Harvard TH Chan School of Public Health

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Data Integration in Spatial and Single Cell Omics: What is Erased, and Can you Recover it?

Harvard TH Chan School of Public Health 677 Huntington Ave, Boston, MA

HSPH Biostatistics and DFCI Data Science Colloquium Thursday, March 27, 2025 4:00pm Harvard TH Chan School of Public Health, FXB G13 Nancy Zhang, PhD Ge Li and Ning Zhao Professor, Professor of Statistics and Data Science, Vice Dean of Wharton Doctoral Programs,  The Wharton School, University of Pennsylvania In single-cell and spatial biology, data integration refers […]

Dissecting Tumor Transcriptional Heterogeneity from Single-cell RNA-seq Data by Generalized Binary Covariance Decomposition

Harvard TH Chan School of Public Health 677 Huntington Ave, Boston, MA

HSPH Biostatistics and DFCI Data Science Colloquium Tuesday March 11th at 4:00pm HSPH FXB G12 Yusha Liu, PhD Research Assistant Professor Department of Biostatistics The University of North Carolina at Chapel Hill Profiling tumors with single-cell RNA sequencing has the potential to identify recurrent patterns of transcription variation related to cancer progression, and to produce […]

Decoding Aging at Spatial and Single-cell Resolution with Machine Learning

Harvard TH Chan School of Public Health 677 Huntington Ave, Boston, MA

HSPH Biostatistics and DFCI Data Science Colloquium Monday March 10th at 4:00pm HSPH Kresge G2 Eric Sun PhD Candidate, Department of Biomedical Informatics Stanford University Aging is a highly complex process and the greatest risk factor for many chronic diseases including cardiovascular disease, dementia, stroke, diabetes, and cancer. Recent spatial and single-cell omics technologies have […]

How Do Neural Networks Learn Features From Data?

Harvard TH Chan School of Public Health 677 Huntington Ave, Boston, MA

HSPH Biostatistics and DFCI Data Science Colloquium Monday March 3rd at 4:00pm HSPH Kresge G2 Adityanarayanan Radhakrishnan Eric and Wendy Schmidt Center Postdoctoral Fellow, Broad Institute of MIT and Harvard, Harvard School of Engineering and Applied Sciences Abstract: Understanding how neural networks learn features, or relevant patterns in data, is key to accelerating scientific discovery. […]