I am a first-year PhD student in the Department of Computer Science at Stanford University. My research interests are in optimization for machine learning.
Previously, I completed a master's in computer science at the University of British Columbia, where I was advised by Mark Schmidt.
I am supported by an NSF Graduate Research Fellowship and an NSERC Postgraduate Scholarship.
October 20: I ranked in the top 10% of NeurIPS reviewers this year, improving from top 50% last year. This was my second time reviewing for NeurIPS.
September 15: I successfully "defended" my master's thesis on first-order optimization under interpolation conditions. The thesis is available here.
September 10: I have relocated (virtually) to Stanford to begin my PhD!
June 29 - July 10: I had a lot of fun attending the virtual MLSS this year — many thanks to the organizers for their hard work! My virtual poster is public on YouTube.
June 15: New preprint is on arXiv! I had great fun helping out with the experiments for this work on implicit regularization and preconditioners in generalized linear models.
April 29: I will attend the (virtual) MLSS 2020 from 28 June to 10 July this summer.
September 25: I'm organizing an overview of the interplay between optimization and generalization for UBC MLRG this semester! It includes "sharp" local minima, implicit regularization, and interpolation.
September 4: I ranked in the top 50% of reviewers for NeurIPS 2019! This was my first time reviewing; I reviewed eight papers, including two emergency reviews.
September 4: Our work on the stochastic Armijo line-search has been accepted for a poster at NeurIPS 2019!
September 4: Our paper on low-rank Gaussian variational inference for Bayesian neural networks was accepted at NeurIPS 2018!
June 25 - 29: I was a teaching assistant for the approximate Bayesian inference tutorial at the 2018 Data Science Summer School at École Polytechnique. The tutorial resources are here.
January 21 - June 30: I joined Emtiyaz Khan as an intern at the RIKEN Center for Advanced Intelligence Project (AIP).