Under the supervision of Daniel Roy, I’m working to make theoretical advancements in fundamental machine learning. I’m interested in generalization error and concentration inequalities that are comparable to current empirical results. Recently, I’ve been looking at these in the context of sequential prediction, and am trying to quantify some notion of model misspecification in this framework. Progress in this area will improve the reliability and interpretability of modern algorithms, which I believe is crucial to deploying them for decisions where human lives are at stake.
I have also spent time working on queueing theory results with David Stanford, and I’m actively involved in these projects as they develop.
To view my curriculum vitae, click here.
PhD, Statistics, 2023 (expected)
University of Toronto
BSc, Financial Modelling, 2018
If you have a problem to solve or just an idea that you think is interesting, please send me an email with the details. Particularly if the problem is related to fundamental machine learning, I’d be eager to collaborate on a project.
I also do more formal consulting for traditional statistics problems such as model fitting, inference, and interpretation. Some consulting projects I’ve worked on in the past involved identifying distributional differences between training and testing data, predicting travel preferences for app users, and modelling residential real estate prices.