Project: Surgery Optimizer Application:1. Led a cross-functional team to develop and deploy AI models for phase segmentation and classification.2. Implemented Docker containers and ONNX models, optimizing operational efficiency by reducing processing time by 40%.3. Created an automated pipeline on Azure and used mlFlow for model monitoring and deployment, enhancing clinical efficiency for 150+ clinicians.Project: AI-DKD :1.Secured a patent for a novel ML and DL approach to detect Diabetic Kidney Diseaseusing ophthalmic and invasive data.2. Awarded the Zeiss patent award 2023 and published research in ARVO Journal and IJO.3. Developed a Random Forest model with an F1 score of 89% and was a finalist in the New Business Challenge 2023, pitching a market-ready solution.

1. Developed an efficient Python-based data pipeline to process 300 GB of ServiceNow data seamlessly.2. Executed data management and transformation, employing advanced statistical analysis, exploratory data analysis, and hypothesis testing to derive actionable insights and conclusions.