Mohamed Tarek
I am a PhD student at UNSW Canberra working on topology optimization. I have 2 seemingly unrelated interests that I hope to combine one day: topology optimization and Bayesian inference. This talk is all about the latter, so let's focus on that. I joined the TuringLang development team 1-2 years ago. Since then, my main goal has been to make Turing as fast as possible without sacrificing usability. In this talk, I share some of the work I have done towards that goal and give an overview of the present and future of Turing and DynamicPPL including features, short-term goals and bottlenecks.
GitHub: https://github.com/mohamed82008
Session
We present DynamicPPL.jl, a modular library providing a lightning-fast infrastructure for probabilistic programming and Bayesian inference, used in Turing.jl. DynamicPPL enables Turing to have C/Stan-like speeds for Bayesian inference involving static and dynamic models alike. Beside run-time speed, DynamicPPL provides a user-friendly domain-specific language for defining and then querying probabilistic models.