Based in San Francisco. firstname.lastname@example.org
Consultant, Small Startup, Summer 2021
I helped a small startup improve their ML model deployment process. Their process involved three change sets touching ~20 files to release an updated version of a search ranking model, and took at least 4 hours start-to-finish. I refactored their code and suggested and implemented a candidate-generation and reranking system. The result was that they could update their model with a 1-line change to a single file and only a few minutes of time, which allowed them to iterate and test and deploy changes much more quickly, as well as enabling contributions from data scientists who weren’t deeply familiar with the code base.
Solo developer, Multi-object tracking for water fleas from video, November-December 2020
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.
Software Engineer, Airbnb, 2018-2020
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.
Solo developer, Human-to-dog translator, Spring 2018
- style transfer for audio (human content + dog style)
- running a Tensorflow graph in the browser (compiled to WebAssembly)
- a novel audio foreground/background separation
Software Engineer, Cruise Automation, 2016-2017
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
Education: PhD in Mathematics, UPenn, 2014
Focus: differential geometry. Dissertation: The Hopf fibrations are characterized by being fiberwise homogeneous.