Stephan Sahm
Stephan Sahm is founder of the Julia consultancy Jolin.io, and organizer of the Julia User Group Munich Meetup. In his academic days, he certified as Master of Applied Stochastics, Master and Bachelor of Cognitive Science, and Bachelor of Mathematics/Informatics. Since more than 5 years Stephan Sahm works as senior consultant for Data Science and Engineering, now bringing Julia to industry.
Stephan Sahm's top interest are in green computing, functional programming, probabilistic programming, real time analysis, big data, applied machine learning and in general industry applications of Julia.
Aside Julia and sustainable computing, he likes chatting about Philosophy of Mind, Ethics, Consciousness, Artificial Intelligence and other Cognitive Science topics.
Sessions
IsDef.jl provides maintainable type inference in that it
1. Uses code generation where possible to deterministically infer the types of your code
2. Allows you to overload type-inference with your custom implementation
3. If neither code generation works nor a custom type inference rule is given, it falls back to Core.Compiler.return_type, wrapped by some safety nets
In this talk IsDef.jl is presented, along with typical applications and details about the implementation.
My super tiny helpers which may also help you. Let me present to you SimpleMatch.jl for nice inline dispatch, NotMacro.jl for using @not instead of !, and ProxyInterfaces.jl for quickly creating proxy types for Dicts, Arrays, etc..
You want to build a complex macro? ExprParsers.jl gives you many prebuilt expression parsers - for functions, calls, args, wheres, macros, ... - so that you don't need to care about the different ways these high-level Expr-types can be represented in Julia syntax. Everything is well typed, so that you can use familiar julia multiple dispatch to extract the needed information from your input Expr.