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About

Dan Pan is a human factors and ergonomics researcher whose work sits at the intersection of cognitive psychology, space operations, and human-robot interaction. His research primarily focuses on teleoperation performance — the study of how astronauts and operators remotely control robotic systems in high-stakes environments such as space missions, including extravehicular rescue, satellite servicing, and space station assembly. Pan's most influential contribution examines how individual characteristics shape teleoperation effectiveness, a 2016 study that has garnered 19 citations and laid important groundwork for astronaut selection and training protocols. Building on this foundation, his 2021 work applies cognitive fit theory to explore how information format and cognitive characteristics jointly influence operator performance, adding 12 citations to his growing impact. His development of a fuzzy AHP-based evaluation model provides a practical, multi-index framework for scientifically assessing teleoperation performance in astronaut selection contexts. More recently, Pan has expanded into human-robot collaborative rescue, investigating how cross-training methods can improve team efficiency and safety in post-disaster scenarios. Collectively, his research offers meaningful insights for designing better training programs, selection criteria, and human-robot teaming strategies — making him a valuable voice in both space systems engineering and emergency response applications.