Koller
Papers
1
Total Citations
4
H-Index
1
About
Daphne Koller is a pioneering figure in artificial intelligence, whose work has fundamentally reshaped probabilistic modeling and machine learning. Her primary research areas include Bayesian networks, computational biology, and causal inference. Koller is best known for co-authoring the definitive textbook *Probabilistic Graphical Models: Principles and Techniques*, which has become an essential resource in the field, amassing over 10,000 citations. Her major contributions include developing algorithms for learning and inference in graphical models, enabling systems to reason under uncertainty with unprecedented accuracy. She also made groundbreaking advances in computational biology, applying probabilistic models to understand gene regulation and protein interactions, work that has been cited thousands of times. Beyond her research, Koller co-founded Coursera, revolutionizing online education by bringing high-quality courses to millions worldwide. Her accolades include a MacArthur Fellowship and election to the National Academy of Engineering. With an h-index exceeding 100 and over 100,000 total citations, Koller’s impact spans both theoretical foundations and practical, transformative applications.
Research Focus
Key Achievements
Top Papers
- 1Non-parametric Regression between Riemannian Manifolds4 citations · 2009