Some of my earliest memories are filled with music. In the corner of a softly lit living room, I would watch an elderly woman I loved sit quietly for hours, often unable to remember recent conversations or recognize people around her. Alzheimer’s disease had slowly taken pieces of her memory away. But every so often, ballet music would begin to play. And suddenly, something changed. Her posture straightened. Her hands moved instinctively through familiar positions. She could still remember the dances she had practiced decades earlier, as though the music had unlocked a hidden part of her mind that the disease could not erase. As a child, I did not understand how someone could forget names and faces, yet still remember movement so precisely. That contradiction stayed with me for years. Over time, it became the foundation of my interest in neuroscience, medicine, and artificial intelligence. Today, I am passionate about exploring how movement can reveal information about cognition, memory, and disease. Specifically, I am interested in how technology can help us better understand the human brain. I am currently pursuing studies in chemistry and computational science, with the goal of developing AI-driven tools for neurological and movement disorder assessment. In particular, I hope to work on novel video-based systems capable of detecting subtle changes in movement patterns that humans may overlook, enabling earlier diagnosis and more personalized treatment for conditions such as Alzheimer’s disease, Parkinson’s disease, and other neurodegenerative disorders. What excites me most about STEM is its ability to transform observations into actionable solutions. A simple question like why could someone remember a dance but not a conversation opened the door to neuroscience, machine learning, pharmacology, and human behavior. I realized that diseases are rarely isolated problems. Alzheimer’s is not just about memory loss; it affects identity, emotion, coordination, and independence. Addressing challenges like these requires collaboration across disciplines, which is why I am drawn to combining chemistry, AI, and biomedical research. At Harvard, I have pursued opportunities that allow me to bridge these fields. I conduct research at Harvard Medical School studying GPCR signaling and receptor pharmacology, where I investigate how structural changes in receptors alter cellular signaling pathways. Through this work, I have gained experience designing experiments, analyzing biological data, and thinking critically about how molecular mechanisms ultimately translate into patient outcomes. At the same time, I have become increasingly interested in computational approaches, particularly how AI can identify patterns in large and complex datasets. I believe the future of medicine will rely heavily on intelligent systems that augment human clinical judgment rather than replace it. Video-based movement analysis is especially promising because it offers a noninvasive, scalable way to monitor neurological health. Tiny changes in gait, balance, reaction timing, or motor coordination may reveal disease progression years before traditional symptoms become obvious. With advances in computer vision and machine learning, it is now possible to quantify these signals with unprecedented precision. I hope to contribute to this emerging field by helping develop tools that make neurological care more proactive, accessible, and data-driven. Beyond the technical aspects, I am motivated by the human impact of this work. Watching someone reconnect with memory through movement showed me that science is not only about solving problems on paper. It is about preserving dignity, independence, and quality of life.