I am a US Citizen born in California. I have always been fascinated by the fields of machine learning and artificial intelligence. As an undergraduate in Computer and Communications Engineering, I concentrated on the area of data mining while working toward a Minor in mathematics. In one of my projects, I utilized Recurrent Neural Networks and Generative Adversarial Networks for the purpose of music generation. As part of my senior year project, sponsored by Intel, I was tasked with defining the expectations and requirements from the future PC. Based on my findings, there is a change in the PC from its existing passive model, where explicit directions are needed to complete tasks, to an intuitive model, where the PC learns and adapts to a user's behavior through a developed sense of intuition through deep learning and natural language processing. Furthermore, I have applied and enhanced my knowledge in machine learning with practical experience through my 2018 summer internship at Stanford University. For three months, I worked on a “Novel Clamp-on Ultrasonic Flow Meter” in the Stanford Electrical Engineering department. I developed a Neural Network framework for the prediction of flow rates from ultrasonic data produced by a novel ultrasonic flow meter. TensorFlow and Keras were used to implement the Neural Network and manipulate the input data, from preprocessing to training. I am currently following up on the continual development of TensorFlow. I have had the pleasure to connect with the Google Develop Relations team for TensorFlow and I am fascinated with the work being done there, especially in the development of TensorFlow for Swift and the compiler immediate representation MLIR. It would be great to get a closer look at and maybe even take part in the development of these two technologies.