I am a recent AI M.Sc. graduate from the computing department at Imperial College London. My research interests revolve around developing predictive models, most recently focusing on object detection models in medical imaging. I am passionate about leveraging the data science techniques that I have acquired through my experience and education to a variety of fields, including tech, healthcare, finance, and entertainment.I began my studies with a B.Sc. in Physics and B.A. in Egyptology at Yale University. I later joined the ATLAS collaboration at CERN to work on particle identification and analysis. There, I developed algorithms in C++ and Python to clean and process terabytes of collision data to identify particle candidates. This experience honed my skills in algorithm development, data analysis, and visualization, laying a strong foundation for my subsequent research in AI and machine learning at Imperial College London.At Imperial, I learned advanced AI techniques, tackling complex challenges in both theoretical and applied machine learning. My work included developing deep learning models such as Generative Adversarial Networks (GANs) and Autoencoders for tasks ranging from generative AI to anomaly detection. I also used transformer models for natural language processing (NLP) applications, where I worked on classifying nuanced aspects of language, such as detecting condescension in text. Additionally, my research in computer vision projects, which involved applying convolutional neural networks (CNNs) and other state-of-the-art techniques to extract meaningful visual features, has expanded my technical ability and fueled my passion for developing novel AI solutions with real-world impact.