Based in San Francisco. email@example.com
Pro-bono work for a longevity researcher, counting and tracking water fleas from video of their tanks of water. I used classical computer vision, kalman filters, and multi-object tracking and association to estimate the number of fleas in a given video.
I worked on search ranking and then on the machine learning platform team. I built ML search ranking models, including building a training pipeline and model architecture for taking positional bias into account when training on historical click data. I built automated train-evaluate-deploy loops, built a system for requesting ad-hoc batch inference jobs, re-worked the online inference system which required quadratic CPU cores (in requests per second) to instead use only linear CPU cores (saving $X00k/year), and improved my team’s productivity by creating and socializing a versioning and release system.
A human to dog translator running entirely in the browser. It works by compiling a Tensorflow model to WebAssembly. I did novel work for this project, implementing:
Tech lead for the Prediction team. I developed technology for autonomous cars, to predict other vehicles, pedestrians, cyclists. I also worked on sensor fusion and multi-object tracking. C++ and Python, both machine learning and non-ML statistical and geometric algorithms.
C++, Python, Java, Tensorflow, Linux, macOS, bash, reverse engineering, computational geometry, airflow, kubernetes
Focus: differential geometry. Dissertation: The Hopf fibrations are characterized by being fiberwise homogeneous.