Qiutong (Polly) Pu graduated from UC Berkeley with highest distinction, holding dual degrees in Economics and Data Science, with a minor in Statistics. Currently, she is enrolled in the MSCF program at Carnegie Mellon University, recognized as a Squarepoint Fellow and MSCF Distinguished Merit Scholar. During her Data Science Honors Thesis, Polly conducted individual research and successfully implemented the Hidden Markov-based Black-Litterman framework for optimizing portfolio allocations through in-depth quantitative analysis and machine learning. While interning at Monet Global Consulting Group, Polly assisted the team in achieving significant improvements in portfolio model adjustments and stock trend forecasting through advanced statistical techniques and machine learning algorithms while demonstrating exceptional communication and teamwork. With a keen interest in leveraging her analytical and technical background to contribute to the field of quantitative finance, Polly aspires to combine her passion for problem-solving with a meticulous approach to data-driven decision-making. Upon graduation, her goal is to pursue a career as a quantitative researcher.