I'm a fourth year CS PhD student at UofT co-advised by
Prof. Nicolas Papernot and
Prof. Chris Maddison, supported by a
Vanier Canada Graduate Scholarship. My research explores the relationship between training data and the models we learn, and how that can inform better ML practices: e.g., the
privacy of individual training data, vulnerability to
data "forging",
distributional robustness, and
improving performance by using better data. Before my PhD, I was a math specialist student (pure math undergrad) at UofT. If you are a student at UofT interested in Machine Learning, consider coming to our weekly
ML lunch talks at Vector! We talk about what's new and exciting in the field, from theory to applications: no expertise required, just curiosity.
A collection of expository essays written while studying C* algebras and their classification under Prof. George Elliott during my undergrad.
The goal of these essays is to present the main prerequisite ideas for a particular problem in one relatively short document (as a sort of reference guide with a common theme).