This half-semester course introduces computational thinking through applications of data science, artificial intelligence, and mathematical models using the Julia programming language. This Spring 2020 version is a fast-tracked curriculum adaptation to fo…

Julia is a fast, flexible and robust language. Having used Julia for 2 weeks, and Python for 7 years, I can already say I prefer Julia. It is not as mature a…

When analysing pharmacokinetic data to determine the degree of exposure of a drug and associated pharmacokinetic parameters (e.g., clearance, elimination half-life, maximum observed concentration (), time where the maximum concentration was observed (), N…

Presented by Avik SenguptaIn this talk, Avik will demonstrate how Julia combines dynamic, high level source with a high performance runtime code. He will sho…

The strongest legacy of Lisp in the Julia language is its metaprogramming support. Like Lisp, Julia represents its own code as a data structure of the language itself. Since code is represented by objects that can be created and manipulated from within th…

Manipulating and visualizing pizza sales data using Julia DataFrames.jl and Plots.jl…

Non-local, semantic compiler transformations such as Automatic Differentiation and certain Bayesian Inference algorithms pose particular challenges for dynam…

Here’s a language that gives near-C performance that feels like Python or Ruby with optional type annotations (that you can feed to one of two static analysis tools) that has good support for macros plus decent-ish support for FP, plus a lot more. What’s …

There’s a big difference between knowing how to follow a recipe and knowing how to cook. If you can master the first principles within a domain, you can see much further than those who are just following recipes. That’s what Julia Child, “The French Chef”…