Responsibilities & achievements

• Implemented Google Cloud Platform pipelines with Vertex AI. Acquiring data from BigQuery and deploying ML models on a Jenkins server. • Improved existing ML algorithms to reduce processing time requirements from 8 hours to 10 minutes and maintain an accuracy of over 97%. • Maintained legacy code as needed by refactoring and creating pertinent UnitTests to keep the code base to a high standard, resulting in over 10,000 lines of code being updated.

Responsibilities & achievements

• Created novel computer vision approaches to the identification of extremely similar items, leading to successfully classifying 90% of items. • Extracted, transformed, and loaded dozens of terabytes worth of unstructured data, allowing for the training of sophisticated deep-learning models. • Created an advanced Python implementation of pyTorch and opencv to aid with the identification of theft events in large supermarket stores, saving the client upwards of 3 billion USD annually.

Responsibilities & achievements

• Computed and processed over 1,000 human Magnetic Resonance Images to ensure their quality and relevance. • Developed, maintained, and analyzed a pre-clinical substance abuse disorder model that generated over 100 thousand data points. • Created analysis pipelines based on nilearn, a python-based tool for fMRI.

Responsibilities & achievements

• Optimized an RNA extraction technique, resulting in a 32% increase in extracted RNA quality and quantity. • Deployed a keypoint model based on pyTorch to analyze animal behavior in low-light conditions, saving the laboratory over 15,000 dollars in licensing fees. • Created models based on Tensorflow and pyTorch, with support of opencv, numpy, and pandas.