JuliaCon 2022 (Times are UTC)

Dmitry Bagaev

My research interests lie in the fields of computers science, machine learning and probabilistic programming. Currently I am a PhD candidate in the SPS group of Electrical Engineering department in Eindhoven University of Technology. I’m working on a high-performant implementation of message passing-based Bayesian inference package in the Julia programming language. My research project focuses on Signal Processing and Active inference applications, but is also aimed to expand the scope of possible applications for message passing in general.


GraphPPL.jl: a package for specification of probabilistic models
Dmitry Bagaev

We present GraphPPL.jl - a package for user-friendly specification of probabilistic models with variational inference constraints. GraphPPL.jl creates a model as a factor graph and supports the specification of factorization and form constraints on the variational posterior for the latent variables. The package collection GraphPPL.jl, ReactiveMP.jl and Rocket.jl provide together a full reactive programming-based ecosystem for running efficient and customizable variational Bayesian inference.