Rong Ma

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Biography

My research interest lies in the intersection of statistics and biomedical data science. My current research focuses on (i) statistical inference for large random matrices and high-dimensional models, (ii) theoretical and computational underpinning of modern nonlinear embedding techniques and manifold learning algorithms, and (iii) developing principled and interpretable machine learning methods for biomedical sciences, especially for single-cell integrative genomics and multiomics.

Education and Training

• MS, Statistics & Data Science
  University of Wisconsin, Madison
• BS, Statistics
  Nankai University
• PhD, Biostatistics
  University of Pennsylvania
• Postdoc, Statistics & Biomedical Data Science
  Stanford University

Awards and Honors

• SIAM Travel Award, 2023
  Society for Industrial and Applied Mathematics
• Saul Winegrad Award for Outstanding Dissertation, 2022
  University of Pennsylvania
• Lawrence D. Brown Ph.D. Student Award, 2022
  Institute of Mathematical Statistics

Publications

• Principled and interpretable alignability testing and integration of single-cell data.

  Ma R, Sun ED, Donoho D, Zou J. Proc Natl Acad Sci U S A 2024 Mar 05

• TISSUE: uncertainty-calibrated prediction of single-cell spatial transcriptomics improves downstream analyses.

  Sun ED, Ma R, Navarro Negredo P, Brunet A, Zou J. Nat Methods 2024

• Using interpretable machine learning to extend heterogeneous antibody-virus datasets.

  Einav T, Ma R. Cell Rep Methods 2023 08 28

• The genetic and evolutionary basis of gene expression variation in East Africans.

  Kelly DE, Ramdas S, Ma R, Rawlings-Goss RA, Grant GR, Ranciaro A, Hirbo JB, Beggs W, Yeager M, Chanock S, Nyambo TB, Omar SA, Woldemeskel D, Belay G, Li H, Brown CD, Tishkoff SA. Genome Biol 2023 02 24

• A spectral method for assessing and combining multiple data visualizations.

  Ma R, Sun ED, Zou J. Nat Commun 2023 Feb 11