Automates UTD UREC shift selection using Selenium with rule-based filters and real-time Telegram alerts.

Built deep CNNs in PyTorch to classify CIFAR-10 images, achieving 90.2% accuracy using regularization and cosine LR scheduling.

Used XGBoost and Neural Networks to predict credit default risk. Enhanced features and interpretability with SHAP analysis.