Hello!/Guten Tag!/你好！

I am a Master of Statistical Science student at Duke University. I graduated from the University of Wisconsin–Madison in December 2024 with a B.S. in Mathematics and Statistics. My academic interests lie at the intersection of machine learning, deep learning, Bayesian statistics, and computational methodology.

I plan to pursue doctoral study in statistics, machine learning, computational statistics, and related quantitative fields. I am especially interested in research that connects statistical learning methods with probabilistic modeling, uncertainty quantification, and reproducible computation. I am also actively seeking TA/RA opportunities. You can contact me by email, and my Duke department page is available here.

Research Interests

• Machine learning and deep learning
• Bayesian statistical modeling and probabilistic machine learning
• Uncertainty quantification and interpretable statistical learning
• Statistical computing, optimization, and reproducible workflows
• Probability, combinatorics, and mathematical foundations of statistics

Selected Updates

• Spring 2026: Continuing graduate coursework in Bayesian modeling, causal inference, statistical computing, deep learning, and probabilistic machine learning at Duke.
• April 2025: Published a journal article in Cancer Control on colorectal cancer incidence trends among adults aged 45-49 in the United States.
• December 2024: Completed the B.S. in Mathematics and Statistics at the University of Wisconsin–Madison.
• 2024: Presented undergraduate research and directed reading projects through the UW–Madison Department of Mathematics.

TA/RA Preparation

My coursework has prepared me for teaching and research assistance in probability, mathematical statistics, regression, machine learning, deep learning, Bayesian modeling, causal inference, statistical computing, and data programming. I am comfortable working with R, Python, SQL, and reproducible analysis workflows, and I am particularly interested in research projects involving machine learning, deep learning, Bayesian statistics, probabilistic modeling, or computational statistics.