Is functional programming for experts? We have found that children learning programming also benefit!

Over the last 5 years, we have taught 25K children through the McMaster University’s outreach program McMaster Start Coding (Canada), and started training undergraduates from peer institutions, including Vellore Institute of Technology, Cihan University (Kurdistan), and Narasu’s Sarathy Institute of Technology (India), so that they they can create their own programs.

Our main motivation for teaching functional programming to children is the similarity between algebra and functional programming. Algebra is a barrier to secondary and postsecondary education, and helping children overcome this barrier can have a huge impact.

We continued to develop our program because both children and teachers loved it. Children find it easy to learn about functions when the functions draw shapes on their screen using a compositional language they find easy to learn. Teachers love it, because children immediately understand concepts including Cartesian coordinates which are difficult to motivate for some.

In this talk we will explain the design philosophy behind our open-source graphics library GraphicSVG for Elm, and why we think Elm (with pure functions, and well-typed standard library) is the best language to teach 10- to 14-year olds.

We will give you a toolkit for advocating functional programming in your local schools, and show what children can create, including children from Vikas School in Hyderabad who were isolating at home with only a smartphone to learn programming on.

Mathematical optimization has vast applications, e.g., image processing, machine learning. It has a huge impact on the current state of computer science and software engineers.

Solving optimization problems involves many challenges. First, we need to build and encode the models correctly. Second, almost all useful models are computationally expensive and we need to speed them up. Finally, we need to be able to feed the model to optimization solvers to get the final numerical results.

Fortunately, we find that Haskell, with its powerful type system and abstraction power, is perfect for this type of problem.

We introduce HashedExpression, a Haskell-embedded domain-specific language that allows us to build and solve optimization problems. It provides users with a type-safe, correct-by-construction interface that uses Haskell type-level programming to express constraints on correctness which the compiler uses to flag many modeling errors as type errors (at compile time).

The library implements symbolic expressions with the "hash consing" technique to achieve common subexpression elimination. We will show how using monadic type class instances help us abstract away details of the underlying lookup table and implement symbolic reverse-mode differentiation on top of it.

For high-performance purposes, we generate low-level C/C++ code for symbolic expressions and provide bindings to open-source optimization solvers such as Ipopt or L-BFGS-B.