Supervisory Performance

Supervisory performance research focuses on improving the effectiveness of human or automated systems that oversee other systems or processes. Current research explores diverse approaches, including enhancing human supervisory capabilities through haptic feedback and developing sophisticated deep neural networks that leverage multiple input sources and supervisory signals for improved accuracy and efficiency, particularly in complex tasks like tractography filtering and autonomous driving. These advancements are significant for improving the reliability and performance of automated systems across various domains, from manufacturing quality control to AI-driven applications.