Jacob Hinkle

Papers

Total Citations

143

H-Index

About

Jacob Hinkle is a leading researcher at the intersection of machine learning, microscopy, and high-performance computing. His work focuses on developing automated and autonomous experimental systems for electron and scanning probe microscopy, where he integrates artificial intelligence to enable real-time decision-making during experiments. Hinkle’s most-cited paper, “Automated and Autonomous Experiments in Electron and Scanning Probe Microscopy” (2021, 134 citations), has become a foundational reference in the field, demonstrating how ML/AI can transform physics research from theory and materials prediction to high-throughput data analysis. He also contributes to large-scale image segmentation, tackling the computational challenges of analyzing massive datasets on supercomputing platforms like Summit. By bridging the gap between advanced microscopy and scalable AI, Hinkle’s work accelerates materials discovery and enables experiments that were previously impossible. His achievements highlight a commitment to pushing the boundaries of autonomous scientific discovery, making him a key figure in the next generation of intelligent, data-driven research.