The Julia programming language is proving to be a highly productive tool in the world of computational biosciences and beyond. In this workshop we explore Julia with applications to systems biology and bioinformatics. No prior knowledge of Julia is assumed. We first walk through key applications in computational biology, showcasing and explaining Julia’s advantages over existing tools. We then slow down a notch and take a step by step approach with elementary statistical applications. An outcome of this workshop is to enable participants to carry out first steps of basic data analysis in Julia, as well as dive into advanced existing Julia research code openly available.
Requirements: No prior Julia experience is needed. Some experience with a language like R, MATLAB, or Python is assumed. You may optionally install Julia prior to the workshop and work along during the workshop, or you may sit back and take it all in prior to exploring Julia yourself.
Sales Engineer, JuliaHub
At JuliaHub, Dr. Roesch shares her knowledge of the impact of Julia and Julia Computing products on Quantitative Systems Pharmacology. She earned her PhD in Theoretical Systems Biology from the University of Melbourne, Australia. She has researched and published about the use of the Julia programming language in Systems Biology.
Mathematical Data Science Researcher, The University of Queensland
Associate Professor Yoni Nazarathy from the University of Queensland Australia specializes in data science and applied probability. His specific research interests include scheduling, control, queueing theory, and machine learning. He has been at The University of Queensland for over a decade and prior to his previous academic positions in Melbourne and the Netherlands, he worked in the aerospace industry in Israel. Currently he is also a part time industry machine learning consultant. He is the co-author of a data science book, Statistics with Julia: Fundamentals for Data Science, Machine Learning and Artificial Intelligence as well as an ongoing book with drafts available online, The Mathematical Engineering of Deep Learning.