Meredith Adkins

OverviewAI-generated summary

Meredith Adkins' research focuses on the application of machine learning and advanced computational techniques to address challenges in remote sensing and agricultural systems. Adkins has received significant federal funding, including a $4,998,818 NSF Convergence Accelerator grant for the "Cultivate IQ - Empowering Regional Food Systems" project and a $743,651 NSF Convergence Accelerator grant for "Data-driven Agriculture to Bridge Small Farms to Regional Food Supply Chains." These projects underscore a commitment to developing data-driven solutions for regional food systems and agricultural supply chains.

Adkins' recent publications demonstrate a focus on developing novel transformer-based models for image segmentation and data analysis. Works such as "AerialFormer: Multi-Resolution Transformer for Aerial Image Segmentation" and "SolarFormer++: Multi-scale Transformer for Solar PV Profiling and Obstruction Localization for Degradation Mitigation" highlight the use of sophisticated deep learning architectures for analyzing aerial imagery and solar photovoltaic data. Collaborations with researchers such as Chase Rainwater and Jackson Cothren at the University of Arkansas at Fayetteville have contributed to this body of work, as evidenced by shared publications.

Metrics

• h-index: 2
• Publications: 5
• Citations: 82