Advisor: Xiaowei Xu• Exploring large language models (LLMs) and their applications, including prompt engineering techniques and multi-agent systems.

Mentor: Huixiao Hong• Developed a random forest machine learning model using Python and scikit-learn to predict human sex hormone-binding globulin (hSHBG) binding affinity with chemicals, achieving a model accuracy of over 90%.• Curated chemicals from literature and the ChEMBL database and computed their molecular descriptors.• Used pandas to preprocess binding affinity data and extract features from over 700 molecular descriptors.• Conducted cross-validated grid searches for hyperparameter tuning to optimize model performance.• Visualized model performance and optimization results using Matplotlib, enabling clear communication of results and insights to other research scientists.

• Delivered personalized, one-on-one tutoring sessions for high school students in algebra and geometry, through the grant-funded Count Up program.• Improved students’ critical thinking and problem-solving skills, contributing to significant improvements in their academic confidence and performance.