My academic passion lies in pursuing a career in the biopharmaceutical industry, where I can apply statistical and computational methods to solve problems related to health, medicine, and human well-being. What I enjoy most about studying Biometry & Statistics and Information Science at Cornell is not only the rigorous foundation in data analysis and modeling, but also how these tools are constantly connected to real-world scientific challenges. One aspect that has genuinely surprised and inspired me is the breadth and quality of academic talks and seminars available on campus. Cornell regularly hosts researchers from leading institutions who present cutting-edge work at the intersection of statistics, machine learning, and applied sciences. For example, I attended a talk by Yihong Gu that explored how to identify stable, causal relationships in complex and changing data environments. This topic directly relates to challenges in biomedical research, where distinguishing true causal effects from spurious correlations is critical. In addition, events organized by departments such as mathematics and statistics expose me to emerging areas like advanced statistical theory and its applications in new scientific domains. These experiences expand my perspective beyond coursework and help me understand how theoretical tools can be translated into impactful innovations. Overall, what I value most is that Cornell creates an intellectually rich environment where I can continuously connect my academic training to meaningful, real-world applications in biomedicine.