About me

Experienced statistician specializing in causal inference, statistical analysis, and machine learning, eager to discover the mathematical formulations that elucidate the causality of phenomena in nature.

🔬 Currently pursuing a Ph.D. in Biostatistics from the University of North Carolina at Chapel Hill, advised by Profs. Michael G. Hudgens and Donglin Zeng. Focusing on causal inference under interference (network setting) using machine learning to develop efficient and robust estimators of causal quantities based on semi-parametric efficiency theory.

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

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

👨‍💼 Experienced in industry settings as a Machine Learning Engineering Intern at Apple, Research Statistics Intern at GlaxoSmithKline, 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 the fields of statistics, causal inference, and machine learning.