Selected Work
Applied engineering projects spanning embedded systems, machine learning, modeling, and web tools.
Designed with a team of four undergraduate students, Drone Eye is a computer-vision and machine-learning drone detection system. The platform uses LiDAR for depth perception and a custom-trained ML model to detect drones and estimate Cartesian coordinates through a graphical interface.
Built an encrypted Tkinter application that helps users track insulin usage and calculate dosage amounts.
Developed a lower-cost pitch calling system inspired by PitchCom. The project uses custom hardware and firmware to communicate called pitches between players.
Created a web platform for modeling fire spread in level 1 materials server-side, making simulation more accessible without requiring local software installation.
Developed a web platform that lets multiple caregivers submit orders and coordinate blood sugar and insulin-related care, with an emphasis on dosing, tracking, and communication.