Julia is a programming language for data science and numerical computing. Julia is a high-level, high-performance dynamic language, it uses just-in-time compilation to provide fast machine code - dynamic code runs at about half the speed of C, and orders of magnitude faster than traditional numerical computing tools.

Julia borrows two main ideas from Lisp lore:

1. Multiple-dispatch: a method is identified by a name and the types of all of its arguments (traditional OOP only uses the type of a single argument) multiple-dispatch allows for a natural programming model suitable for mathematics and general purpose programming. Interestingly, the same feature is responsible for Julia's ability to generate really fast machine code.
2. Macros: Julia has syntax that is familiar to users of other technical computing environments, however, Julia expressions are homoiconic -- Julia code can be represented as Julia data structures, and transformed from one form to another via hygienic macros. There are some very interesting uses of macros to create domain-specific languages for effective programming. 

Julia is a programming language for data science and numerical computing. Julia is a high-level, high-performance dynamic language, it uses just-in-time compilation to provide fast machine code - dynamic code runs at about half the speed of C, and orders of magnitude faster than traditional numerical computing tools.

Julia borrows two main ideas from Lisp lore:

1. Multiple-dispatch: a method is identified by a name and the types of all of its arguments (traditional OOP only uses the type of a single argument) multiple-dispatch allows for a natural programming model suitable for mathematics and general purpose programming. Interestingly, the same feature is responsible for Julia's ability to generate really fast machine code.
2. Macros: Julia has syntax that is familiar to users of other technical computing environments, however, Julia expressions are homoiconic -- Julia code can be represented as Julia data structures, and transformed from one form to another via hygienic macros. There are some very interesting uses of macros to create domain-specific languages for effective programming. 

(I will be presenting this workshop along with Viral B Shah - co-creator of Julia)

Escher.jl is a Julia package designed to give anyone the power to create sophisticated web-based UIs that are driven by data on server and inputs from the user. Escher is based on the principles of denotative programming - denotations of UI components (called Virtual DOM nodes) are handed off to the browser to be rendered. The result is a framework that abstracts the capabilities of CSS, JavaScript and HTML elements into a single consistent library. Interaction in Escher is done via Reactive.jl - a port of Elm's signal library. In this talk I will explain the inner workings of this Signal library, and of Escher and show how all the pieces fit together to form a convenient functional framework for full-stack web development.

Code used during the talk: https://github.com/shashi/fuconf-talk
Slides: https://docs.google.com/presentation/d/16Xfqd-xU8y2JEN0TIcacDoYnp0b5-W7ESDB5v1SmcXs/edit#slide=id.p

Elm is a strongly typed functional reactive programming (FRP) language that compiles to HTML, CSS, and Javascript. In Elm, the Signal type represents a time-varying value--things like mouse position, keys pressed, current time are signals. With Signals, one can write terse code that is isomorphic to a dataflow diagram of the app. The code hence feels natural and is 100% callback free. All this, with powerful type inference.

This talk is an introduction to FRP. It explores functionally composing graphics and UIs, and creating interactions and animations with the Signal type. There will also be an overview of Elm’s execution mechanism and the time traveling debugger: a consequence of Elm's purely functional approach.

While instructive, it will be good fun too, in the spirit of Elm.