JuliaCon 2022 (Times are UTC)

Jose Storopoli

Associate Professor and Researcher of the Department of Computer Science at Universidade Nove de Julho - UNINOVE located in São Paulo - Brazil.
Lead on Education and Training at Pumas-AI.

Teaches undergraduate and graduate courses in Data Science, Statistics, Bayesian Statistics, Machine Learning and Deep Learning using Julia, R, Python, and Stan. Contributor to Julia, R and Stan ecosystems. Proficient in C/C++ and Rust.
Has published Julia, Rust, R, and Python packages in official repositories/registries.

Researches, publishes and advises PhD candidates on topics about Bayesian Statistical Modeling and Machine Learning applied to Decision Making.
Principal Investigator of LabCidades - Smart City Research Lab at UNINOVE.

Coauthor of Julia Data Science book.
Leads the development of education and training materials for Pumas users in Julia.
Member of the Stan Governing Body - SGB.
Member of the Turing.jl Developer Team.
Certified RStudio Tidyverse Instructor.


TuringGLM.jl: Bayesian Generalized Linear models using @formula
Jose Storopoli

TuringGLM makes easy to specify Bayesian Generalized Linear Models using the formula syntax and returns an instantiated Turing model.


@formula(y ~ x1 + x2 + x3)

Heavily inspired by brms (uses RStan or CmdStanR) and bambi (uses PyMC3).