I’m VP of Applied Deep Learning Research at NVIDIA, where we solve interesting problems from video games to chip design using deep learning. Prior to this role, I worked at Baidu with Adam Coates and Andrew Ng to create next generation systems for training and deploying deep learning. Before that, I was a researcher at NVIDIA, where I worked on programming models for parallel processors, as well as libraries for deep learning, which culminated in the creation of CUDNN.

I earned my PhD from Berkeley, under the direction of Kurt Keutzer, where I built the Copperhead language and compiler, which allows Python programmers to use nested data parallel abstractions and gain high efficiency on contemporary parallel platforms. I earned my MS and BS from Brigham Young University, where I worked with Brent Nelson on higher radix floating-point representations for FPGAs.