My interests lie in quantitative research, systematic trading, and the use of machine learning to understand market behavior. At Carnegie Mellon, I studied Statistics and Machine Learning, where I built a strong foundation in statistical modeling, programming, and data reasoning. Through my quantitative research capstone, I worked on monthly stock-return prediction using large-scale data with more than 160 features. I implemented XGBoost models, designed rolling validation methods, and identified signals that held up out of sample. This experience strengthened my interest in constructing models that are accurate, robust, and interpretable. Beyond finance, my research experience spans network analysis, natural language processing, and investigating large-scale behavioral data. These projects taught me to approach problems both rigorously and creatively. I bring curiosity, discipline, and a collaborative mindset to every project. I would be excited to connect virtually or in person to discuss how my analytical skills and research experience can contribute to your team. 听
