Post-Graduate Stories

Alex Sharp Explores “Statistical Foundations of Invariant Representation Learning, with Applications to Data-driven Compression and Algorithm Design”

Alex will develop novel deep learning methods based on notions of invariance and/or sufficiency for data compression and data-driven algorithm design (e.g., discrete optimization) and will develop specialized statistical theory for those settings.

Program: CANSSI Distinguished Postdoctoral Fellowship
Region: National
Date: 2023–2025

Project Focus Areas

Advances in deep learning methods have been driven largely by computation and engineering. Some of the most successful methods, however, incorporate notions of invariance and/or sufficiency. These properties are especially important for robustness to distribution shift and applications to causal inference. This project will establish statistical theory for such methods, elucidating the respective roles of statistical invariance and sufficiency in modern machine learning, including approximate versions learned from data.

In addition to the theoretical objectives of the research project, Alexander will collaborate on work to develop theoretically sound representation learning methods for data compression and data-driven algorithm design. Such methods are at the forefront of research into data-driven solutions to core computer science problems, and pose challenging methodological and theoretical questions. Successful development of new methods will require designing appropriate deep neural network models and optimization objectives, implementing them to be trained efficiently on massive data sets, and running reproducible large-scale computer experiments to assess empirical performance. Moreover, collaborating on the release of clean, open-source code will be part of the project.

Getting to Know Alex

Alex Sharp’s first research experience came at St. Thomas University, where he participated in a project to model the profitability of a set of natural gas royalty regimes to be considered by the province of New Brunswick. This experience was “a great introduction for me to the utility of mathematics and statistics in informing real-world decision-making.”

Later he completed his master’s and PhD degrees at the University of Waterloo under the supervision of Dr. Ryan Browne. His research during this time focused mostly on theorizing and implementing parametric functional mixture models and led to two publications. He also had the chance to take a leadership role in collaborations with junior graduate students, leading to an additional publication with another soon to be submitted.

Alexander notes that his interest in the CDPF program comes from his desire to complement the “individualistic” research skills he has developed during his doctoral program with an experience that emphasizes research as a “collaborative enterprise, where many specialists contribute their expertise toward the advancement of a common goal.”

As a recipient of the CANSSI Distinguished Postdoctoral Fellowship, Alex will work under the supervision of Benjamin Bloem-Reddy at the University of British Columbia and Chris. J. Maddison at the University of Toronto

I am eager to apply the research skills I have honed to a problem that truly requires a collaborative and interdisciplinary approach—a problem that cannot be solved solely by my individual expertise.