Current third-year B.S. student in the Department of Applied Mathematics & Statistics at Johns Hopkins University, with an additional major in Computer Science. At Hopkins, I work on statistical modeling and computer vision projects, advised by Dr. Mario Micheli and Dr. Anton Dahbura, centered on Bayesian optical flow estimation using Gaussian process regression, and robust object tracking using deep convolutional neural networks.

Previously, I conducted research with Johns Hopkins Medical Institute's Laboratory of Computational Intensive Care Medicine, where I worked on projects relating to unsupervised learning for phenotype discovery in critical care patients. I also worked with Dr. John Edison and Johns Hopkins Makerspace to develop wearable position-tracking IoT devices for sports analytics.

My website showcases several of my personal machine learning projects, including cardiac arrhythmia detection, stock price forecasting, and spam email detection.

Fall 2025: Statistical Learning Theory, Bayesian Statistics, Time Series Analysis, Matrix Analysis
Spring 2025: Deep Learning, Computer Vision, Mathematical Statistics, Stochastic Processes
Earlier: Probability, Optimization, Linear Algebra, Data Structures, etc