Xingyi (Daniel) Chen

I am Xingyi (Daniel) Chen, a Senior at Johns Hopkins University studying Applied Mathematics & Statistics, with minors in Computational Medicine and Mathematics.

My research interests lie in biostatistics, statistical genomics, and biomedical data science. I am especially interested in developing and evaluating statistical and machine-learning methods for high-dimensional genomic and biomedical data, with applications in single-cell genomics, transcriptomics, and spatial omics.

I am currently a research assistant in the Hicks Lab at the Johns Hopkins Department of Biostatistics, where I work on statistical methodology, computational genomics, and reproducible workflows. My recent work includes methods for identifying age-dependent isoform trajectories, spatially aware quality control for spatial omics, and reproducible analysis workflows for Visium HD data.

I also lead an independent research project with Dr. Alexis Battle on donor-aware evaluation of single-cell RNA-seq cell-type annotation, studying when random cell-level evaluation can overestimate cross-donor generalization.

In Summer 2026, I was a QSURE intern at Memorial Sloan Kettering Cancer Center in the Shah/McPherson Lab, where I developed statistical and computational methods for detecting homozygous deletions in low-coverage single-cell DNA sequencing data.

I also serve as an Undergraduate Lead Teaching Assistant for Differential Equations & Applications course in Department of Mathematics at Hopkins.

I plan to pursue PhD training in biostatistics, statistical genomics, or biomedical data science, with a focus on developing rigorous statistical methodology for genomic and biomedical applications.

Outside of research, I enjoy traveling, exploring new cafes, restaurants, and cities, and spending time with friends.

You can find my CV, Email, GitHub, Google Scholar, LinkedIn, and other links below.