Developed tools in Python and MATLAB to run Machine Learning Classifiers, join data and produce reports, resulting in a ML algorithm with an accuracy of 97.48% that demonstrated the effectiveness of a new drug for patients with schizophrenia.

Revamped a monolithic codebase by Implementing OOP concepts to modularize existing code which automated processing data and allowed research team to hit a stringent deadline for submitting publications

Identified bottleneck by benchmarking machines researchers used and orchestrated the team to use a High Performance Computing Cluster, reducing time to results from 3 days to under 6 hours per report

Increased performance of models on average of 10% by running various combinations of classifiers