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.

Wearable robotic exoskeletons have the potential to help people with mobility impairments walk more independently, but they require accurate real-time information about how a user’s joints are moving. Traditional motion capture systems and full-body inertial measurement unit arrays can be accurate, but they are often expensive, uncomfortable, and difficult to set up. In this project, I investigated whether a reduced sensor system using only thigh-mounted IMUs could predict hip motion with accuracy comparable to a full-body configuration. Using convolutional neural networks trained on gait data from both unassisted and exoskeleton-assisted walking, this study shows that sparse sensing can provide a practical path toward more comfortable and accessible rehabilitation robotics.

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.