Talk Topic: Probalistic Language Scheme
Alexy Radul, MIT
October 17, 2007

Abstract
Reasoning with probabilistic models is a widespread and successful technique in areas ranging from computer vision, to natural language processing, to bioinformatics. Currently, these reasoning systems are either coded from scratch in general-purpose languages or use formalisms such as Bayesian networks that have limited expressive power. In both cases, the resulting systems are difficult to modify, maintain, compose, and interoperate with. This work presents Probabilistic Scheme, an embedding of probabilistic computation into Scheme. This gives programmers an expressive language for implementing modular probabilistic models that integrate naturally with the rest of Scheme. 

Bio
Alexey, a summer 2007 ITA intern, is a PhD student at CSAIL. His career in computer science began by meeting Gerry Sussman, in whose orbit he has remained ever since. His current Lagrange point is the Right Way to build support for probabilistic inference into general-purpose programming languages.