Taraneh Dean
Papers
6
Total Citations
67
H-Index
4
About
Taraneh Dean is a pioneering researcher in robotics and artificial intelligence, whose work has fundamentally shaped how autonomous systems perceive, plan, and act in complex environments. Her primary research areas include Bayesian decision theory, sensor fusion, adaptive control, and robotic navigation. Dean’s most significant contribution is the application of Bayesian decision theory as a unified framework for high-level robotic control, elegantly integrating sensor fusion, prediction, and sequential decision making. This approach, detailed in her highly cited 1992 paper (37 citations), explicitly values sensor information to optimize control policies. She further advanced the field by developing sensor abstractions that modularly combine sensing and planning, and by tackling the challenging problem of learning time-varying control laws in high-dimensional spaces. Her work also addresses the critical role of reasoning about change, linking observability and controllability from control theory to planning. Dean’s research has provided foundational insights for designing robust, adaptive robots capable of operating in unpredictable environments, making her a key figure in the evolution of intelligent autonomous systems.
Research Focus
Key Achievements
Top Papers
- 1A decision-theoretic approach to planning, perception, and control37 citations · 1992
- 2Sensor abstractions for control of navigation17 citations · 2002
- 3Toward learning time-varying functions with high input dimensionality5 citations · 2002
- 4Prediction, observation and estimation in planning and control4 citations · 2002
- 5
- 6Knowledge representations for learning control2 citations · 2002