about me

I'm a Machine Learning and Vision Scientist at Apple, working at the intersection of graphics, image/video coding, and human & computer vision.

I have a PhD in Neuroscience from NYU, where I was advised by Eero Simoncelli and David Heeger. My dissertation focuses on theories of adaptive efficient coding in neural networks, and representational geometry. Formerly, I was a PhD intern on the Open Codecs team at Google where I worked on adaptive ML models for video compression.

I was born and raised in Yellowknife, Northwest Territories, Canada 🥶🍁. My BSc is in Physiology and Physics from McGill University, where I began working on what would eventually become my MSc at the University of Western Ontario (our lab migrated), modeling neural correlates of visual attention and memory, advised by Julio Martinez-Trujillo.

recent notes

Normalizing Flows and RealNVP

5 minute read

Simple implenetation of the RealNVP invertible neural network.

Batch and instance whitening

6 minute read

This short post will cover graphical intuition and PyTorch code for two different kinds of whitening: batch and instance.

Orthogonal iteration

5 minute read

A tutorial for a classic numerical linear algebra algorithm to find eigenvectors and values.

Elliptical slice sampling

6 minute read

A Python implementation of the elegant algorithm introduced by Iain Murray et al. (2010).

Interpolating covariances with their geometric mean

2 minute read

What’s the best way to quantify and visualize distance between two positive definite matrices? Julia code included.

Stochastic gradient Markov chain Monte Carlo

5 minute read

PyTorch implementation and explanation of SGD MCMC sampling w/ Langevin or Hamiltonian dynamics.