- Worked on Understanding team to improve traffic lights and emergency vehicle detection

- Implemented a system that detects whether objects are occluding traffic light bulb detections using semantic segmentation, visual detections, and LIDAR camera projections as data sources, which was pushed to production on the car.

- Designed and implemented an ML pipeline for flashing traffic light detection, including scripts for data extraction, HTML / JS labeling and visualization tools, and experiments with various machine learning methods (Bayesian modeling, HMM, RNN).

- Architected and built a full ML pipeline for an RNN detector for flashing emergency vehicles, improving over the original model in precision and recall. Utilized CNNs and LSTMs for the model.

- Integrated the model into the Cruise emergency lights package using Tensorflow C++ API

- Implemented and deployed a Node JS/Express website that tracks soldier vests on Google Maps

- Added a Java API (using websockets) to integrate with the existing training app

- Presented to CEO and President of division