Above all, Koller believes her work "can provide a framework to use as a starting point for answering tough questions." She says she hopes to resolve "the complex network of interactions between many genes, proteins, metabolites and signals" using computational modeling, and is optimistic that she can help to identify patterns of gene expression across species as her methods are further developed.
When asked what she plans to do with the award, Koller said that the matter will require significant thought. "It's really a major decision, and they give you some time to think about it." The news always comes as a big surprise, since the selection process is confidential and no notification is given to fellows until final selections are made.
Koller's research tackles questions of how complex information with high levels of uncertainty can be approached using algorithms, probabilistic modeling and other computational methods. These tools strive to represent knowledge and reasoning at the intersection of traditional logic and subjective judgment, and have far-reaching implications in the fields of artificial intelligence and biomedical and genetic data analysis.
A significant contribution of Koller's work is the expansion of Bayesian networks -- reasoning frameworks that deal with uncertainty -- by showing how they can be organized into logical, object-oriented hierarchies. She has advanced this concept by implementing "probabilistic relational models," which blend logical and statistical representations in ways that employ standard deductive reasoning.