Identify Best-fit and Optimize AI/ML models to support the Digital Twins related to Air Quality

NSF IUCRC Center for Spatiotemporal Thinking, Computing, and Applications (STC), Project: The proposed work continues done last year in analyzing and comparing various Machine Learning techniques for air quality and GHG measurements analysis. In this continuation project, the following tasks will be completed: (1) Continue the systematic study of AI/ML algorithms for air quality analysis; (2) Create an open-source AI/ML toolkit for Air Quality and integrate it in the AIST AQACF and FireAlarm projects; and (3) Curate a comprehensive training pollutant dataset with corresponding uncertainties and make it available open-source. Overall this project will also promote STEM education, working with High-School and Undergraduate students; it will also contribute to the AIST ESDT focus area by providing ML tool that have been well evaluated and validated on openly available training datasets.

Advance Earth system science knowledge through the identification, development, and demonstration of innovative information systems technologies

This is early/mid-stage (TRL 4) — the most realistic path in is NASA SBIR/STTR, which funds small businesses and research institutions to develop technology aligned with NASA's needs (equity-free, phased funding). Check whether a current SBIR/STTR solicitation topic overlaps with this project's technology area, or contact the project directly (above) to ask.

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