Optimized data throughput and latency of a filtering pipeline by optimizing filter ordering and eliminating redundancies in the logic

Reduced throughput, allowing pipeline transfer from multiple complex SQL queries on the cloud to a java business rule engine using Drools and Spring Boot

Created a test platform for MLEs to evaluate and mitigate risks of ML models pre-deployment

Developed features for producing synthetic data and verifying output, monitoring latency and bandwidth, and detecting model drift using PyTorch and TorchScript

Used React Native, Swift, and Objective C to migrate Sprinter Health’s android mobile app

to iOS, improving the scalability and reach of their platform. Optimized UI for iPad mini and wrote a React Native native module for bluetooth printers

Cleaned and concatenated clinical datasets into a ML parseable database. Created organized cloud accessible data with Python and pandas