I am a PhD candidate in Applied Mathematics at the University of British Columbia being 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.
During October 2016 – February 2017 I completed an internship with Awake Labs, where I worked as a data scientist developing online algorithms for performing learning tasks on structured high-dimensional physiological data.
I completed my MSc Mathematics in August 2014 at the University of Toronto. My supervisor was Dr. Adrian Nachman, under whom I researched fast computational methods in image processing with applications in medical imaging. Before that, I completed my BSc in Mathematics & Statistics at McMaster University in Hamilton, ON.
PhD Candidate, Applied Mathematics
University of British Columbia
MSc in Applied Mathematics, 2014
University of Toronto
BSc in 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 brief summary and some incomplete reflections from my time as the workshop TA for the 2017 BC Data Science workshop.
A tutorial on how to create, customize and host a website using Hugo and GitHub Pages.
A mini-project I designed for the 2017 BC Data Science Workshop
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).
A proof of Calderón’s convolutional reproducing formula for functions in L^2 with the Haar measure.
On Multiscale Analysis and PDE Methods on Graphs in Image Processing
My 5 minute presentation for the 2017 UBC IAM retreat.
I was a teaching assistant for the 2017 and 2018 UBC MDS programs. In 2017: Descriptive Statistics and Probability, Data Wrangling, Supervised Learning I, Feature and Model Selection, Statistical Inference and Computation II and Experimentation and Causal Inference. In 2018: Communication & Argumentation, Data Wrangling, Databases and Data Retrieval, Unsupervised Learning, Spatial & Temporal Models, Web and Cloud Computing.
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.