Mykola Lukashchuk
Mykola Lukashchuk is a PhD candidate in Electrical Engineering at Eindhoven University of Technology, focusing on probabilistic inference and efficient Bayesian computation. His research develops flexible computational engines that trade precision for efficiency through message-passing algorithms and Riemannian manifold representations.
He holds dual Master's degrees in Statistics (Kyiv University) and Computer Science (Instituto Politécnico Nacional). His work contributes to the RxInfer ecosystem with particular focus on ExponentialFamilyManifolds.jl and efficient Bayesian inference methods.
Session
Traditional message passing-based Bayesian inference algorithms are fast but often struggle with real-world models that lack closed-form update rules, such as those with non-conjugate terms or any external code integrated into RxInfer: differential equation solvers, root finding, or even code outside of Julia. The Bayesian inference package RxInfer (from v3) introduces a powerful projection mechanism that overcomes this limitation, enabling modelers to maintain the speed of message passing while handling complex model components.
The core idea is simple: you define your joint distribution in Julia, then annotate challenging factors with ProjectedTo(<family>) constraints. RxInfer then automatically derives the necessary inference approximations and delivers streaming estimates of posterior distributions. This talk will demonstrate this workflow through two compact examples: a hierarchical Gaussian filter for choice data and integrating an ordinary differential equation (ODE) solver for an SIR epidemic model. Attendees will receive runnable notebooks and a clear recipe for applying reactive variational inference to their own non-conjugate or simulator-based models.