Install python3 using Homebrew.
I am a CRM Applied Math Lab postdoctoral fellow at Concordia University, supervised by Simone Brugiapaglia (Concordia) and Tim Hoheisel (McGill).
I completed my PhD in Applied Mathematics at the University of British Columbia, advised by Özgür Yilmaz and Yaniv Plan.
My research is in the area of compressed sensing, convex optimization and machine learning — comprising aspects of high-dimensional probability, applications of random matrix theory, convex analysis and geometric functional analysis.
Ph.D. Applied Mathematics, 2021
University of British Columbia
M.Sc. Applied Mathematics, 2014
University of Toronto
B.Sc. Mathematics & Statistics, 2013
McMaster University
A brief summary and some incomplete reflections from the 2018 BC Data Science workshop.
A kind of repository for some links to blogs and resources that I’ve found useful over the years.
A post describing the code for a matrix completion tutorial I wrote for the 2017 BC Data Science workshop
A tutorial on how to create, customize and host a website using Hugo and GitHub Pages.
A matrix completion mini-project that I designed for the 2017 BC Data Science Workshop
My take on the Smart Shores project from the bcdata workshop
My take on the video compression project from the bcdata workshop
Gradient descent concepts from Mark Schmidt’s 540 course at UBC (with code ported to Python).
On Multiscale Analysis and PDE Methods on Graphs in Image Processing
In Fall 2020, I was the lecture and lab instructor for the UBC MDS program’s DSCI 551: Descriptive Statistics and Probability for Data Science.
In 2017, 2018 and 2019, I was a teaching assistant for the UBC MDS program. UBC MDS courses for which I’ve been a TA include:
I was co-organizer and workshop TA for the 2017 BC Data Science workshop and 2018 BC Data Science Workshop.
Past TA duties include: ODEs, Vector Calculus, Calculus I and Mathematics for Biology.