Aleena Baby

Designed and deployed scalable AI pipelines combining simulation and experimental data for casting defect prediction (R² >0.95). Built MLflow, Docker, and CI/CD model-serving pipelines. Applied Random Forest, XGBoost, and Deep Learning models to structured and unstructured industrial data. Reduced design iteration time by 80%. Presented at 4+ conferences and published research.

Graduate-level lectures on Artificial Intelligence and Machine Learning with industrial case studies (logistics, supply chain, manufacturing). Designed Python and AI/ML curriculum adopted by 2+ institutions. Guided 5 master's theses in applied AI/ML. 100% student retention rate.

Built early weather warning system for Tanzania (25% improved accuracy). Developed AI-driven air quality forecasting model using Python and Scikit-learn. Led ETL of waste management data with a cross-functional team of 80+ global members. Delivered data-driven optimization models for real-world environmental challenges.

Built early weather warning system for Tanzania (25% improved accuracy). Developed AI-driven air quality forecasting model using Python and Scikit-learn. Led ETL of waste management data with a cross-functional team of 80+ global members. 95% client approval rating.