I am a first-year Computer Science student at Michigan State University with a minor in business, driven by a deep curiosity about how technology can be used to solve real-world problems at scale. I’ve always been drawn to environments where analytical thinking meets collaboration, whether that’s in a research lab, a classroom, or on a tennis court. Through my engineering career, I hope to create solutions that are not only technically sound but also accessible and impactful—especially in areas like sustainability, data-driven decision-making, and inclusive technology. I see engineering as a way to bridge gaps: between innovation and usability, efficiency and ethics, and technology and people. My decision to pursue engineering was shaped largely by hands-on research experiences that showed me the power of applying theory to tangible problems. During my time as a research assistant in Michigan State University’s High School Scholars Program, I worked on analyzing solar panel layers to identify defects and improve durability. Building predictive models to optimize material properties made me realize how computational tools can directly influence sustainability and product performance. Earlier, at Oakland University, I was introduced to machine learning through work on deep neural networks that predicted sediment age. That experience, especially learning advanced techniques like feedforward neural networks, solidified my interest in computer science and engineering research. These opportunities transformed engineering from an abstract concept into a creative, problem-solving discipline where small technical improvements can lead to meaningful real-world outcomes. Outside of research, leadership and teaching have also influenced my path. As a varsity tennis captain and freelance tennis instructor, I learned how to break down complex skills into approachable steps—something that translates directly to how I think about engineering problems. Co-founding and co-leading a STEM-focused club reinforced my belief that access to technical education matters, and that engineers have a responsibility not just to build solutions, but to bring others along in the process. These experiences have shaped how I want to approach my career: with technical rigor, empathy, and a focus on long-term impact. One engineer I deeply admire is Grace Hopper. Beyond her groundbreaking contributions to computer programming, what stands out to me is her commitment to making technology more understandable and accessible. She believed that computers should adapt to people, not the other way around—a philosophy that strongly resonates with me. As someone who values both technical depth and communication, I aspire to follow that mindset by developing systems that are powerful yet intuitive, and by using my skills to open doors rather than create barriers. Together, my experiences in research, leadership, and teamwork have shaped my goal of becoming an engineer who uses technology thoughtfully and responsibly—to build solutions that last, empower others, and make a meaningful difference in the world.

When I programmed my first robot in sixth grade, I was not thinking about how computer science and artificial intelligence could change the world. I was just excited that I could make something follow commands—simple as they were. But that small spark of curiosity grew into something much greater. Now, I find myself fascinated by the way technology can solve problems, uncover patterns, and improve lives. My interests in computer science do not end at simply learning the theory behind code and algorithms, but to investigate the real-world applications and implications of new technologies. These questions motivate me to consider the broader impact of the systems we build. Working with Professor Vijitashwa Pandey at Oakland University on my first neural network opened my eyes to the impact of emerging technologies. Using machine learning to predict sediment age, I discovered how even small changes in a model could yield tremendous gains in efficiency and accuracy. I loved the process of troubleshooting and refining, but what really stayed with me was realizing how this technology could be applied to real-world challenges, like predicting disease outbreaks or improving educational tools. Outside of the lab, my primary goal is to become a more effective and confident public speaker. While I am comfortable navigating complex datasets and debugging code, I recognize that the most impactful ideas are often those that can be communicated clearly to a non-technical audience. I want to develop the ability to stand in front of a community board, a group of policymakers, or a classroom of students and articulate not just how a technology works, but why it matters. By mastering public speaking, I hope to bridge the gap between technical innovation and social implementation, ensuring that the tools I build are understood and utilized by the people they are intended to help. In the future, I plan to combine my technical expertise in machine learning with my commitment to advocacy and communication. I envision a career where I am not only developing the next generation of predictive models but also leading the conversations around their ethical use and deployment. Whether I am advocating for better resource allocation in healthcare or presenting data-driven climate solutions, my goal is to be a "multilingual" leader who can speak the language of both algorithms and human impact. By synthesizing these skills, I can ensure that the "Coding+" path I take is one that amplifies diverse voices and solves real-world problems through both innovative technology and persuasive storytelling.