Tommy Reddad

As I went through a laptop upgrade and migration recently, I discovered remnants of my old academic webpage.

I had some fun over the last week preparing a gender-swapped version of EarthBound for the SNES.

I’ve been messing around in my free time doing a bit of coding for fun and to explore areas of interest.

I decided yesterday to rebuild this website using Hugo, while it was originally built using Jekyll.

galtonwatson is a Go module in early development which implements efficient algorithms for the generation and manipulation of Galton-Watson trees.

In my previous post, I discussed the Burrows-Wheeler transform, and embedded an HTML form on the page which dynamically computed the forward and inverse Burrows-Wheeler transforms with an additional move-to-front compression step.

Input BWT MTF BWT+MTF Alphabet: Bit count: Alphabet: Bit count: The above form implements algorithms to code and decode from the move-to-front compression scheme, with or without an additional application of the Burrows-Wheeler transform.

The splay tree is probably my favourite data structure.

Let \(U_1, U_2, \dots\) be an infinite sequence of independent \(\mathrm{Uniform}[0, 1]\) random variables.

These days, most of everyone’s favourite languages and libraries for scientific computing come ready-equipped with random number generators for most common univariate distributions: the uniform, binomial, normal, geometric, exponential, beta, etc.

This morning, I submitted the final version of my paper Discrete minimax estimation with trees (Devroye and Reddad, 2019), which is to appear in the Electronic Journal of Statistics.

When programmers hear the phrase “random tree,” they most likely think of a random binary search tree, i.e., a binary search tree built from the insertion of a uniformly random permutation of \(n\) keys—denote such a tree by \(\mathrm{BST}_n\).