Personal Statement(**NOT FOR APPLICATION USE**)

My academic journey has been driven by a deep interest in applied mathematics, with a particular focus on probability theory and stochastic processes. Throughout my undergraduate studies at the University of Wisconsin-Madison, I have sought opportunities to apply mathematical techniques to solve real-world problems, particularly in areas of statistics, data science, and mathematical modeling.

One of the highlights of my research experience has been my role as a research assistant in a project investigating Gender and Racial/Ethnic Disparities of Early-onset Colorectal Cancer. In this project, I utilized my statistical and computational skills to analyze large datasets from SEER*Stat, performing detailed statistical analyses to uncover patterns in cancer prevalence across various demographics. This work reinforced my ability to work with complex, real-world data and to apply probabilistic reasoning to understand data distributions and trends. The use of tools like SAS for p-value analysis also highlighted the crucial role that statistical theory plays in driving meaningful conclusions in applied fields like healthcare.

In another significant project, my work on regression model design for human bodyfat prediction provided a solid foundation in statistical modeling and the exploration of key predictors. It was through this project that I developed a strong interest in the intersection of statistical learning and real-world applications, especially in designing robust predictive models. By performing F-tests and model selection processes, I gained insights into how stochastic processes and probability distributions could be utilized to refine predictions and provide valuable insights for practical applications.

My current project, a numerical study of Kakeya maximal inequalities, has exposed me to more theoretical aspects of applied mathematics, particularly through the study of conjectures in 2D and 3D geometric spaces. In this work, I have employed stochastic techniques and optimization algorithms to challenge existing conjectures, further fueling my interest in stochastic processes and probability theory. This research experience has strengthened my problem-solving skills, while also allowing me to delve deeper into mathematical theories.

Overall, my interest lies in the application of probability theory and stochastic processes to solve complex problems. I am particularly fascinated by how these concepts can be used in mathematical modeling, data analysis, and optimization across various fields, and I look forward to contributing to research that advances both theoretical understanding and practical applications of these powerful mathematical tools.