I am a Senior Lecturer in the School of Computer Science of the University of Sydney, in the Sydney Algorithms and Computing Theory (SACT) group. Prior to that, I was a postdoc first in the Stanford Theory Group, then at IBM Research Almaden. Even prior to that, I obtained my Ph.D. from the Computer Science department of Columbia University, where I was advised by Prof. Rocco Servedio. Long ago, in a distant land, I received a M.Sc. in Computer Science from the Parisian Master of Research in Computer Science, and an engineering degree from one of France's "Grand Schools," the École Centrale Paris.
My main areas of study are distribution testing (and, broadly speaking, property testing), learning theory, and, more generally, randomised algorithms and the theory of machine learning. One of my current focuses is on understanding the computational aspects of learning and statistical inference subject to various resource or information constraints. Another, not quite disjoint from the first, lies in reliable and rigorous approaches to data privacy, specifically differential privacy.
Prospective Ph.D. students: If you are an undergrad/masters student with a strong background in algorithms and/or discrete mathematics interested (broadly) in the theoretical aspects of learning, randomised algorithms, or privacy, and are keen on spending 3-4 years in one of the world's best places to live, you can contact me, including your CV and a short paragraph of introduction. Please check my publications for some of my recent work, or my recent survey.
I also regularly supervise research projects (over Summer and Winter) for undergraduate students, as part of the Engineering Vacation Research Internship Program, and Honours students (18cp). If you are interested in either, please get in touch!
Conference Committees
![Unified lower bounds for interactive high-dimensional estimation under information constraints [link]](http://bibbase.org/img/filetypes/link.svg "Paper")