Jason A. Tullis
Affiliation confirmed via AI analysis of OpenAlex, ORCID, and web sources.
Professor / Department Chair
Also affiliated: University of South Carolina (2003–2005)
Faculty Researcher
Research Areas
Biomedical Subjects
Links
Biography and Research Information
OverviewAI-generated summary
Jason A. Tullis studies the application of machine learning and advanced computational techniques to agricultural and environmental challenges. His work investigates the use of predictive learning models to understand environmental drivers of crop yield, specifically focusing on maize and soybeans in the United States. Tullis also examines spatiotemporal characteristics of crop phenology using remotely sensed data, contributing to improved agricultural monitoring and management. His research extends to geospatial data analysis and cyberinfrastructure for big data in the geosciences, with publications addressing challenges in data reproducibility and analysis, including in the context of COVID-19.
With a h-index of 13 and over 1,240 citations across 43 publications, Tullis has established collaborations with researchers at the University of Arkansas at Fayetteville, including Jackson Cothren, Malcolm Williamson, Lawton Lanier Nalley, and Harrison Smith. His recent work also includes exploring frameworks for feature mapping in planetary science.
Metrics
- h-index: 13
- Publications: 42
- Citations: 1,241
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)
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Geoweaver: Connecting Dots for Artificial Intelligence in Geoscience (2020)AGU Fall Meeting Abstracts OpenAlex
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Geoweaver: Advanced Cyberinfrastructure for Managing Hybrid Geoscientific AI Workflows (2020)
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Geographic information system (GIS)-based image analysis for assessing growth of Physarum polycephalum on a solid medium (2015)
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Deciduous Forest Structure Estimated with LIDAR-Optimized Spectral Remote Sensing (2013)
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A geological characterization of Ligeia Mare in the northern polar region of Titan (2013)
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A remote sensing and GIS-assisted landscape epidemiology approach to West Nile virus (2013)
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Spatial Scale Management Experiments Using Optical Aerial Imagery and LIDAR Data Synergy (2010)
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Per-segment Aboveground Forest Biomass Estimation Using LIDAR-Derived Height Percentile Statistics (2009)
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Modeling red oak borer, Enaphalodes rufulus (Haldeman), damage using in situ and ancillary landscape data (2007)
Collaboration Network
Top Collaborators
- 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
- 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
- Framework for Mapping Sublimation Features on Mars’ South Polar Cap Using Object-Based Image Analysis
- Framework for Mapping Sublimation Features on Mars’ South Polar Cap Using Object-Based Image Analysis
- Spatiotemporal Characterization of Soybean Phenology in the Arkansas Delta Region Using Multi-Source Remotely Sensed Data from 2002 to 2020
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