Research

My research interests lie at the intersection of statistics, machine learning, and genomics. I am particularly interested in developing computational and statistical methods that enable researchers to extract meaningful biological insights from complex, high-dimensional genomic data.

As an undergraduate researcher at Arizona State University, I worked across several areas of bioinformatics and computational biology. In the Wang Lab, I developed and evaluated algorithmic approaches for a contraception decision aid designed for transgender and gender-nonconforming individuals assigned female at birth, leading to a first-authored publication presented at the American Medical Informatics Association (AMIA) Annual Symposium. In the Embedded Machine Intelligence Lab, I developed a machine learning framework for data balancing for metabolic syndrome prediction and interpretable risk assessment, resulting in a first-authored publication accepted to IEEE EMBC 2025. In the Phan Lab, I worked with Oxford Nanopore long-read sequencing data, benchmarking basecalling and demultiplexing workflows to improve the reliability of genomic analyses.

Through these experiences, I discovered that the questions that excite me most lie at the intersection of statistis and genomics. I am fascinated by how statistical models, machine learning algorithms, and computational frameworks can be developed to better understand biological systems and genomic variation. My passion for statistical methodology and fascination with genomic biology are the primary drivers for my specific research interest.

As a PhD student in Bioinformatics and Genomics at Penn State University, I hope to develop novel statistical and machine learning methods for genomic data analysis, with applications in human health and disease.

Research Interests

• Statistical Genomics
• Computational Genomics
• Machine Learning for Biological Data
• Bioinformatics Method Development
• Interpretable Artificial Intelligence