Audrey Balaska
Papers
1
Total Citations
2
H-Index
1
About
Audrey Balaska is a researcher whose work lies at the intersection of robotics, probabilistic modeling, and human-robot teaming. Her primary research focuses on developing mathematical frameworks to represent and optimize search tasks conducted by heterogeneous robot teams or mixed human-robot groups. In her most-cited paper, "Development of a Model and Estimation Method to Represent Team Search with Uncertain Detection" (2020), Balaska introduces a novel approach that models object-finding as a probabilistic detection process. This means searchers may need to revisit areas, accounting for imperfect sensing and the inherent uncertainty of real-world environments. By providing an estimation method for such dynamic, uncertain searches, her work has direct implications for applications like search-and-rescue, environmental monitoring, and autonomous exploration. While her citation count is still growing—reflecting an early-career stage—her contributions offer a rigorous foundation for future work in multi-agent coordination and decision-making under uncertainty. Balaska’s research is particularly valuable for students and engineers interested in bridging theory and practice in field robotics and human-robot collaboration.
Research Focus
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Top Papers
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