Jonathan Siegel

Junior-level Computer Science major at San Diego State University and a first-generation student. Bilingual (English, Spanish) and programming proficient in Java, JavaScript, HTML, CSS, C++, and Python. Computer Science Foundation provides 1.5 years of customer service experience working with hundreds of individuals at the Registrar through Zoom, in-person, and phone, dealing with understanding inquiries, reviewing academic records, and troubleshooting other issues to assist faculty and staff.

 To apply an affine transformation to an image, use a 2x3 matrix (M) to map pixels from the input to new coordinates in the output. Affine transformations maintain parallel lines and include translation, rotation, reflection, and scaling.

This Python-based recipe generator revolutionizes the way we think about cooking by offering personalized and creative recipe suggestions. Tailored to individual tastes and pantry contents, this innovative tool crafts unique culinary creations from a user's specified preferences and the ingredients they have on hand. Whether you're looking to explore new flavors or simply make the most of your kitchen inventory, our generator ensures a customized cooking experience that inspires and delights.

In this project, we've developed a gesture recognition system that interprets Sign Language gestures. By employing a robust dataset containing hundreds of images and videos showcasing various Sign Language gestures, we harness the power of TensorFlow, a leading machine learning framework, to train our model. The system meticulously analyzes the gestures and accurately translates them into corresponding textual outputs displayed on the console. This innovative approach not only bridges communication gaps but also demonstrates the potential of machine learning in enhancing accessibility.