This is my doctoral thesis, synthesizing all of my work completed during my PhD program at UCSD. It explores privacy notions for releasing samples and various methods for achieving them, ranging from privatizing sufficient statistics, changing the posterior update rule, combining soruces of uncertainty through the framework of diffusions, and hiding generating distributions through a convex optimization task.
Interests are in machine learning, with a focus in privacy preserving techniques such as differential privacy. I am also interested in teaching and science communication. Currently, I am taking some personal time after completing my graduate degree. Outside of work, I enjoy video games and other such interactive fiction.