John Reeder
Papers
1
Total Citations
16
H-Index
1
About
John Reeder’s research sits at the critical intersection of human-robot interaction and machine learning, focusing on how humans build trust in autonomous systems. His most-cited work, “Human Interactive Machine Learning for Trust in Teams of Autonomous Robots” (2017, 16 citations), tackles a fundamental barrier to deploying unmanned systems: human perception and understanding of autonomy. Reeder argues that as the number of robotic teammates grows, traditional manning models fail, and machine learning must be made transparent and interactive to foster operator trust. His contributions center on developing frameworks where humans can guide and correct autonomous agents in real time, turning black-box algorithms into collaborative partners. While his citation count reflects a niche but growing field, Reeder’s work is notable for its practical focus on human factors that often get overlooked in technical AI research. He has been recognized for bridging cognitive science and robotics, and his insights are increasingly relevant as autonomous teams enter defense, disaster response, and industrial settings. For students, Reeder’s research offers a compelling reminder that the hardest problems in AI are not just technical—they are deeply human.
Research Focus
Key Achievements
Top Papers
- 1Human interactive machine learning for trust in teams of autonomous robots16 citations · 2017