. As of Summer 2016 I am a Research Scientist at OpenAI
working on Deep Learning, Generative Models and Reinforcement Learning. Previously I was a
Computer Science PhD student at Stanford, working with Fei-Fei Li
. My research centered around Deep Learning and its applications in Computer Vision, Natural Language Processing and their intersection. In particular, I was interested in fully end-to-end learning with Convolutional/Recurrent Neural Networks architectures and recent advances in Deep Reinforcement Learning. Over the course of my PhD I squeezed in two internships at Google where I worked on large-scale feature learning over YouTube videos, and last summer I interned at DeepMind and worked on Deep Reinforcement Learning and Generative Models. Together with Fei-Fei, I designed and taught a new Stanford undergraduate-level class on Convolutional Neural Networks for Visual Recognition (CS231n)
. The class was the first Deep Learning course offering at Stanford and has grown from 150 enrolled students last year to 330 students this year.
On a side for fun I blog
). I am also sometimes jokingly referred to as the
reference human for ImageNet (post
:)), and I create those nice-looking conference proceedings LDA visualization pages each year (NIPS 2015 example
). I also recently expanded on this with arxiv-sanity.com
, which lets you search and sort through 20,000+ Arxiv papers on Machine Learning over the last 3 years in the same pretty format.