Nathan J. McNeese
Papers
6
Total Citations
181
H-Index
6
About
Nathan J. McNeese is a leading researcher at the intersection of human factors, cognitive engineering, and artificial intelligence, whose work is fundamentally reshaping how we understand and design human-robot teams. His primary research areas focus on human-autonomy teaming, shared cognition, and the critical dynamics of interaction within mixed human-robot groups. McNeese’s major contributions lie in empirically demonstrating that effective human-robot teams require more than just functional autonomy; they need sophisticated, human-like teaming principles. His highly cited 2020 work (87 citations) established a foundational framework for understanding human-robot teams through the lens of all-human team interaction and shared cognition. He has further advanced the field by exploring the tradeoffs of explanation-based communication in search and rescue (35 citations) and, most recently, by investigating the crucial role of AI teammate etiquette for building trust and effective collaboration (20 citations). Notably, McNeese is also pioneering the integration of large language models into robotics with his CLEAR platform, enabling prompt-engineered, context-aware robot control. His work is not just theoretical; it provides the practical, evidence-based design guidelines necessary for the next generation of collaborative autonomous systems.
Research Focus
Key Achievements
Top Papers
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- 3Understanding the impact and design of AI teammate etiquette20 citations · 2023
- 4Developing Human-Robot Team Interdependence in a Synthetic Task Environment19 citations · 2019
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