Category Archives: Probabilistic programming

Interview with Matt Might, Part 2

Matt Might at the White House, Jan 2015

Matt at the White House, Jan 2015

This post is the second part of my March 10th interview of Matt Might, a PL researcher and Associate Professor in the Department of Computer Science at the University of Utah.

In Part I, we talked about Matt’s academic background, his PL research (including his favorite among the papers he’s written), and his work on understanding and treating rare disease, which began with the quest to diagnose his son Bertrand, and has led to a role in the President’s Initiative on Precision Medicine.

In this post, our conversation continues, covering the topics of blogging, privacy, managing a crazy schedule, and looking ahead to promising PL research directions. Continue reading

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Filed under Bioinformatics, Interviews, Language adoption, Probabilistic programming, Program Analysis, Scientists, Software Security, Types

What is probabilistic programming?

In this post, I introduce the emerging area of probabilistic programming, showing how probabilistic programs will hopefully make it easier to perform Bayesian-style machine learning, among other applications. Probabilistic programming is an exciting, and growing, area of research, with fantastic people in both AI/ML and PL working together and making big strides. PL methods — including formal semantics, optimization techniques, and forms of static analysis — have proven very useful in advancing this area forward.

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Filed under Formal verification, Probabilistic programming, Program Analysis, Semantics