Probabilistic Programming in Julia
Julia's PPL community is strong and growing. The various groups already have a strong professional relationship, but a BoF would help to formalize this somewhat, as well as being a good introduction for newcomers to the community. Join the BoF channel on Discord.
Possible topics (just a starting point really):
- Standardizing output data structures for samplers
- Making distributions extensible, and AD- and GPU-friendly
- Interop across PPLs
- Connecting with non-PPL libraries (e.g. Flux)
- What existing (Python, etc) capabilities is Julia PPL missing?
- What's next for Julia PPL?
- What's next for PPL in general?
Chad Scherrer
Chad Scherrer is a Senior Research Scientist at RelationalAI.
Chad's work in probabilistic programming began in 2010, leading to the development of Passage, a Haskell-based language to generate parallel C/OpenMP code for Gibbs sampling. Following this, he served as technical lead for the language evaluation team for the DARPA program "Probabilistic Programming for Advancing Machine Learning".