The AQcGAN Air Quality Emulator for GEOS
AQcGAN is an air quality emulator for surface O3 and NOx concentrations.
The AIMFAHR Project
Artificial Intelligence (AI) techniques, particularly Machine Learning (ML), have undergone significant growth in heliophysics research in recent years. Various ML models have eme…
Qualitative Evaluation of Foundation Models (QEFM)
Objective: Quantify the performance of Foundation Models (FMs) for weather and climate to guide GSFC scientists in effectively integrating AI into their research.
System for Uncertainty, Risk, and Feature Assessment of Surfaces (SURFAS) Holistic Scene Analysis
SURFAS, given the above combination of inputs for a particular vehicle, is informed by expertise of mission designers to fuse relevant data together in order to create a holistic…
Explainable Machine Learning Methods for Ocean Worlds Mass Spectrometry Data: Biosignatures and Environmental Characterization
Future astrobiological and geochemical investigations of ocean worlds (OWs) such as Europa and Enceladus will face challenges that can be addressed through science autonomy. While…
Transiting Exoplanet Survey Satellite (TESS) Neural Network (NN)
The Transiting Exoplanet Survey Satellite (TESS) is a NASA mission focused on exploring and finding exoplanets around nearby stars using the transiting method. TESS telescope cove…
RAG Chatbots: Enhancing User and Internal Support Through Dual Knowledge Systems
This work presents two complementary RAG-based chatbot systems developed for NASA's Community Coordinated Modeling Center. These tools represent practical applications of retriev…
Machine Learning Techniques for Fast Radiative Transfer
Radiative transfer models for satellite data assimilation and physical atmospheric retrievals need to be both fast and accurate to fulfill operational constraints. These contradic…
Using ChatGSFC to Streamline Analysis and Reporting
A series of 90+ examples of how ChatGSFC or a NASA-focused LLM can be utilized to enhance project planning and controls (PP&C) analysis and streamline project management activitie…
Ground-based Detection of Martian Dust Devils With a Fine-tuned Fast R-CNN
We developed a two-stage pipeline for efficient dust devil detection in Mars rover imagery. Our approach combines preprocessing filters to remove unsuitable images, followed by a…
A Detection and Reporting System for Spacecraft Threats
Goal is to identify, and potentially predict when an event (e.g., anomaly, interference, etc.) has/will occur. Spacecraft anomalies / interference events can take them out of of m…
Neural Posterior Estimation for X-ray reflection spectroscopy: Training on complex physical models and AGN Observation-Driven Parameter Grids.
The development of an automated inference tool tailored to extract key physical parameters from obscured AGN (active galactic nuclei) X-ray spectra by means of more complex physic…
Revolutionizing Neutron Star Parameter Inference through Machine Learning
Observations of neutron stars provide estimates of their mass and radius—key parameters for constraining their still uncertain equation of state. However, the accuracy of paramete…
Curiosities of a Systems Engineer
AI has helped enable me as a systems engineer to dive deep into subjects outside my expertise. Helping bridging the gap between specialists and generalists. This includes helping…
Continuing Adventures in the Discovery of Multiple Star Systems with AI/ML
Early data-driven analyses of ozone chemistry sensitivity primarily relied on "ratio-based" indicators to partially linearize the non-linear aspects of urban ozone chemistry, whic…
Autonomous Science and Technology for Responsive Adaptation (ASTRA)
The ASTRA team is working to develop and mature capabilities for extensibility and science autonomy. Extensibility would give us the ability to add and collaborate among multi-org…
Multimodal Earth Observation Workflow for Machine Learning (MEOW-ML): A Case Study in Canopy Height Model and Canopy Height Change Prediction
Here we introduce an updated version of Multimodal Earth Observation Workflow for Machine Learning (MEOW-ML), an end-to-end data fusion and artificial intelligence (AI) and machin…
Sub-Saharan West Africa Land Cover Change
In recent decades, Sub-Saharan West Africa has seen rapid and ongoing land cover change fueled by population growth and subsequent agricultural expansion and intensification. Thes…
Near-real-time aerosol retrievals from OMPS Limb Profiler measurements
Rapid aerosol retrievals from OMPS Limb Profiler are important to monitor large wildfires and volcanic eruptions that reach the stratosphere. These near-real-time (NRT) retrieval…
Retrieving stratospheric water vapor from OMPS Limb Profiler measurements
Stratospheric water vapor (SWV) plays an important role in atmospheric chemistry, dynamics, and radiative forcing. OMPS Limb Profiler (LP) provides daily near-global coverage and…
Retrieving stratospheric NO2 profiles from OMPS Limb Profiler measurements
Stratospheric NO2 plays an important role in ozone photochemistry. The OMPS Limb Profiler (LP) instrument provides daily near-global coverage; it is currently operating on two sa…
Automated identification of volcanic plumes
Use ML to reduce noise in image of satellite SO2 retrievals, and automatically identify volcanic SO2 plumes in the images.
Correcting NASA NPOL weather radar beam blockage using machine learning approaches
Our objective is to fill in blocked radar data using Convolutional Neural Networks (CNN). This project is in the early stages in preparing the data and model for training.
Application of ML to Detection of Anomalies in Spacecraft Health and Status Data
We are collaborating with the Magnetospheric Multiscale (MMS) mission to research Machine Learning (ML) techniques capable of predicting and detecting anomalies in spacecraft heal…