Research interests

I research at the interface of the mathematics of machine learning and signal processing, using high-dimensional probability, optimization and machine learning to understand novel aspects of high-dimensional structured signal recovery, the mathematics of information and deep learning. 

I want provable guarantees with practical relevance for the design and analysis of methods for structured underdetermined linear inverse problems, like LASSO for compressed sensing or deep learning methods for computational sensing. I also want clever tools with practical applications to realistic signal processing tasks. 


Presently, I'm a Staff Research Scientist at Deep Render where I develop mathematical, statistical and deep learning tools to advance the world's first real-time fully AI video codec. Until July 2023 I was a CRM Applied Math Lab- and IVADO-funded postdoctoral researcher in the Department of Mathematics & Statistics at McGill University in Montréal, Canada with Drs. Tim Hoheisel (McGill) and Simone Brugiapaglia (Concordia). 

Ph.D. Applied Mathematics

Advised by 

Drs. Yaniv Plan and Özgür Yılmaz
University of British Columbia
Vancouver, British Columbia, Canada

M.Sc. Applied Mathematics

Supervised by
Dr. Adrian Nachman
University of Toronto
Toronto, Ontario, Canada

B.Sc. Mathematics & Statistics

McMaster University
Hamilton, Ontario, Canada

Publications & Preprints


For a non-exhaustive list of talks I have given, see my CV.