I am a Statistics Student at the University of Waterloo. Previously I worked for 6 years at the Bank of Canada. I have an MA in Economics from Carleton University and a BMath in Statistics from University of Waterloo.

My professional interests are in Survey Methods, Statistical Applications, and Large Language Models/AI.

On this site you will find links to my research, stata and R programs, class projects, and personal interest projects.

At the top-right corner you can find my CV as well as links to my LinkedIn, GitHub, and Google Scholar pages.

This website is generated using Hugo with the ananke template.

Recent Blog

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Statistical Machine Translation: R Package

I wrote an R package for conducting Statistical Machine Translation (SMT) as part of my first-year comps. Find it here. It is based largely on Koehn’s 2009 SMT book and implements the so-called “IBM” models, as well as phrase-based translation. While these methods have been largely supplanted by neural network-based methods, they are still interesting models, and the IBM models can be used to derive word alignments between a sentence and its translation.
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R Tutorial: Multi-State Models

I wrote this tutorial on estimating multi-state models in R as part of the class STAT 935 (Survival Analysis) at University of Waterloo. There are other tutorials out there, but this one (in my biased opinion, and to the best of my knowledge) is the only one that goes one by one through each type of mult-state model, the theory, how to structure the data, and how to estimate the models using the coxph function in R.
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Forests Ontario Blog: Heritage Tree Travellers Discover Ontario's Majestic Living History

Due to COVID, my wife and I have started enjoying nature a lot more. It turns out Ontario is a beautiful place with lots to see, including some really cool old trees! Forests Ontario has been cataloguing such trees in Ontario, and so we’ve been using their map to find them. Forests Ontario thought it was cool, and so asked me to write an article about our tree exploits! It was a really fun and nerdy experience and you can find the article here.
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The Big Data Paradox in COVID Surveys

Meng (2018) summarizes the Big Data Paradox as "The more the data, the surer we fool ourselves." It may be counterintuitive at first--even a trained Statistician is likely to get caught in the idea that "more data is better" at some point. But this idea can be quickly squashed with simple examples. For instance, if we are interested in measuring the average height of the population, surveying 1,000 men is going to give us an estimate that is too high.

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ksmirnovk: Stata Program for Performing a k-sample Kolmogorov-Smirnov Test

How Influential Are Music Critics?

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