Barira Rashid
Affiliation confirmed via AI analysis of OpenAlex, ORCID, and web sources.
Researcher
Also affiliated: Kyma Technologies (United States) (2025); National University of Sciences and Technology (2018–2020)
Unknown Researcher
Research Areas
Biomedical Subjects
Links
Biography and Research Information
OverviewAI-generated summary
Barira Rashid's research focuses on the intersection of environmental science, agriculture, and machine learning. Her recent publications investigate methods for mapping livestock infrastructure and identifying animal feeding operations using machine learning and object-based image analysis. These efforts aim to address gaps in U.S. livestock data, which pose barriers to effective environmental and disease management.
Rashid also explores the broader implications of artificial intelligence in sustainability, particularly within circular bioeconomies. Her work extends to understanding the impacts of climate change and urbanization on wildlife by studying the connectivity of thermal refuges in desert environments. She has collaborated with researchers at the University of Arkansas at Fayetteville, including Rebecca Logsdon Muenich and Arghajeet Saha, on multiple publications.
Metrics
- h-index: 3
- Publications: 5
- Citations: 56
Selected Publications
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Beyond linearity: reimagining AI as a participant in circular bioeconomies (2026)
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Structural and Functional Connectivity of Thermal Refuges in a Desert City: Impacts of Climate Change and Urbanization on Desert Wildlife (2025)
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Gaps in U.S. livestock data are a barrier to effective environmental and disease management (2025)
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Machine learning-based identification of animal feeding operations in the United States on a parcel-scale (2025)
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Navigating Earth Sciences: Addressing Equity and Sustainability Through Interdisciplinary Endeavors (2024)
Collaboration Network
Top Collaborators
- Gaps in U.S. livestock data are a barrier to effective environmental and disease management
- Machine learning-based identification of animal feeding operations in the United States on a parcel-scale
- Gaps in U.S. livestock data are a barrier to effective environmental and disease management
- Machine learning-based identification of animal feeding operations in the United States on a parcel-scale
- Machine learning-based identification of animal feeding operations in the United States on a parcel-scale
- Machine learning-based identification of animal feeding operations in the United States on a parcel-scale
- Gaps in U.S. livestock data are a barrier to effective environmental and disease management
- Gaps in U.S. livestock data are a barrier to effective environmental and disease management
- Gaps in U.S. livestock data are a barrier to effective environmental and disease management
- Gaps in U.S. livestock data are a barrier to effective environmental and disease management
- Gaps in U.S. livestock data are a barrier to effective environmental and disease management
- Gaps in U.S. livestock data are a barrier to effective environmental and disease management
- Gaps in U.S. livestock data are a barrier to effective environmental and disease management
- Gaps in U.S. livestock data are a barrier to effective environmental and disease management
- Gaps in U.S. livestock data are a barrier to effective environmental and disease management
- Gaps in U.S. livestock data are a barrier to effective environmental and disease management
- Gaps in U.S. livestock data are a barrier to effective environmental and disease management
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