CV

Below is an abbreviated CV. A full PDF version is available above.

Contact Information

Name Xingyi (Daniel) Chen
Professional Title Computational Genomics Researcher
Email xchen274@jh.edu

Professional Summary

Undergraduate at Johns Hopkins University studying Applied Mathematics and Statistics, interested in statistical genomics, computational biology, and biomedical data science.

Experience

  • 2025 - present

    Baltimore, MD

    Research Assistant — Hicks Lab
    Johns Hopkins University, Department of Biostatistics
    PI: Dr. Stephanie Hicks
    • Designed isoform-level machine learning models for human brain aging using bulk RNA-seq and identified tissue-agnostic and hippocampus-specific transcript usage markers.
    • Developed reusable Visium HD preprocessing and visualization pipelines and contributed to a pip-installable visiumhd-utils package.
    • Ported SpotSweeper from R/Bioconductor to Python for spatially aware quality control in spatial omics workflows.
    • Benchmarked GeneCover against scRNA-seq gene panel selection methods for Xenium panel design.
  • 2026 - 2026

    New York, NY

    QSURE Intern — Shah Lab
    Memorial Sloan Kettering Cancer Center
    Mentors: Dr. Sohrab Shah, Dr. Andrew McPherson, Matt Myers
    • Research project on scDNA homozygous deletion analysis.
  • 2023 - 2025

    Baltimore, MD

    Research Assistant — Beer Lab
    Johns Hopkins University, Department of Biomedical Engineering
    PI: Dr. Michael Beer
    • Performed exploratory analysis and visualization for gene expression datasets using R.
    • Assisted with dataset extraction, cleaning, and preprocessing for downstream bioinformatics analyses.
  • 2024 - 2024

    Shanghai, China

    Bioinformatics Intern — Wei Lab
    Lin Gang Laboratory
    PI: Dr. Wu Wei
    • Synthesized spatial-omics literature into internal presentations focused on ML and statistical applications.
    • Supported bioinformatics projects through data cleaning and preliminary analysis.
  • 2024 - present

    Baltimore, MD

    Undergraduate Lead Teaching Assistant
    Johns Hopkins University, Department of Mathematics
    Supported courses: Differential Equations & Applications, Calculus III
    • Promoted from Course Assistant to Lead TA and led weekly discussion sections for approximately 25 students.
    • Delivered lectures using self-prepared notes and supported quiz, review session, and course material development.
    • Mentored undergraduate TAs and assisted with course logistics and student support.
    • Earned a 98% average student evaluation rating.

Education

  • 2023 - 2027

    Baltimore, MD

    Bachelor of Science
    Johns Hopkins University
    Applied Mathematics & Statistics
    • Computational Genomics: Data Analysis, Genomic Data Visualization, Real Analysis I/II, Monte Carlo Methods, Optimization, Systems Pharmacology
    • Minor in Computational Medicine, Mathematics
    • Dean’s List Recipient (6 semesters)

Publications

Skills

Programming: Python, R, SQL, C/C++, Java
Libraries & Tools: Scanpy, AnnData, pandas, numpy, scikit-learn, ggplot2, tidyverse, Git/GitHub, Unix, LaTeX
Research Areas: Statistical Genomics, Computational Biology, Machine Learning, Spatial Transcriptomics, scRNA-seq, Biomedical Data Science

Languages

Mandarin : Native
English : Bilingual
French : Elementary

Projects

  • Random Cell-Level Splits Introduce Systematic Bias in scRNA-seq Cell Type Annotation

    Benchmarked scRNA-seq cell type annotation under random cell-level and donor-held-out evaluation schemes using healthy PBMC data, demonstrating systematic performance inflation caused by donor-level leakage.

    • Compared HVG, PCA, Harmony, and scVI representations using logistic regression classifiers.
    • Evaluated macro F1, per-class F1, confusion matrices, and cross-site transfer performance.
    • Built a fully reproducible research package with modular Python workflows and archived Zenodo release.
  • BRCA-Mutant Breast Cancer scRNA-seq Analysis

    Led differential expression analysis for BRCA-mutant breast cancer single-cell RNA-seq data using custom hierarchical sample trees.

    • Applied dual statistical testing with FDR correction and fold-change analysis.
    • Generated publication-ready volcano plots and identified candidate driver genes.