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As a teaching fellow at Harvard, I apply my machine learning expertise and passion to two courses: CS109a, an introduction to data science, and CS207, a project-based course on software development for computational science. I support the instructors and students in these courses, facilitating learning and fostering creativity and problem-solving skills.

I hold two bachelor's degrees in electrical engineering and mathematics from Texas Christian University, graduating with honors. I also pursued a master's degree in data science at Harvard University, where I explored advanced topics in deep learning, Bayesian methods, and unsupervised learning. Additionally, during my time as a research associate at Carnegie Mellon University, I contributed to the field by publishing practical applications of machine learning to real-world challenges, such as tracking cortical spreading depressions in the brain. I am continually enthusiastic about acquiring new skills and knowledge and collaborating with others on data science projects. My ultimate aim is to leverage data science to make a positive impact on society and the environment.