2015 - 2017
I was a research scientist and a founding member at OpenAI
, where I worked on deep learning, computer vision, generative models and reinforcement learning.
2011 - 2015
My PhD was focused on convolutional/recurrent neural networks and their applications in computer vision, natural language processing and their intersection. My adviser was Fei-Fei Li
at the Stanford Vision Lab and I also had the pleasure to work with Daphne Koller
, Andrew Ng
, Sebastian Thrun
and Vladlen Koltun
along the way during the first year rotation program.
I designed and was the primary instructor for the first deep learning class Stanford - CS 231n: Convolutional Neural Networks for Visual Recognition
. The class became one of the largest at Stanford and has grown from 150 enrolled in 2015 to 330 students in 2016, and 750 students in 2017.
Along the way I squeezed in 3 awesome internships: at (a baby) Google Brain in 2011 working on learning-scale unsupervised learning from videos, then again in Google Research in 2013 working on large-scale supervised learning on YouTube videos, and finally at DeepMind in 2015 working on the deep reinforcement learning team.
2009 - 2011
MSc at the University of British Columbia where I worked with Michiel van de Panne
on learning controllers for physically-simulated figures. Think: agile robotics but in a physical simulation.
2005 - 2009
BSc at the University of Toronto with a double major in computer science and physics and a minor in math. This is where I first came in contact with deep learning, attending Geoff Hinton's
class and reading groups.