TECoSA Research Seminar: Efficient Monte Carlo Inference for Probabilistic Programming Languages

Speaker:  Daniel Lundén, TECoSA postdoc
Location: Gustaf Dahlander (floor 3, Teknikringen 31, KTH Campus)
Zoom link and sign-up link circulated to members
Please email vickid@kth.se if you have any questions.

ABSTRACT: Probabilistic programming languages (PPLs) allow users to express statistical inference problems that the PPL implementation then, ideally, solves automatically. In particular, PPL users can focus on encoding their inference problems, and need not concern themselves with the intricacies of inference. In the last decade, PPL-related research have seen a dramatic increase, with applications in research fields such as evolutionary biology, epidemiology, cognitive science, motion forecasting, computer vision, database cleaning, and inverse graphics.

In this seminar, Daniel will give an introduction to PPLs, their applications, and my research. His research focuses on efficient implementations of Monte Carlo inference algorithms as part of PPL compilers, with a particular interest in programming language theory, compilers, and static program analysis.

You can read more about Daniel’s research at: https://dlunde.github.io/