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

11
H-Index
12
Papers
3,843
Total Citations
320
Avg Citations/Paper
🏆 Most Cited Paper
FastSLAM: a factored solution to the simultaneous localization and mapping problem
2,049 citations · 2002
📈 Most Prolific Year: 2004 (4 Papers)
🤝 Key Collaborators: 19
🏛 Institutions: Stanford University, Laboratoire d'Informatique de Paris-Nord

Top Papers

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Key Collaborators

Contact & Links

Available for collaboration
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