People / Students / Class of 2026
Pinyi Li graduated from the University of California, Los Angeles, in June 2025 with dual bachelor’s degrees in Chemistry and Statistics & Data Science. During her undergraduate studies, Pinyi developed skills in Python and R through various academic projects, building expertise in data mining and modeling techniques. Building on this foundation, she combined her chemistry background with data science by applying machine learning to experimental optimization during a summer research project. There, she tested a range of bandit algorithms to identify optimal chemical reaction conditions. By incorporating a percent point function into the Bayesian UCB algorithm, she improved accuracy by 3% in identifying ideal ligands for C-H arylation reactions and by 6% for activator-base combinations in amide coupling reactions, showcasing her ability to integrate statistical modeling with scientific discovery.
Pinyi’s professional journey began as a data science intern at the Deloitte AI Institute, where she witnessed the transformative potential of artificial intelligence in workplace applications. At Deloitte, she evaluated AI model performance across general knowledge, logical reasoning, and translation tasks, identifying areas for improvement to support deployment strategies. She also tested AI plugins for office applications, assessing functionality, requirement alignment, and user experience to help refine tools for real-world adoption. Most recently, at a securities firm, Pinyi applied large language models (LLMs) to extract key trading elements—such as counterparties, bond codes, transaction sizes, and prices—from chat records. This information was used to build customer profiles and recommendation indices, enabling traders to identify high- and low-activity bonds and make more informed decisions. Additionally, she classified gold-related news using unsupervised learning and applied various feature selection techniques and machine learning models to forecast gold price trends, supporting portfolio optimization.
Pinyi looks forward to further developing her technical expertise and industry-ready skills through the MLDS program. She aims to leverage her interdisciplinary background to create impactful, human-centered solutions across diverse domains, including healthcare, finance, and technology. Ultimately, she aspires to contribute to innovative projects that not only advance data-driven insights but also deliver meaningful benefits to individuals and communities.
