Daphne Koller
Stanford University, Laboratoire d'Informatique de Paris-Nord
Papers
12
Total Citations
3,843
H-Index
11
About
Daphne Koller is a pioneering researcher at the intersection of probabilistic reasoning, machine learning, and robotics, whose work has fundamentally shaped how autonomous systems understand and navigate the world. She is perhaps best known for her landmark 2002 paper on FastSLAM, which revolutionized simultaneous localization and mapping by introducing a factored probabilistic approach that overcame critical limitations of the dominant Extended Kalman Filter methods — a contribution that has since garnered over 2,000 citations and remains a cornerstone of mobile robotics. Alongside this, her scalable Sparse Extended Information Filter algorithms for SLAM further demonstrated her commitment to computationally efficient solutions for real-world robotic challenges. Her 2009 textbook, *Probabilistic Graphical Models: Principles and Techniques*, has become an essential reference in the field, cited nearly 500 times and used widely in graduate curricula worldwide. Koller's broader research portfolio spans probabilistic object detection, multi-agent planning under uncertainty, and hierarchical environment modeling, reflecting a consistent drive to equip robots with richer, more adaptive representations of dynamic environments. Her work exemplifies the powerful synergy between principled probabilistic theory and practical autonomous systems.
Research Focus
Key Achievements
Top Papers
- 1FastSLAM: a factored solution to the simultaneous localization and mapping problem2,049 citations · 2002
- 2Simultaneous Localization and Mapping with Sparse Extended Information Filters648 citations · 2004
- 3
- 4Detecting and modeling doors with mobile robots152 citations · 2004
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- 7Integrating Visual and Range Data for Robotic Object Detection88 citations · 2008
- 8Planning under uncertainty in complex structured environments84 citations · 2003
- 9
- 10Sampling in Factored Dynamic Systems59 citations · 2001