About

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.

• Deep generative demixing: Error bounds for demixing subgaussian mixtures of Lipschitz signals

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• On the best choice of Lasso program given data parameters

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• Deep generative demixing: Recovering Lipschitz signals from noisy subgaussian mixtures

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• Sensitivity of ℓ₁ minimization to parameter choice

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• Parameter instability regimes in sparse proximal denoising programs

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• Enrichment map profiling of the cancer invasion front suggests regulation of colorectal cancer progression by the bone morphogenetic protein antagonist, gremlin-1

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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:

• DSCI 551: Descriptive Statistics and Probability for Data Science
• DSCI 542: Communication & Argumentation
• DSCI 523: Data Wrangling
• DSCI 552: Statistical Inference & Computation I
• DSCI 513: Databases and Data Retrieval
• DSCI 571: Supervised Learning I
• DSCI 573: Feature and Model Selection
• DSCI 563: Unsupervised Learning
• DSCI 572: Supervised Learning II
• DSCI 553: Statistical Inference and Computation II
• DSCI 574: Spatial & Temporal Models
• DSCI 554: Experimentation and Causal Inference
• DSCI 525: Web and Cloud Computing
• DSCI 575: Advanced Machine Learning

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.

• aberk@math.ubc.ca
• LSK 309, University of British Columbia, Vancouver BC