At WPI, I developed machine learning pipelines to analyze large-scale MRI datasets for drug-resistant epilepsy, achieving 70% accuracy, 75% recall, and a 73% F1 score. I also built CNN-based facial recognition models in PyTorch that achieved over 90% image-classification accuracy.
In my current work at DaVita, I maintain and troubleshoot dialysis and water treatment systems while using Excel and Power BI to report service activity, visualize maintenance trends, and forecast regional inventory needs. I bring together bioinformatics, Python, R, SQL, machine learning, and data visualization to turn complex data into practical insights.
