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Tutorial
Probabilistic Programming
Frank Wood

Mon Dec 07 10:00 AM -- 12:00 PM (PST) @ Level 2 room 210 E,F

Probabilistic programming is a general-purpose means of expressing and automatically performing model-based inference. A key characteristic of many probabilistic programming systems is that models can be compactly expressed in terms of executable generative procedures, rather than in declarative mathematical notation. For this reason, along with automated or programmable inference, probabilistic programming has the potential to increase the number of people who can build and understand their own models. It also could make the development and testing of new general-purpose inference algorithms more efficient, and could accelerate the exploration and development of new models for application-specific use.

The primary goals of this tutorial will be to introduce probabilistic programming both as a general concept and in terms of how current systems work, to examine the historical academic context in which probabilistic programming arose, and to expose some challenges unique to probabilistic programming.

[ Slides] 

Author Information

Frank Wood (University of Oxford)

Dr. Wood is an associate professor in the Department of Engineering Science at the University of Oxford. Before that he was an assistant professor of Statistics at Columbia University and a research scientist at the Columbia Center for Computational Learning Systems. He formerly was a postdoctoral fellow of the Gatsby Computational Neuroscience Unit of the University College London. He holds a PhD from Brown University (’07) and BS from Cornell University (’96), both in computer science. Dr. Wood is the original architect of both the Anglican and Probabilistic-C probabilistic programming systems. He conducts AI-driven research at the boundary of probabilistic programming, Bayesian modeling, and Monte Carlo methods. Dr. Wood holds 6 patents, has authored over 50 papers, received the AISTATS best paper award in 2009, and has been awarded faculty research awards from Xerox, Google and Amazon. Prior to his academic career he was a successful entrepreneur having run and sold the content-based image retrieval company ToFish! to AOL/Time Warner and served as CEO of Interfolio.

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