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Daphne Koller

proper noun

In plain wordsAn Israeli-American computer scientist who wrote the standard text on probabilistic reasoning in AI, co-founded the online-course company Coursera, and founded a machine-learning drug-discovery company.

  1. Israeli-American computer scientist, a leading figure in probabilistic machine learning and founder of the drug-discovery company insitro. As a professor at Stanford University from 1995 she worked on representing and reasoning with uncertainty, developing probabilistic relational models and writing, with Nir Friedman, 'Probabilistic Graphical Models: Principles and Techniques' (2009), the field's standard text (see probabilistic graphical modelprobabilistic graphical model, n.: A way of describing how many uncertain quantities depend on one another by drawing them as a network, which makes it practical for a computer to reason about all of them together., Bayesian networkBayesian network, n.: A diagram of causes and effects with probabilities attached — say, how rain and a sprinkler can each make grass wet — that lets a computer update its beliefs about hidden causes when it sees evidence.). She applied these methods to biology and genomics and received a MacArthur Fellowship (2004) and the inaugural ACM-Infosys Foundation Award, now the ACM Prize in Computing. Her online version of the Stanford graphical-models course helped launch Coursera, which she co-founded with Andrew NgAndrew Ng, prop. n.: A British-born American computer scientist whose online courses have taught machine learning to millions; he also co-founded Google's deep learning team, Google Brain, and the online-course company Coursera. in 2012. After serving as chief computing officer of Alphabet's aging-research company Calico, she founded insitro (2018), which combines machine learning with large-scale biological experiments to find drug targets.

Related

See also
  • probabilistic graphical modelprobabilistic graphical model, n.: A way of describing how many uncertain quantities depend on one another by drawing them as a network, which makes it practical for a computer to reason about all of them together.
  • Bayesian networkBayesian network, n.: A diagram of causes and effects with probabilities attached — say, how rain and a sprinkler can each make grass wet — that lets a computer update its beliefs about hidden causes when it sees evidence.
  • Andrew NgAndrew Ng, prop. n.: A British-born American computer scientist whose online courses have taught machine learning to millions; he also co-founded Google's deep learning team, Google Brain, and the online-course company Coursera.
  • Judea PearlJudea Pearl, prop. n.: An Israeli-American computer scientist who gave AI the mathematics for reasoning under uncertainty and, later, for telling cause and effect apart from mere correlation.
  • machine learningmachine learning, n.: A way of building software that learns from examples instead of following rules written by a programmer. Shown thousands of labeled photos, for instance, a program can learn to recognize cats.

Sources

  1. Koller, D. & Friedman, N. (2009). Probabilistic Graphical Models: Principles and Techniques. MIT Press.