Learning objectives

• Recognize examples of low-dimensional geometric structure in single-cell and spatial omics data, including manifolds, trajectories, and tissue coordinates.
• Formulate biological questions about cell states, tissue organization, and developmental dynamics as geometric inference problems.
• Understand how manifold learning and geometric inference can improve embedding reliability and spatial gene analysis.
• Use vector-field geometry to analyze RNA velocity and infer dynamic cellular processes on cell-state manifolds.

Speaker Bio

Rong Ma is an Assistant Professor in the Department of Biostatistics at the Harvard T.H. Chan School of Public Health, with affiliated appointments at the Broad Institute, Dana-Farber Cancer Institute, and the Harvard Data Science Initiative. His research focuses on developing principled statistical and machine learning methods for high-dimensional biological data, with particular interests in geometric inference, manifold learning, representation learning, and single-cell genomics. He develops reliable and interpretable approaches for studying cellular dynamics, developmental trajectories, and complex biological systems from noisy high-dimensional data.