About me

Experienced statistician specializing in causal inference, statistical analysis, and machine learning, eager to mathematically elucidate the causality of natural phenomena.

🔬 Ph.D. candidate in Biostatistics at UNC-Chapel Hill, advised by Profs. Michael G. Hudgens and Donglin Zeng. Focusing on causal inference under network setting using machine learning to develop efficient and robust estimation based on semi-parametric efficiency theory. See my research statement.

💻 Proficient in data preprocessing, model training, evaluation, and inference using various statistical, machine learning & deep learning algorithms, with expertise in programming languages (R, Python, SQL, SAS, Bash).

🏆 Recognized with prestigious awards and scholarships for academic excellence and research achievements, including publications in the Journal of the American Statistical Association, multiple fellowships, and conference travel awards.

👨‍💼 Experienced in industry settings as a Machine Learning Engineering Intern at Apple, Research Statistics Intern at GlaxoSmithKline, and Graduate Research Assistant at UNC Chapel Hill.

Passionate about leveraging data-driven approaches to solve complex problems and drive impactful research outcomes. Open to new opportunities and collaborations in statistics, causal inference, and machine learning.