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.

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.

I gave this presentation on December 6, 2021 as part of the course SURV727 “Fundamentals of Cmputing and Data Display.” I use R for data collection, and then clean and analyze the data in Stata. My data science experience grew quickly and greatly during this course, and I had a lot of fun combing various sources of data involving API’s from Spotify, Last.FM, Wikipedia, and Google, and using web scraping techniques to obtain review scores from Wikipedia.