About me

I am a first-year MS student in Biostatistics at the University of Minnesota, with a strong interest in E-health and disease modeling. My research focuses on applying machine learning and statistical methods to health data, aiming to improve disease prediction and public health outcomes.

I currently work as a Research Assistant (RA) in two labs:

• Dr. Feng Xie’s Lab – Developing emergency sepsis prediction models using the MIMIC-IV-ED and UMN datasets, leveraging AutoScore method to enhance predictive accuracy.
• Dr. Xiao Zang’s Lab – Conducting a descriptive study on drug overdose deaths in older Black populations using CDC data, applying statistical methods and data visualization techniques.

Previously, I earned a Bachelor of Science in Nursing from Peking University in 2022. My research journey began in my third year, focusing on chronic disease management and the application of big data in healthcare. To deepen my technical expertise, I joined an AI research group at COCHE Hong Kong, where I worked on cuff-less blood pressure estimation using machine learning models (LSTM, Transformer).

I am proficient in Python and R, with expertise in machine learning, deep learning, and data visualization.

My long-term goal is to pursue a PhD and further explore the intersection of AI, health data science, and disease modeling. I am passionate about leveraging advanced analytical techniques to drive meaningful improvements in public health.