Skin cancer is one of the most common forms of cancer worldwide, yet early detection can dramatically improve patient outcomes. Inspired by years of dermatology appointments related to eczema and skin abnormalities, I explored whether machine learning could accurately distinguish cancerous lesions from benign skin conditions. Using thousands of dermoscopic images from the International Skin Imaging Collaboration (ISIC) database, I developed and trained a neural network capable of identifying suspicious lesions. To make the technology more accessible, I also designed and built a low-cost handheld prototype that integrates image capture, machine learning, and embedded hardware into a practical screening device.

Harmful algal blooms can devastate marine ecosystems, contaminate fisheries, and threaten coastal economies. Because these events are driven by complex interactions among ocean currents, weather patterns, and nutrient transport, predicting their occurrence remains a major challenge. In this project, I developed a physics-guided machine learning framework to forecast harmful algal bloom conditions along the California coast. By combining satellite observations, oceanographic data, and physical transport equations, I investigated whether machine learning models could provide accurate forecasts while remaining consistent with the underlying physics governing bloom formation and movement.

Internet memes are often viewed as entertainment, but they can also shape political discussion and public opinion. In this project, I examined how political memes on Reddit responded to major political events between 2020 and 2025. Using topic modeling, time-series analysis, and machine learning techniques, I identified recurring themes in thousands of memes and analyzed how their frequency changed in response to real-world events. By studying humor as a form of political communication, this work explores how online communities frame policy issues and influence public engagement through satire and shared narratives.