In this talk, we'll explore why some technologies end up inexplicably dominating our field while others fade away despite their obvious merits. What will it take for the languages we care about to succeed, and what are the consequences of success? Let's apply a broad perspective to the mess we've made of modern computer science, and explore our options for getting the situation back on track through a careful examination of history, science and ponies.

Although Ruby is not known as a functional language, it does support higher order functions in the form of lambdas and procs. Ruby's support for both object oriented and functional approaches, along with its conciseness, clarity, and expressiveness, make it an excellent choice as a general purpose programming language.

This session, geared toward the functional novice, shows how to implement functional approaches in Ruby, and shows why you would want to.

Topics covered will include:

• in testing, using lambdas to verify that certain behaviors do or do not not raise errors
• lambdas as predicates
• deferred execution
• composite functions
• nested functions
• using lambdas to hide variables
• functions that return functions (partial application, currying)
• lightweight event handling
• chaining behavior with lambda arrays
• how lambdas differ from conventional Ruby methods

Sync, Async, Blocking, Non-Blocking, Streaming are the buzzwords in the reactive programming world. This talk will attempt to attach some meaning to them. It will also demo the performance and resource consumption patterns for blocking-io, Scala Futures and RxJava Observables for comparable programs. Finally, a command line application that consumes twitter streams API will demo what is possible using the new reactive abstractions.

Immutable, persistent data structures form a big part of the value proposition of most functional programming languages.

It is important to understand why these data structures are useful and how they make it easier to reason about your program. 

It is also instructive to see how these data structures are implemented to get a greater appreciation for the inherent tradeoffs between performance and immutability.

In this talk I will do a walkthrough of some of these data structures drawing from the work of Chris Okasaki[1], and attempt to explain the essential ideas in a simple way. 

[1] Purely Functional Data Structures, Chris Okasaki, School of Computer Science, Carnegie Mellon University 

Brief history of recursion 

Snippets from a few languages

What is tail recursion?

Design choices around recursion

The importance of tail recursion in erlang

How do you profile such improvements?

Functional programming as a programming style and discipline is useful even in languages which are not pure functional languages. By practising programming in a pure functional language like Haskell, programmers can drastically improve the quality of code when coding in other languages as well.

The talk is based on first hand experience of using Haskell in internal courses in our organisation to improve code quality.

This talk will cover Gofer (one of the earliest variants of Haskell) as a teaching tool, including the choice of the language, the features from Haskell that should (and shouldn't) be covered and the obstacles and benefits of the exercise.

Haskell has populairzed typelcasses a principled way to add ad-hoc extensions on existing data types. They allow you to 1) add new operations on existing data types and 2) support new data types on existing operations, and thus solve the famous "expression problem".

There is a lot of similarily between typeclasses and Java good practices of programming to interfaces and preferring composition over inheritance. The missing link is the implicit dictionary passing which allows haskell to be much more concise and expressive.

In this tutorial, we will look at how Scala adopts typeclasses by adding the missing link of implicits.

Objected oriented languages like Scala support the paradigm of programming with functions. How does it work? The language has to conceptually map functions to objects. This tutorial will explain this idea by starting with a simple Java-like code and progressively refactor it to make use of higher order functions. As a result, you will learn about a few syntax sugars and also the cost implication of using objects to represent functions.