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, tweet, and maintain several Deep Learning libraries written in Javascript (e.g. ConvNetJS, RecurrentJS, REINFORCEjs, t-sneJS). 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.
