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Colloquium: Probabilistic Programming and Artificial Intelligence - Vikash Mansinghka

Event Details

Date
Tuesday, February 26, 2019
Time
4-5 p.m.
Location
Description
Probabilistic programming is an emerging field at the intersection of programming languages, probability theory, and artificial intelligence. This talk will show how to use recently developed probabilistic programming languages to build systems for robust 3D computer vision, without requiring any labeled training data; for automatic modeling of complex real-world time series; and for machine-assisted analysis of experimental data in synthetic biology that is too small and messy for standard approaches from machine learning and statistics. This talk will use these applications to illustrate recent technical innovations in probabilistic programming that formalize and unify modeling approaches from multiple eras of AI, including generative models, neural networks, symbolic programs, causal Bayesian networks, and hierarchical Bayesian modeling. Specifically, it will present languages in which models are represented using executable code, and in which inference is programmable using novel constructs for Monte Carlo, optimization-based, and neural inference. It will also present techniques for Bayesian learning of probabilistic program structure and parameters from real-world data. Finally, this talk will review challenges and research opportunities in the development and use of general-purpose probabilistic programming languages that are both performant enough and flexible enough for real-world AI engineering. BIO: Vikash Mansinghka is a research scientist at MIT, where he leads the Probabilistic Computing Project. Vikash holds S.B. degrees in Mathematics and in Computer Science from MIT, as well as an M.Eng. in Computer Science and a PhD in Computation. He also held graduate fellowships from the National Science Foundation and MIT’s Lincoln Laboratory. His PhD dissertation on natively probabilistic computation won the MIT George M. Sprowls dissertation award in computer science, and his research on the Picture probabilistic programming language won an award at CVPR. He co-founded two VC-backed startups: Prior Knowledge (acquired by Salesforce in 2012) and Empirical Systems (acquired by Tableau in 2018). He served on DARPA’s Information Science and Technology advisory board from 2010-2012, currently serves on the editorial boards for the Journal of Machine Learning Research and the journal Statistics and Computation, and co-founded the International Conference on Probabilistic Programming. Coffee and cookies will be available.
Cost
Free

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