Chase Rainwater
Affiliation confirmed via AI analysis of OpenAlex, ORCID, and web sources.
Professor / Department Chairperson
Also affiliated: University of Florida (2006–2009); Fayetteville Public Library (2025)
Faculty Researcher
Research Areas
Biomedical Subjects
Links
Biography and Research Information
OverviewAI-generated summary
Chase Rainwater's research program focuses on the application of machine learning and artificial intelligence to address complex problems, particularly in the domains of computer vision and agriculture. His work has explored the development of advanced neural network architectures for tasks such as image segmentation, domain adaptation, and object detection, with a significant portion of his recent publications detailing advancements in aerial image analysis and video captioning. Rainwater has also been involved in projects aimed at improving regional food systems through data-driven approaches. He is a Co-Principal Investigator on two NSF Convergence Accelerator grants totaling over $5.7 million, focusing on cultivating IQ for regional food systems and leveraging data for agriculture to connect small farms with regional supply chains. These grants highlight a commitment to applying technological solutions to agricultural challenges and strengthening food system infrastructure.
His research group at the University of Arkansas at Fayetteville includes collaborators such as Taisei Hanyu, Jackson Cothren, Khoa Luu, and Thanh-Dat Truong, with whom he has co-authored multiple publications. Rainwater's scholarly output is reflected in his h-index of 15 and over 900 citations across nearly 60 publications. His recent activity indicates a continued engagement with research and development in his areas of expertise, including contributions to areas like robotics and sensor-based localization through projects like AerialFormer.
Metrics
- h-index: 15
- Publications: 58
- Citations: 971
Selected Publications
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Rethinking Progression of Memory State in Robotic Manipulation: An Object-Centric Perspective (2026)
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Land8Fire: A Complete Study on Wildfire Segmentation Through Comprehensive Review, Human-Annotated Multispectral Dataset, and Extensive Benchmarking (2025)
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RSSep: Sequence-to-Sequence Model for Simultaneous Referring Remote Sensing Segmentation and Detection (2025)
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A Bi-Modular Auto Encoder-Based Unsupervised Degradation Detection Methodology for Remaining Useful Life Prediction (2024)
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AerialFormer: Multi-Resolution Transformer for Aerial Image Segmentation (2024)
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A Bi-Modular Auto Encoder-Based Unsupervised Degradation Detection Methodology for Remaining Useful Life Prediction (2024)
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VLCAP: Vision-Language with Contrastive Learning for Coherent Video Paragraph Captioning (2022)
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EQAdap: Equipollent Domain Adaptation Approach to Image Deblurring (2022)
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Direct Aerial Visual Geolocalization Using Deep Neural Networks (2021)
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BiMaL: Bijective Maximum Likelihood Approach to Domain Adaptation in Semantic Scene Segmentation (2021)
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A quantitative risk assessment model of Salmonella contamination for the yellow-feathered broiler chicken supply chain in China (2020)
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A computational comparison of cargo prioritization and terminal allocation problem models (2020)
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Horizontal collaboration: opportunities for improved logistics planning (2019)
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Modeling the Reduction of Salmonella spp. on Chicken Breasts and Wingettes during Scalding for QMRA of the Poultry Supply Chain in China (2019)
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A Markov Decision Process approach for balancing intelligence and interdiction operations in city-level drug trafficking enforcement (2019)
Federal Grants 2 $5,742,469 total
NSF Convergence Accelerator Track J Phase 2: Cultivate IQ - Empowering Regional Food Systems
Collaboration Network
Top Collaborators
- AerialFormer: Multi-Resolution Transformer for Aerial Image Segmentation
- BiMaL: Bijective Maximum Likelihood Approach to Domain Adaptation in Semantic Scene Segmentation
- VLCAP: Vision-Language with Contrastive Learning for Coherent Video Paragraph Captioning
- RSSep: Sequence-to-Sequence Model for Simultaneous Referring Remote Sensing Segmentation and Detection
- Land8Fire: A Complete Study on Wildfire Segmentation Through Comprehensive Review, Human-Annotated Multispectral Dataset, and Extensive Benchmarking
- AerialFormer: Multi-Resolution Transformer for Aerial Image Segmentation
- Direct Aerial Visual Geolocalization Using Deep Neural Networks
- RSSep: Sequence-to-Sequence Model for Simultaneous Referring Remote Sensing Segmentation and Detection
- Land8Fire: A Complete Study on Wildfire Segmentation Through Comprehensive Review, Human-Annotated Multispectral Dataset, and Extensive Benchmarking
- BiMaL: Bijective Maximum Likelihood Approach to Domain Adaptation in Semantic Scene Segmentation
- VLCAP: Vision-Language with Contrastive Learning for Coherent Video Paragraph Captioning
- EQAdap: Equipollent Domain Adaptation Approach to Image Deblurring
- BiMaL: Bijective Maximum Likelihood Approach to Domain Adaptation in Semantic Scene Segmentation
- EQAdap: Equipollent Domain Adaptation Approach to Image Deblurring
- AerialFormer: Multi-Resolution Transformer for Aerial Image Segmentation
- VLCAP: Vision-Language with Contrastive Learning for Coherent Video Paragraph Captioning
- A Bi-Modular Auto Encoder-Based Unsupervised Degradation Detection Methodology for Remaining Useful Life Prediction
- A Bi-Modular Auto Encoder-Based Unsupervised Degradation Detection Methodology for Remaining Useful Life Prediction
- AerialFormer: Multi-Resolution Transformer for Aerial Image Segmentation
- Land8Fire: A Complete Study on Wildfire Segmentation Through Comprehensive Review, Human-Annotated Multispectral Dataset, and Extensive Benchmarking
- AerialFormer: Multi-Resolution Transformer for Aerial Image Segmentation
- RSSep: Sequence-to-Sequence Model for Simultaneous Referring Remote Sensing Segmentation and Detection
- BiMaL: Bijective Maximum Likelihood Approach to Domain Adaptation in Semantic Scene Segmentation
- BiMaL: Bijective Maximum Likelihood Approach to Domain Adaptation in Semantic Scene Segmentation
- Direct Aerial Visual Geolocalization Using Deep Neural Networks
- EQAdap: Equipollent Domain Adaptation Approach to Image Deblurring
- EQAdap: Equipollent Domain Adaptation Approach to Image Deblurring
- EQAdap: Equipollent Domain Adaptation Approach to Image Deblurring
- VLCAP: Vision-Language with Contrastive Learning for Coherent Video Paragraph Captioning
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