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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. […]

Universal Prediction of Cell-cycle Position Using Transfer Learning

Harvard TH Chan School of Public Health, FXB G13 677 Huntington Ave, Boston, MA, United States

HSPH Biostatistics and DFCI Data Science Colloquium Thursday, March 6, 2025 4:00pm Harvard TH Chan School of Public Health, FXB G13 Kasper Hansen, PhD Associate Professor, McKusick-Nathans Insitute of Genetic Medicine, Department of Biostatistics, Johns Hopkins University A significant barrier to progress in biomedical data science is the development of prediction models that work across contexts such […]

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 […]

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 […]

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 […]

Fréchet Regression of Random Objects on Vector Covariates and Its Applications for Single Cell RNA-seq Data Analysis

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

HSPH Biostatistics and DFCI Data Science Colloquium Thursday, April 3, 2025 4:00pm Harvard TH Chan School of Public Health, FXB G13 Hongzhe Li, PhD Perelman Professor of Biostatistics, Epidemiology and Informatics Director, Center for Statistics in Big Data Vice Chair for Research Integration, Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania Population-level single-cell RNA-seq […]

Modeling Multiscale Genome and Cellular Organization

HSPH Biostatistics and DFCI Data Science Seminar Tuesday April 15 at 4:00pm Dana-Farber Cancer Institute Center for Life Sciences Building, 11th Floor, Room 11081 Jian Ma, PhD Ray and Stephanie Lane Professor of Computational Biology Carnegie Mellon University   The intersection of Al/ML and biomedicine is entering a transformative era, with growing potential to impact […]

Complex Disease Modeling And Efficient Drug Discovery With Large Language Models

HSPH Biostatistics and DFCI Data Science Seminar Tuesday April 29 from 11:00-12:00pm Zoom only (Link to be posted shortly) Yu Li, PhD Assistant Professor, CSE The Chinese University of Hong Kong Large language models, which can integrate and process large amounts of data in biomedicine, have great potential in modeling complex diseases and discovering functional […]

Preference Inference for Language Models Debiased by Fisher Random Walk Models

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

HSPH Biostatistics & DFCI Data Science Colloquium Series September 11 at 4:00PM Harvard TH Chan School of Public Health, FXB-301 Junwei Lu, PhD Associate Professor of Biostatistics, Harvard TH Chan School of Public Health Human preference alignment has been shown to be effective in training the large language models (LMs). It allows the LLM to […]

Reproducible Research – Tools and a case study with NHANES

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

HSPH Biostatistics & DFCI Data Science Colloquium Series September 18, 2025 4:00 PM HSPH FXB-301 Robert Gentleman, PhD Principal Research Scientist Harvard T.H. Chan School of Public Health and Dana-Farber Cancer Institute I will discuss how new technologies and statistical methodologies can help enhance our ability to perform reproducible research. I will demonstrate how these […]

Navigate the Crossroad of Statistics, Generative AI and Genomic Health

HSPH Biostatistics & DFCI Data Science Colloquium Series Thursday October 2, 2025 4:00pm ET HSPH FXB-301 Xihong Lin, PhD, Department of Biostatistics and Department of Statistics, Harvard University Integrating statistics with generative Al provides unprecedent opportunities to empower statistical science and accelerate trustworthy scientific discovery by leveraging the potential of generative Al models alongside rigorous […]

Flexible Adaptive Procedures for Testing Multiple Treatments, Endpoints or Populations in Confirmatory Clinical Trials

HSPH Biostatistics & DFCI Data Science Colloquium Series Thursday October 9, 2025 4:00pm ET HSPH FXB-301 Cyrus Mehta, President and Co-Founder of Cytel, Inc, Adjunct Professor, Department of Biostatistics, Harvard TH Chan School of Public Health The statistical methodology for the classical two-arm group sequential design has advanced vastly over the past three decades to […]