I deserve this scholarship because I have repeatedly turned ambitious ideas into work that creates practical value for other people. At Vanderbilt, I study computer science while pursuing projects that force me to learn quickly and follow through. With my freshman roommate, I helped build an autonomous tractor: a system that connected sensing, control, and decision-making in the physical world. Building it taught me that real progress comes from testing honestly, rebuilding when assumptions fail, and staying with a problem until the result is dependable. Our work helped us raise funding for the venture, but its most important lesson was that students can build serious technology when they combine curiosity with discipline. That same mindset guides my work now. In a Recursive Agent Framework project, I am exploring how software agents can structure and improve their own problem-solving loops. I am drawn to AI because useful systems can help people make better decisions, coordinate difficult work, and attempt things that once seemed inaccessible. I want to build technology that is not merely impressive, but reliable and humane. I also try to make my work extend beyond my own goals. I co-founded Refrb, a nonprofit refurbishing electronics for organizations in more than ten countries. That experience reinforced my belief that technical skill matters most when it expands access and opportunity. My advantage is persistence backed by execution. I have experience moving from a vague idea to a tested prototype, collaborating under real constraints, and thinking seriously about who benefits from a system. This scholarship would help me keep turning that commitment into research and products that serve people well.

I became interested in computer science by building things that had to work outside a classroom. With my freshman roommate, I helped build an autonomous tractor. The project connected sensing, control, and decision-making in one machine, so every software assumption met reality quickly. A weak sensor reading or an unclear interface was not an abstract bug; it changed how the system behaved. I learned to test carefully, isolate variables, and rebuild when evidence contradicted our expectations. That process made computer science feel both creative and rigorous. The tractor project grew into the fastest autonomous tractor we could build and helped us raise funding for the venture. More important to me, it demonstrated that students can turn ambitious technical ideas into reliable systems. I loved watching an uncertain concept become a prototype that other people could understand, improve, and use. It is why I chose to study computer science at Vanderbilt. After earning my degree, I hope to build and research AI systems that make people more capable. In my Recursive Agent Framework project, I am exploring how software agents can organize and improve their own problem-solving loops. Long term, I want a career at the intersection of applied AI research and entrepreneurship: creating products that help people make better decisions, coordinate difficult work, and solve problems that once felt inaccessible. I would be a strong recipient of this scholarship because I bring both technical ambition and a record of following through. I have built hardware and software under real constraints, helped raise funding for a venture, and co-founded Refrb, a nonprofit refurbishing electronics for organizations in more than ten countries. I care about the practical responsibility behind technology: not merely whether a system can work, but how it can be dependable, humane, and broadly useful. This scholarship would help me keep turning that commitment into work that benefits others.

The project I have been most passionate about is an autonomous tractor that I built with my freshman roommate. We wanted to prove that ambitious robotics does not have to remain a classroom thought experiment. We began with a machine that had to operate in the physical world, where a bad sensor reading or an unclear software interface could become an immediate problem rather than a harmless error message. The work required us to connect sensing, control, and decision-making into one dependable system. We tested constantly, learned to isolate variables, and rebuilt parts of the design whenever evidence showed that our assumptions were wrong. I loved that every improvement had to be earned. A successful run was not magic; it was the result of careful debugging, honest measurement, and many small decisions made by a team that trusted each other. Our project became the fastest autonomous tractor we could build and helped us raise funding for the venture. But I am proudest of the effect it had on people who saw it. It turned an abstract claim—students can build serious autonomous systems—into something visible and real. The tractor showed me that computer science can create agency. Good software is not only elegant code; it helps people make better decisions, coordinate difficult work, and attempt things that once seemed inaccessible. That lesson guides my work at Vanderbilt. I am studying computer science because I want to build systems that expand what people can do. In a current Recursive Agent Framework project, I am exploring how software agents can structure and improve their own problem-solving loops. I find the most rewarding moments are the ones where a vague idea becomes a testable prototype, then a tool someone else can learn from or extend. Technology inspires me because it combines imagination with responsibility. A builder has to ask not just whether a system can work, but how it will fail, who it will affect, and how it can be made more useful. I want a career where I keep learning from real constraints, collaborate generously, and turn curiosity into products that help people solve meaningful problems. I also want to mentor other students, share methods openly, and make every technical project more reliable, humane, and useful for the people who depend on it. I hope to carry this discipline forward by building technology that is useful, accountable, and accessible to people beyond my immediate team.

I think of hacking as the habit of treating a constraint as an invitation to build a better path. The project that made that feel real to me began with a difficult question: how can a small team make an autonomous machine move quickly, safely, and reliably in a real field? My freshman roommate and I built an autonomous tractor. We had to connect sensors, control software, and a physical machine that could not be debugged by refreshing a browser tab. Every test forced us to make a hypothesis, collect evidence, and change one piece at a time. When a behavior was unstable, I learned to resist patching around it. Instead, we traced the problem back through the system, improved the interface between components, and tested again. The project eventually became the fastest autonomous tractor we could build, but the result I value most is what it taught our team. People around us were delighted because the machine made a far-fetched idea feel concrete: students could design something ambitious, take responsibility for its failures, and keep improving it until it worked. Raising funding for the venture was exciting, but it was even more meaningful to show that curiosity paired with disciplined experimentation can turn a sketch into a real system. That same spirit shapes how I approach computer science at Vanderbilt. I like projects where I can move from an abstract question to something another person can use. In my Recursive Agent Framework work, I have been exploring how software agents can organize and improve their own problem-solving loops. The interesting part is not just writing code; it is asking where a system will break, building a small test, and learning quickly from the answer. I try to share that process instead of guarding it. When someone is stuck, I enjoy helping them reduce a huge problem to the next testable step. Technology feels most worthwhile when it gives people more agency: a tool that helps a farmer, a teammate, or a student see that an intimidating problem is actually approachable. Kyle Lam's curiosity and generosity are a model for the kind of builder I want to be. I want to keep taking things apart carefully, ethically, and with purpose, then turn that tinkering into tools and teams that make other people feel capable of building too. I will keep earning that trust through careful work, useful experiments, and generous collaboration with every team I join.