Description
In align with our research topic on large volume data analytics, one major application domain for the big data analytics research is Earth’s climate because the large volume of available climate simulation data and Earth observation data. Research challenges on this topic include: 1) How to apply and improve existing data science approaches for climate data; 2) How to integrate domain knowledge in Earth and atmospheric science with data science techniques to solve related scientific problems.
We have worked on novel data mining approaches for climate related problems such as dust detection, cloud type classification and storm prediction, combine physics knowledge and data science in climate applications, hybrid causality discovery techniques leveraging climate simulation and data science. Our work in this research topic results in peer-reviewed publications at major journals (such as interdisciplinary climate studies section of Frontiers in Earth Science) and conferences (SMARTComp, eScience, IEEE Big Data, etc.).
Projects
- 2021 – 2025: NSF Harnessing Data Revolution (HDR) Institute: Harnessing Data and Model Revolution in the Polar Regions (iHARP), National Science Foundation (NSF)
- 2021 – 2023:
REU Site: Online Interdisciplinary Big Data Analytics in Science and Engineering, National Science Foundation (NSF) - 2020 – 2025: CAREER: Big Data Climate Causality Analytics, National Science Foundation (NSF)
- 2018 – 2021: Efficient and Flexible Aggregation and Distribution of MODIS Atmospheric Products based on Climate Analytics-as-a-Service Framework, National Aeronautics and Space Administration (NASA), PI
- 2017 – 2021: CrossTraining of Researchers in Computing, Applied Mathematics and Atmospheric Sciences using Advanced Cyberinfrastructure Resources, National Science Foundation (NSF)