PUBLICATIONS

2024

Machine learning discovery of cost-efficient dry cooler designs for concentrated solar power plants - paper, Nature Journal
DOI 10.1038/s41598-024-67346-6

SuNeRF: 3D Reconstruction of the Solar EUV Corona Using Neural Radiance Fields - Astrophysical Journal Letters (FDL USA 2022)
DOI 10.3847/2041-8213/ad12d2
Arxiv 2401.16388

Improving Thermospheric Density Predictions in Low-Earth Orbit With Machine Learning- AGU Space Weather Volume 22, Issue 2
DOI 10.1029/2023SW003652

Physically Motivated Deep Learning to Superresolve and Cross Calibrate Solar Magnetograms - The Astrophysical Journal
DOI 10.3847/1538-4365/ad12c2

Virtual EVE: a Deep Learning Model for Solar Irradiance Prediction - Paper
Arxiv 2408.17430v1

Multiscale Geoeffectiveness Forecasting using SHEATH and DAGGER - Poster, Astronomical Society of India 2024

2023

A Scientific Cloud Computing Platform for Ingestion and Processing of SDO Data - 4th Eddy Cross-Disciplinary Symposium (FDL-X 2023)

AIA 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 10.1051/0004-6361/202040051

Arxiv 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 1911.04006v1

A 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 1911.04008v1

Single-Frame Super-Resolution of Solar Magnetograms: Investigating Physics-Based Metrics & Losses- NeurIPS Workshop 2019 (FDL USA 2019)
Arxiv 1911.01490v1

Correlation of Auroral Dynamics and GNSS Scintillation with an Autoencoder- NeurIPS Workshop 2019 (FDL USA 2019)
Arxiv 1910.03085v1

Prediction of GNSS Phase Scintillations: A Machine Learning Approach - NeurIPS Workshop 2019 (FDL USA 2019)
Arxiv 1910.01570v1

A 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.aaw6548

A machine learning dataset prepared for NASA’s Solar Dynamics Observatory- Astrophysical Journal 2019 (FDL USA 2018)
DOI 10.3847/1538-4365/ab1005

Arxiv 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

Beyond reproducibility at the Frontier Development Lab (FDL): Community driven continuous optimization for the SDO machine learning dataset (SDOML).- AGU 2021

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) - Arxiv

Meteorite 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

Automated Discovery of Anomalous Features in Ultralarge Planetary Remote-Sensing Datasets Using Variational Autoencoders - IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Volume 17

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)