Prashant Upadhyay

Built a Machine Learning-based Crash Injury Prediction System by integrating Python predictive models into engineering workflows via FastAPI REST APIs, enabling faster decision cycles for crash CAE. Developed an internal CAE Model Development Portal (Python) for model tracking and OK/NG status prediction. Designed and published Power BI dashboards to monitor KPIs, track CAE milestones, and visualize engineering performance metrics. Delivered end-to-end ML pipelines including preprocessing, feature engineering, model training and evaluation using Pandas, NumPy, scikit-learn, and XGBoost on automotive crash simulation datasets. Validated/preprocessed finite element (FE) models by performing meshing and quality checks in HyperMesh to support crash simulation accuracy and compliance. Served as IT Coordinator for the CAE division, streamlining software/hardware operations and providing technical support to engineering teams.