University of California, Santa Barbara
Hello! I'm Tej, a student and aspiring AI Engineer pursuing my B.S. in Statistics and Data Science at UC Santa Barbara. I am particulary interested in machine learning, deep learning, and agent-based AI tools.
My path into AI started with curiosity about how data reveals patterns and drives decisions. I’ve worked on predictive models, chart summarization tools, and care pathway systems using technologies like GPT, scikit-learn, and TensorFlow to create tools that support medical outcomes.
When I’m not building or learning, I enjoy cooking for my family, friends, and myself, working out, and spending quality time with my loved ones. I’m excited about AI's potential to drive change, especially where it can improve health, equity, and access through smart, data-driven tools.
Developed a Convolutional Neural Network model using TensorFlow and Keras to classify brain tumors (glioma, meningioma, pituitary tumor, no tumor) with 95% accuracy on test data, leveraging a dataset of 7,000+ MRI scans. Implemented advanced data preprocessing and augmentation techniques to improve model performance and ensure robust predictions.
Developed a Random Forest classification model in Python to simulate and predict 2025 NBA Finals outcomes, achieving 62% test accuracy using team-level regular season and playoff metrics. Engineered a Monte Carlo simulation to estimate championship probabilities over a best-of-seven series, predicting a 75.5% chance of the Oklahoma City Thunder winning against the Indiana Pacers.