Daphne Koller

Digital Library

ACM AAAI Allen Newell Award

USA - 2019


For pioneering contributions to machine learning and probabilistic models, the application of these techniques to biology and human health, and for contributions to democratizing education.

Daphne Koller is recognised for foundational work in a range of domains, including machine learning and probabilistic models; game theory and economics; computational biology and medicine; computer vision and robotics; decision making under uncertainty; and digital education. Koller was a leader in the development and use of graphical models, including learning the model structure as well as its parameters, and pioneered the unification of statistical learning and relational modeling languages. She also developed foundational methods for inference and learning in temporal models. Her textbook (with Nir Friedman) Probabilistic Graphical Models is the definitive text in this area.

As an early leader in bringing machine learning methods to the life sciences, she developed Module Networks, wherein she and her co-authors harnessed modularity in gene regulatory programs to build an effective model of gene activity. She has developed ground-breaking applications of machine learning to pathology, work that not only demonstrated the ability of machine learning to outperform human pathologists, but also was one of the first to highlight the importance of the stromal tissue in cancer prognosis (now well-recognized). Koller is also the co-founder and former co-CEO of Coursera, a platform offering free education from top universities to people worldwide. Coursera, now in its eighth year, has touched the lives of over 50 million learners in every country in the world. Koller is currently the founder and CEO of insitro, a biotech startup which works to discover better medicines through the integration of machine learning and biology at scale.

Press Release

ACM Prize in Computing

USA - 2007


For her work on combining relational logic and probability that allows probabilistic reasoning to be applied to a wide range of applications, including robotics, economics, and biology.

Prof. Koller's work on combining relational logic and probability is the most important of her many research contributions in Artificial Intelligence and Computer Science. It has transformed the way people handle uncertainty in large computer systems, such as heterogeneous databases, image understanding systems, biological and medical models, and natural language processing systems.

Her interest in this topic stems from her 1994 PhD dissertation. At that time logical reasoning and probabilistic reasoning were two distinct sub-fields in AI, with very little interaction. Prof. Koller and a few others recognized that relational logic and probability were complementary. Relational logic brings expressive power, but cannot handle the uncertainty that is inherent in most real-world domains. On the other hand, probability (and probability-based knowledge representation tools, like Bayesian networks and Markov models) provides a sound methodology for dealing with uncertainty, but is unable to reason about objects and the relations between them. The combination of relational logic and probability led to a new knowledge representation paradigm, known as relational probabilistic models. Prof. Koller's Computers and Thought Award Lecture at IJCAI 2001 established it as a major research area in AI.

Aside from establishing the foundations, Prof. Koller's algorithmic work brought these models into the realm of feasibility. A good knowledge representation language must be expressive, and must also support efficient inference and learning algorithms. During the last decade, she and her students have both added to the theory and built operational systems that apply these ideas to real-world domains involving millions of objects, variables, and relations. Her learning algorithms make it possible to construct large models of complex domains, for example in biology, epidemiology, and computer vision. Her inference algorithms make it possible to evaluate the probability of a query in a way that exploits all of the information available about the huge number of inter-linked objects in the domain.

Prof. Koller's work has influenced many other areas of computer science and other fields, including information retrieval from heterogeneous databases, natural language understanding, robotics, machine perception, economics, and biology. She has applied her approaches to a number of important real problems, including biological problems and robot perception problems.