Queens, New York, United States
• Researching Heterogeneous Boosting to compare diverse weak learners against traditional methods like XGBoost, developing experimental frameworks using Python, scikit-learn, and Seaborn for evaluation.
• Analyzing results to identify optimal weak learner combinations to improve the performance of ensemble models.