A Biostatistics PhD student from the University of Oslo. I'm working on combining computer simulations with Bayesian statistical models. I have been a big fan of Julia since version 0.4. My research interests are Bayesian statistics, symbolic computing and applied category theory.
This workshop will introduce the recommended workflow for applied Bayesian data analysis by working through an example analysis together. We will start with the simplest non-trivial model and use increasingly sophisticated models to explain the properties of our data set based on model diagnostics. We will also give an overview of the different probabilistic programming packages in Julia and show where we have advantages over other languages such as Stan and Python.
Designing vaccines is an expensive and time consuming process. This talk demonstrates how we can exploit automatic differentiation of ODEs, parallelization, stochastic search and Bayesian optimization to minimize post-vaccination invasive pneumococcal disease and antibiotic resistant strains in a bacteria population using a novel computational model of the bacterial population dynamics that integrates epidemiological and genomic data.
Have you ever wished some NumPy/jax/PyTorch/TensorFlow code was written in Julia instead? Have you ever translated ODEs written in Matlab to Julia by hand? Would you like to use sparse arrays or GPU computation in a legacy Stan model? Transpilers.jl is a common interface for translating numerical code into Julia. Currently it has backends for R and Python. Matlab and Stan backends are in development.