI arXiv - (FDL Europe 2018)
Mapping Informal Settlements in Developing Countries using Machine Learning and Low Resolution Multi-spectral Data - AIES 19 (FDL Europe 2018)
Cumulo: A Dataset for Learning Cloud Classes - JPL NASA Abstract (FDL Europe 2019)
The Link of Tropical Convection and Low Cloud Cover - NeurIPS (FDL Europe 2019)
Classifying without Supervision but with Distributional Constraints - NeurIPS - (FDL Europe 2019)
Cumulo: A Dataset for Learning Cloud Classes - NeurIPS (FDL Europe 2019) Received best paper award review here
Flood Detection on low cost orbital Software - NeurIPS (FDL Europe 2019)
FDL: Mission Support Challenge - NeurIPS - (FDL Europe 2019)
Learning from History: Scoring & Automating Spacecraft Constellation Schedules - SpaceOps (FDL Europe 2019)
AI for the Developing World, Self-supervised Learning Theory & Practice - NeurIPS 2021 (FDL Europe 2021)- Submitted
-
Spacecraft Collision Risk Assessment with Probabilistic Programming - Neurips 2020 - AI for Earth Science (FDL Europe - 2020)
Towards Automated Satellite Conjunction Management with Bayesian Deep Learning - Neurips 2020 - ML4PhysicalSciences (FDL Europe 2020)
RainBench: Towards Global Precipitation Forecasting from Satellite Imagery - AAAI 2020, top-5 conference in CS (FDL Europe 2020)
Towards Data-Driven Physics-Informed Global Precipitation Forecasting from Satellite Imagery - Neurips 2020 - Tackling Climate Change with Machine Learning (FDL Europe 2020)
RainBench: Enabling Data-Driven Precipitation Forecasting on a Global Scale - Neurips 2020 - Tackling Climate Change with Machine Learning (FDL Europe 2020)
Tracking Marine Boundary Layer Cloud Transitions Using Machine Learning - AGU 2020 (FDL Europe 2020)
-
Smart satellites: machine learning on-board for low-latency novelty detection - AGU 2021 (FDL Europe 2021)
Unsupervised change detection of extreme events using ML on-board - HADR.ai NeurIPS (FDL Europe 2021)
