What I Learned: Although I entered ENGR 102 with prior programming experience from robotics, the course helped me develop a more structured approach to engineering problem solving. Through Python programming assignments and engineering design projects, I learned how computational tools can be used to model systems, analyze data, and solve real-world engineering challenges. The course also introduced software design principles, debugging methodologies, and collaborative development practices that extended beyond simply writing code. . Why It Matters: ENGR 102 was my first exposure to how engineers formally approach complex problems. While robotics had shown me the practical side of engineering, this course connected those experiences to a broader engineering framework centered on design, analysis, and iterative improvement. It reinforced my interest in Computer Engineering by showing how programming can be used as a tool to solve challenges across many disciplines. . How I Applied It: The structured problem-solving techniques introduced in ENGR 102 became a foundation for many of my later projects. Whether developing robotics analytics software, designing research systems for environmental monitoring, or completing coursework in computer science and statistics, I frequently relied on the decomposition, debugging, and design processes emphasized in this course. ENGR 102 helped bridge the gap between my previous programming experience and the more rigorous engineering mindset required for larger technical projects. . Semester Completed: Fall 2024
Personal Statement
My interest in engineering began through competitive robotics, where I was first exposed to the challenge of designing systems that combine software, hardware, and data-driven decision making. As Captain and Lead Programmer of my high school robotics team, I learned that successful engineering requires more than technical knowledge, it requires leadership, communication, and the ability to solve complex problems under uncertainty.
Today, I am pursuing a Bachelor of Science in Computer Engineering at Texas A&M University with minors in Statistics and Mathematics. My academic interests lie at the intersection of software engineering, data science, machine learning, and intelligent systems. I am particularly interested in how large amounts of data can be transformed into meaningful insights that improve decision-making, whether in robotics, environmental research, or other real-world applications.
Throughout my undergraduate experience, I have sought opportunities that extend beyond the classroom. Through projects such as Vigil, a robotics analytics platform, and DSTT, a deep-sea turtle tracking and environmental sensing system, I have explored how software and data can be used to address practical challenges. These experiences have reinforced my interest in building technologies that combine rigorous engineering principles with measurable real-world impact.
My coursework in computer science, mathematics, statistics, and electrical engineering has provided a strong technical foundation, while leadership roles in organizations such as Triangle Fraternity, Aggie Data Science Club, and Fish Council have helped me develop professionally and personally. Each experience has contributed to a broader understanding of how technical expertise, collaboration, and continuous learning work together to create meaningful solutions.
As I continue my undergraduate education, my goal is to deepen my knowledge of software engineering, data science, and machine learning while gaining experience through research, internships, and collaborative projects. Ultimately, I hope to contribute to innovative technologies that solve complex problems and create lasting value for the communities they serve.