Jason A. Tullis Data-verified
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Professor / Department Chair
faculty
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Biography and Research Information
OverviewAI-generated summary
Jason A. Tullis is a Professor and Department Chair at the University of Arkansas at Fayetteville. His research focuses on the application of machine learning and advanced data analysis techniques to geospatial data, with a particular emphasis on environmental and agricultural applications. He has investigated the challenges and limitations of geospatial data in the context of public health issues, such as COVID-19, and has explored cyberinfrastructure needs for machine learning in the geosciences.
Tullis's work also extends to remote sensing for agricultural insights, including characterizing crop phenology and yield using multi-source data. His research has involved developing frameworks for mapping features on planetary bodies, such as Mars, using object-based image analysis. He has published 43 papers, accumulating 1,231 citations and an h-index of 13. Key collaborators include Jackson Cothren, Malcolm Williamson, Lawton Lanier Nalley, and Harrison Smith.
Metrics
- h-index: 13
- Publications: 43
- Citations: 1,234
Selected Publications
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Harvesting insights: interpretable machine learning to understand environmental drivers of U.S. maize and soybean yield (2026)
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Spatiotemporal Characterization of Soybean Phenology in the Arkansas Delta Region Using Multi-Source Remotely Sensed Data from 2002 to 2020 (2025)
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Framework for Mapping Sublimation Features on Mars’ South Polar Cap Using Object-Based Image Analysis (2025)
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A review of cyberinfrastructure for machine learning and big data in the geosciences (2022)
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Context for Reproducibility and Replicability in Geospatial Unmanned Aircraft Systems (2022)
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Challenges and Limitations of Geospatial Data and Analyses in the Context of COVID-19 (2021)
Collaboration Network
Top Collaborators
- Challenges and Limitations of Geospatial Data and Analyses in the Context of COVID-19
- Geoprocessing, Workflows, and Provenance
- Challenges and Limitations of Geospatial Data and Analyses in the Context of COVID-19
- Geoprocessing, Workflows, and Provenance
- Challenges and Limitations of Geospatial Data and Analyses in the Context of COVID-19
- Challenges and Limitations of Geospatial Data and Analyses in the Context of COVID-19
- Challenges and Limitations of Geospatial Data and Analyses in the Context of COVID-19
- Challenges and Limitations of Geospatial Data and Analyses in the Context of COVID-19
- Context for Reproducibility and Replicability in Geospatial Unmanned Aircraft Systems
- A review of cyberinfrastructure for machine learning and big data in the geosciences
- A review of cyberinfrastructure for machine learning and big data in the geosciences
- A review of cyberinfrastructure for machine learning and big data in the geosciences
- A review of cyberinfrastructure for machine learning and big data in the geosciences
- A review of cyberinfrastructure for machine learning and big data in the geosciences
- Geoprocessing, Workflows, and Provenance
- Geoprocessing, Workflows, and Provenance
- Geoprocessing, Workflows, and Provenance
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