· automated ML experiment training pipeline using JavaScript and AWS; increased efficiency by 200-fold

· monitored and generated analytic visualizations for experiment results using Splunk and d3.js

· attended regular ML team meetings and discussed improvements for machine vision algorithms

· developed the back-end of a context-aware music recommendation system that can understand subjective queries and present personalized results

· collected, organized, and analyzed music data through the Spotify API and constructed queries from natural language inputs using Python, JSON, and SPARQL

· extracted and computationally analyzed genetic variants from large Genome-Wide Association Studies (GWAS) datasets to find their impact on disease heritability

· identified statistically significant variants that affect the gene expression of multiple sclerosis, visualized the collected data in heatmaps

· discussed relevant publications with lab members each week to modify evaluation methods and yield reliable results