Hi, I'm Ezra Savard

I'm an engineer who likes coffee, tinkering and weightlifting.

Hi, I'm Ezra Savard

Welcome to my site!

At work, I develop software for large scale concurrent systems. In my free time I enjoy lifting weights, studying things, and playing around with various technologies, like Jekyll, which I learned to make this website.

On this site, you'll find a growing pile of information about my undergraduate research, a bit about hobby projects and some random odds and ends. Happy browsing.

Most Recent Posts

Minimax Solver for 2048

16 Apr 2017

In the last few months I’ve been studying some extra computer science through edX. Most recently, I have been working through the ColumbiaX AI course , which seems to follow AI AMA pretty closely, at least for the first half. The course has had a few projects, but I really enjoyed the most recent one: writing a search agent to play 2048.

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To Always Improve

25 Mar 2017

About two years ago I realised that I needed to stop being insecure. When it comes to insecurity, there are a lot of ideas out there like imposter syndrome that I do not personally resonate with. I gave a lot of thought to what it meant to me to feel insecure and came up with a fairly simple pact that sounds a bit dramatic reading it now.

I will never again feel insecure.

I may feel unsure, afraid or confused, but those feelings have a source and I will answer them.

Insecurity in itself is useless.

Insecurity is the unanswered call to improve myself.

It is the certain knowledge that I am not being my best.

I like how this approach has been working out for me. In particular, I’ve come to appreciate how natural it is to gauge myself against my own ideals, something that I used to fight against. Though I can never reach an ideal (by definition?), I feel great satisfaction in knowing that I am making progress in the right direction.

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Quantum Monte Carlo - Version 2.0 and Animations

02 Nov 2016

As a first step in my undergraduate research on simulating physical quantum annealing, I wrote some Monte Carlo solver. I wrote my first implementation of a path-integral Quantum Monte Carlo (PI-QMC) in Python using numpy linear algebra operations. The code quality was not great though and it was not particularly nice to use. So a few weeks ago, I decided to re-write it as something I would be happy to share with others, so here it is!

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