PUBLICATIONS
-
2025
Image calibration between the Extreme Ultraviolet Imagers on Solar Orbiter and the Solar Dynamics Observatory, Astronomy & Astrophysics DOI: 10.1051/0004-6361/202556401
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