Mayank Kishore

Papers

Total Citations

H-Index

About

Mayank Kishore investigates the intersection of artificial intelligence, human-robot interaction, and cognitive modeling, with a primary focus on optimizing team performance in mixed human-robot teams. His most cited work, "Predicting Individual Human Performance in Human-Robot Teaming" (2021, 5 citations), tackles a critical challenge: the inherent variability in human abilities that complicates task allocation in collaborative settings. Kishore’s research develops predictive models that account for individual human performance differences, moving beyond traditional approaches that treat humans as uniform agents. This work has implications for designing adaptive robotic teammates that can dynamically adjust to human partners, enhancing efficiency and safety in domains like manufacturing, healthcare, and autonomous systems. By bridging cognitive science and robotics, Kishore contributes to more intuitive and effective human-robot collaboration. His findings are particularly relevant for researchers developing intelligent systems that must operate alongside people, offering a framework for personalizing team coordination. As the field grows, Kishore’s emphasis on individual human variation positions him as a key voice in creating truly responsive and human-aware robotic teammates.