I'm a Master of Science in Business Analytics (MSBA) student at UCLA Anderson, passionate about blending data, product thinking, and storytelling to drive impact. I specialize in using analytics and machine learning to solve real-world business problems — from predicting player movements in the NFL Big Data Bowl to optimizing SQL schemas for scalable data systems.
Before UCLA, I worked on projects that spanned business intelligence, product strategy, and data visualization — building tools that turned complex data into actionable insights. My work bridges the gap between data science and product management: identifying the “why” behind data, designing solutions that scale, and communicating results clearly across teams.
Outside of academics, I’m a competitive volleyball player and a firm believer that teamwork in sports mirrors collaboration in analytics — precision, timing, and trust all matter. I’m currently exploring opportunities where I can apply data-driven decision-making to product development, strategy, or analytics innovation.
The NFL Big Data Bowl 2026 invited data scientists to predict football play outcomes before they occur using Next Gen Stats player-tracking data. The challenge required transforming high-frequency spatiotemporal coordinates into meaningful predictive features — from velocity and acceleration to team formation patterns.
Preprocessed 650K+ tracking records; calculated relative velocity vectors, angular momentum, and inter-player distances to encode player interactions per frame.
Implemented deep neural networks in PyTorch with GroupKFold cross-validation to prevent data leakage across plays and games; compared with gradient boosting baselines.
Optimized for Kaggle’s custom metric using GPU-accelerated training; visualized predicted trajectories and field-heatmaps to interpret play outcomes and decision boundaries.
If you'd like to collaborate, network, or just say hi — feel free to reach out!