Developed Fabric's Rust frontend to facilitate the creation of Zero-Knowledge Proof (ZKP) protocols,

enhancing the range of data types available for ZKP graph construction.

Solely directed and executed the integration of the Plonky2 ZKP protocol into the Fabric front-end and graph building systems.

Streamlined and optimized the ZKP graph building codebase through a comprehensive refactoring effort.

Released Dev board micro in late 2022.

Developed embedded software for Coral products in Python and C++.

Implemented ASR ML networks utilized by embedded hardware.

Wrote documentation for embedded products and aided in the technical writing for the team.

Programmed security based software in Python, C++, and Rust.

Developed build systems in Rust, C++, and Makefile for large scale software.

Developed system for deployment of AWS Personalize to manage all packages and dependencies needed for a service to run the correct version of AWS Personalize based on server region using Java, AWS (CloudWatch, SageMaker, IAM, SWF, DDB), Ruby, and Python, optimizing team development time by 10+ hours weekly.

Monitored all customer related tickets, alarms, and services, to triage, and identify errors with systems 24/7 for 2+ week periods. Worked with customers directly and scaled out servers in multiple regions to support increased traffic needed for service to function for entire continental regions.

Wrote solutions to multiple customer issues using Java and AWS to fix recurring system errors which caused failures to 10+ customers daily.

Migrated 50% of AWS Personalize integration code base to new account and pipeline using java to refactor code base to allow for different integration test accounts for every test.

Configured 80% of AWS objects and code infrastructure (ruby, java, python) needed to migrate 50%+ of AWS Personalize to the new deployment service.

Leveraged deep learning (CNN structures such as VGG16 and HRnet) to research and improve technology in gait analysis and pose estimation for Cincinnati Children's Hospital Medical Center.

Developed software to produce gait classification system from more than 10,000 patient videos within sports medical center database utilizing Python and Keras.

Configured data pipelines for previous pose estimation software on Google servers, enabling ability to run state-of-the-art neural networks such as HRnet, Openpose, and Posenet.