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BEGIN:VEVENT
DTSTART;TZID=America/New_York:20240830T080000
DTEND;TZID=America/New_York:20240830T170000
DTSTAMP:20260407T210809
CREATED:20240729T122857Z
LAST-MODIFIED:20240904T112937Z
UID:5455-1725004800-1725037200@ds.dfci.harvard.edu
SUMMARY:Fall schedule to be announced
DESCRIPTION:Watch here for updates on our fall seminar schedule. You can also sign up for our weekly newsletter to stay informed of data science events in the Boston area.
URL:https://ds.dfci.harvard.edu/event/fall-schedule-to-be-announced/
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/svg+xml:https://ds.dfci.harvard.edu/wp-content/uploads/2021/02/dfds_logo.svg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20240912T160000
DTEND;TZID=America/New_York:20240912T170000
DTSTAMP:20260407T210809
CREATED:20240904T112923Z
LAST-MODIFIED:20240917T172028Z
UID:5503-1726156800-1726160400@ds.dfci.harvard.edu
SUMMARY:Hierarchical Causal Models
DESCRIPTION:HSPH Biostatistics and DFCI Data Science Colloquium Series \nThursday September 12\, 2024\n4:00-5:00PM\nHSPH FXB Building Room 301 \nDavid Blei\, PhD\nProfessor of Statistics and Computer Science\nColumbia University \nAnalyzing nested data with hierarchical models is a staple of Bayesian statistics\, but causal modeling remains largely focused on “flat” models. In this talk\, we will explore how to think about nested data in causal models\, and we will consider the advantages of nested data over aggregate data (such as data means) for causal inference. We show that disaggregating your data—replacing a flat causal model with a hierarchical causal model—can provide new opportunities for identification and estimation. \nAs examples\, we will study how to identify and estimate causal effects under unmeasured confounders\, interference\, and instruments. \nThis is joint work with Eli Weinstein.
URL:https://ds.dfci.harvard.edu/event/hierarchical-causal-models/
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/jpeg:https://ds.dfci.harvard.edu/wp-content/uploads/2024/09/blei_headshot_crop.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20240919T160000
DTEND;TZID=America/New_York:20240919T170000
DTSTAMP:20260407T210809
CREATED:20240917T172017Z
LAST-MODIFIED:20240920T175443Z
UID:5541-1726761600-1726765200@ds.dfci.harvard.edu
SUMMARY:Inference for Treatment-Specific Survival Curves using Machine Learning
DESCRIPTION:HSPH Biostatistics and DFCI Data Science Colloquium Series \nThursday September 19\, 2024\n4:00-5:00PM\nHSPH FXB Building Room 313 \nTed Westling\, Assistant Professor\, Department of Mathematics & Statistics\, University of Massachusetts Amherst \nIn the absence of data from a randomized trial\, researchers often aim to use observational data to draw causal inference about the effect of a treatment on a time-to-event outcome. In this context\, interest often focuses on the treatment-specific survival curves; that is\, the survival curves were the entire population under study to be assigned to receive the treatment or not. Under certain causal conditions\, including that all confounders of the treatment-outcome relationship are observed\, the treatment-specific survival can be identified with a covariate-adjusted survival function. Several estimators of this function have been proposed\, including estimators based on outcome regression\, inverse probability weighting\, and doubly robust estimators. We propose a cross-fitted doubly-robust estimator that incorporates data-adaptive estimators of the conditional survival functions. We establish conditions on the nuisance estimators under which our estimator is consistent and asymptotically linear\, both pointwise and uniformly in time. We also propose an ensemble learner for combining multiple candidate estimators of the conditional survival estimators. Our methods and results accommodate events occurring in discrete or continuous time (or both). We investigate the practical performance of our methods using an application to the effect of a surgical treatment to prevent metastases of parotid carcinoma on mortality. Time permitting\, we will discuss ongoing work concerning sensitivity analysis for survival curves.
URL:https://ds.dfci.harvard.edu/event/inference-for-treatment-specific-survival-curves-using-machine-learning/
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/png:https://ds.dfci.harvard.edu/wp-content/uploads/2024/09/Ted-Westling-long.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20241018T150000
DTEND;TZID=America/New_York:20241018T160000
DTSTAMP:20260407T210809
CREATED:20241008T113529Z
LAST-MODIFIED:20241022T164100Z
UID:5585-1729263600-1729267200@ds.dfci.harvard.edu
SUMMARY:Empirical Bayes Matrix Factorization\, and Genomic Applications
DESCRIPTION:Data Science Seminar\nFriday\, October 18\, 3:00 PM ET \nCenter for Life Sciences Building\, 11th Floor \nMatthew Stephens\, PhD\nChair\, Department of Statistics; Ralph W. Gerard Professor of Statistics\, Human Genetics\, University of Chicago
URL:https://ds.dfci.harvard.edu/event/empirical-bayes-matrix-factorization-and-genomic-applications/
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/jpeg:https://ds.dfci.harvard.edu/wp-content/uploads/2024/10/Stephens_Matthew_600x600-300x300-1.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20241023T120000
DTEND;TZID=America/New_York:20241023T130000
DTSTAMP:20260407T210809
CREATED:20241011T180746Z
LAST-MODIFIED:20241023T172801Z
UID:5608-1729684800-1729688400@ds.dfci.harvard.edu
SUMMARY:Targeting CARM1 in Dendritic Cells for Cancer Immunotherapy
DESCRIPTION:Compbio Connections\n\nOctober 23rd\, 12:00 -1:00 PM ET\nCenter for Life Sciences Building\, 11th floor\, Zelen Commons \nXixi Zhang\, PhD\nResearch Fellow\, Dana-Farber Cancer Institute \nLunch is provided.
URL:https://ds.dfci.harvard.edu/event/targeting-carm1-in-dendritic-cells-for-cancer-immunotherapy/
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/jpeg:https://ds.dfci.harvard.edu/wp-content/uploads/2024/10/Zhang_Xixi_headshot.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20241106T120000
DTEND;TZID=America/New_York:20241106T130000
DTSTAMP:20260407T210809
CREATED:20241029T141932Z
LAST-MODIFIED:20241029T141932Z
UID:5644-1730894400-1730898000@ds.dfci.harvard.edu
SUMMARY:Harnessing Bioinformatics for Cancer Research at DFCI
DESCRIPTION:Compbio Connections\n\nNovember 6\, 12:00 -1:00 PM ET\nCenter for Life Sciences Building\, 11th floor\, Zelen Commons \n\n\n\n\nMichael Tolstorukov\nDirector\, Bioinformatics and Molecular Data\, Informatics and Analytics\, Dana-Farber Cancer Institute \n\n\n\n\nLunch is provided.
URL:https://ds.dfci.harvard.edu/event/harnessing-bioinformatics-for-cancer-research-at-dfci/
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/jpeg:https://ds.dfci.harvard.edu/wp-content/uploads/2024/10/T_Michael_headshot-e1730211557553.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20241107T100000
DTEND;TZID=America/New_York:20241107T120000
DTSTAMP:20260407T210809
CREATED:20241023T164140Z
LAST-MODIFIED:20241023T164140Z
UID:5634-1730973600-1730980800@ds.dfci.harvard.edu
SUMMARY:Ligand Receptor Analysis in a Spatial Context
DESCRIPTION:The Harvard Chan Bioinformatics Core in partnership with The Cancer Data Sciences Program at DF/HCC are very excited to present the Fall seminar series: \nNovel Applications in Single Cell Omics \n\n\n\nLigand receptor analysis in a spatial context \nNovember 7th\nSizun Jiang\, PhD\nBeth Israel Deaconess Medical Center Harvard Medical School \nMartin Hemberg\, PhD and Jingyi Cao\, PhD Brigham and Women’s Hospital\nHarvard Medical School \nTrajectory inference in single cell data\nNovember 21st\nLuca Pinello\, PhD and Cameron Ray Smith\, MD\, PhD\nMassachusetts General Hospital \nSpatial transcriptomics deconvolution analysis\nDecember 5th\nKevin Wei\, MD\, PhD Brigham and Women’s Hospital\nRuben Dries\, PhD and George Chen Boston University School of Medicine \nAll seminar sessions are hybrid events being held from 10am to noon.\nLocation: CLSB Building\, 3 Blackfan Circle\, Boston MA 02115 \n\n\n\n\nRegister now at: https://bit.ly/fall2024_speaker_series
URL:https://ds.dfci.harvard.edu/event/ligand-receptor-analysis-in-a-spatial-context/
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/png:https://ds.dfci.harvard.edu/wp-content/uploads/2024/10/HBCFall24.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20241120T120000
DTEND;TZID=America/New_York:20241120T130000
DTSTAMP:20260407T210809
CREATED:20241108T140605Z
LAST-MODIFIED:20241120T192210Z
UID:5679-1732104000-1732107600@ds.dfci.harvard.edu
SUMMARY:Niche-Driven Phenotypic Plasticity And Cis-Regulatory Dynamics Of A Revised Model For Intestinal Epithelial Differentiation
DESCRIPTION:CompBio Connections \nNovember 20th at 12pm\nCenter for Life Sciences Building\, 11th floor \nSwarnabh Bhattacharya\nResearch Fellow Dana-Farber Cancer Institute\n\nLunch is provided.
URL:https://ds.dfci.harvard.edu/event/niche-driven-phenotypic-plasticity-and-cis-regulatory-dynamics-of-a-revised-model-for-intestinal-epithelial-differentiation/
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/png:https://ds.dfci.harvard.edu/wp-content/uploads/2024/11/Zico_headshot-e1731074579602.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20241121T100000
DTEND;TZID=America/New_York:20241121T120000
DTSTAMP:20260407T210809
CREATED:20241029T142503Z
LAST-MODIFIED:20241029T142503Z
UID:5650-1732183200-1732190400@ds.dfci.harvard.edu
SUMMARY:Trajectory Inference in Single Cell Data
DESCRIPTION:The Harvard Chan Bioinformatics Core in partnership with The Cancer Data Sciences Program at DF/HCC are very excited to present the Fall seminar series: \nNovel Applications in Single Cell Omics \n\n\n\nLigand receptor analysis in a spatial context\nNovember 7th\nSizun Jiang\, PhD\nBeth Israel Deaconess Medical Center Harvard Medical School \nMartin Hemberg\, PhD and Jingyi Cao\, PhD Brigham and Women’s Hospital\nHarvard Medical School \nTrajectory inference in single cell data\nNovember 21st\nLuca Pinello\, PhD and Cameron Ray Smith\, MD\, PhD\nMassachusetts General Hospital \nSpatial transcriptomics deconvolution analysis\nDecember 5th\nKevin Wei\, MD\, PhD Brigham and Women’s Hospital\nRuben Dries\, PhD and George Chen Boston University School of Medicine \nAll seminar sessions are hybrid events being held from 10am to noon.\nLocation: CLSB Building\, 3 Blackfan Circle\, Boston MA 02115 \n\n\n\n\nRegister now at: https://bit.ly/fall2024_speaker_series
URL:https://ds.dfci.harvard.edu/event/trajectory-inference-in-single-cell-data/
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/png:https://ds.dfci.harvard.edu/wp-content/uploads/2024/10/HBCFall24.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20241121T160000
DTEND;TZID=America/New_York:20241121T170000
DTSTAMP:20260407T210809
CREATED:20241119T210657Z
LAST-MODIFIED:20241126T123840Z
UID:5686-1732204800-1732208400@ds.dfci.harvard.edu
SUMMARY:Enhanced Insights for Rare Diseases: Leveraging External Controls and Longitudinal Data in snSMART Designs
DESCRIPTION:HSPH Biostatistics/DFCI Data Science Colloquium \nThursday\, November 21 at 4:00pm\nHarvard TH Chan School of Public Health\nFXB-G01 \nKelley Kidwell\, PhD\nProfessor\, Biostatistics\nAssociate Chair for Academic Affairs\, Biostatistics\nUniversity of Michigan\, Ann Arbor
URL:https://ds.dfci.harvard.edu/event/enhanced-insights-for-rare-diseases-leveraging-external-controls-and-longitudinal-data-in-snsmart-designs/
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/png:https://ds.dfci.harvard.edu/wp-content/uploads/2024/11/kidwell-crop.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20241205T100000
DTEND;TZID=America/New_York:20241205T120000
DTSTAMP:20260407T210809
CREATED:20241029T143216Z
LAST-MODIFIED:20241206T124841Z
UID:5660-1733392800-1733400000@ds.dfci.harvard.edu
SUMMARY:Spatial Transcriptomics Deconvolution Analysis
DESCRIPTION:The Harvard Chan Bioinformatics Core in partnership with The Cancer Data Sciences Program at DF/HCC are very excited to present the Fall seminar series: \nNovel Applications in Single Cell Omics \n\n\n\nLigand receptor analysis in a spatial context\nNovember 7th\nSizun Jiang\, PhD\nBeth Israel Deaconess Medical Center Harvard Medical School \nMartin Hemberg\, PhD and Jingyi Cao\, PhD Brigham and Women’s Hospital\nHarvard Medical School \nTrajectory inference in single cell data\nNovember 21st\nLuca Pinello\, PhD and Cameron Ray Smith\, MD\, PhD\nMassachusetts General Hospital \nSpatial transcriptomics deconvolution analysis\nDecember 5th\nKevin Wei\, MD\, PhD Brigham and Women’s Hospital\nRuben Dries\, PhD and George Chen Boston University School of Medicine \nAll seminar sessions are hybrid events being held from 10am to noon.\nLocation: CLSB Building\, 3 Blackfan Circle\, Boston MA 02115 \n\n\n\n\nRegister now at: https://bit.ly/fall2024_speaker_series
URL:https://ds.dfci.harvard.edu/event/spatial-transcriptomics-deconvolution-analysis/
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/png:https://ds.dfci.harvard.edu/wp-content/uploads/2024/10/HBCFall24.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20241211T120000
DTEND;TZID=America/New_York:20241211T130000
DTSTAMP:20260407T210809
CREATED:20241204T191023Z
LAST-MODIFIED:20241211T183620Z
UID:5718-1733918400-1733922000@ds.dfci.harvard.edu
SUMMARY:EVOCANCERGPT: Generating Cancer Progression and Treatment Responses with Single-Cell RNA Sequences
DESCRIPTION:CompBio Connection Seminar Series \nWednesday December 11 at 12:00pm\nZelen Commons\, 11th floor\nCenter for Life Sciences Building \nSam Wang\, Computational Biologist\, Department of Data Science \nLunch is provided.
URL:https://ds.dfci.harvard.edu/event/evocancergpt-generating-cancer-progression-and-treatment-responses-with-single-cell-rna-sequences/
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/jpeg:https://ds.dfci.harvard.edu/wp-content/uploads/2024/12/Wang_Sam.jpeg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20241219T120000
DTEND;TZID=America/New_York:20241219T130000
DTSTAMP:20260407T210809
CREATED:20241209T183348Z
LAST-MODIFIED:20250114T202112Z
UID:5733-1734609600-1734613200@ds.dfci.harvard.edu
SUMMARY:Interpretable and Data-driven Machine Learning Models for Analyzing High-dimensional Biological Data
DESCRIPTION:Data Science Seminar \nThursday December 19\, 2024 – 12pm-1pm\nCenter for Life Sciences Building\, room 11081 \nJunchen Yang\nDepartment of Computational Biology and Bioinformatics\, Yale University
URL:https://ds.dfci.harvard.edu/event/interpretable-and-data-driven-machine-learning-models-for-analyzing-high-dimensional-biological-data/
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/jpeg:https://ds.dfci.harvard.edu/wp-content/uploads/2024/12/junchen-scaled-e1733769209863.jpeg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20250205T120000
DTEND;TZID=America/New_York:20250205T130000
DTSTAMP:20260407T210809
CREATED:20250128T174809Z
LAST-MODIFIED:20250205T180933Z
UID:5793-1738756800-1738760400@ds.dfci.harvard.edu
SUMMARY:Nice-Driven Cell Identities in the Self-Renewing Stomach Corpus Epithelium
DESCRIPTION:CompBio Connections Seminar\nWednesday February 5 at 12:00pm\nCenter for Life Sciences Building\, Zelen Commons\nKe Li\, PhD\, Research Fellow\, Dana-Farber Cancer Institute and Harvard Medical School \nLunch is provided.
URL:https://ds.dfci.harvard.edu/event/nice-driven-cell-identities-in-the-self-renewing-stomach-corpus-epithelium/
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/png:https://ds.dfci.harvard.edu/wp-content/uploads/2025/01/Ke_headshot_square.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20250206T160000
DTEND;TZID=America/New_York:20250206T170000
DTSTAMP:20260407T210809
CREATED:20250114T202055Z
LAST-MODIFIED:20250207T140322Z
UID:5776-1738857600-1738861200@ds.dfci.harvard.edu
SUMMARY:Choosing Good Subsamples for Regression Modelling: Nearly-True Models?
DESCRIPTION:Harvard Biostatistics Colloquium Series\nThursday February 6th\n4:00-5:00PM\nHarvard TH Chan School of Public Health\, FXB G12 \nThomas Lumley\, PhD\, Chair in Biostatistics\, University of Aukland\, New Zealand; Affiliate Professor\, University of Washington\, Department of Biostatistics
URL:https://ds.dfci.harvard.edu/event/choosing-good-subsamples-for-regression-modelling-nearly-true-models/
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/png:https://ds.dfci.harvard.edu/wp-content/uploads/2025/01/lumley.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20250213T110000
DTEND;TZID=America/New_York:20250213T120000
DTSTAMP:20260407T210809
CREATED:20250207T182809Z
LAST-MODIFIED:20250211T172627Z
UID:5831-1739444400-1739448000@ds.dfci.harvard.edu
SUMMARY:Analysis and Design of RNA sequences with Deep Learning
DESCRIPTION:Data Science Seminar\nThursday February 13th at 11am\nCenter for Life Sciences Building\, 111081 \nJoseph Valencia\, Oregon State University
URL:https://ds.dfci.harvard.edu/event/analysis-and-design-of-rna-sequences-with-deep-learning/
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/jpeg:https://ds.dfci.harvard.edu/wp-content/uploads/2025/02/joseph-e1738952849381.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20250219T120000
DTEND;TZID=America/New_York:20250219T130000
DTSTAMP:20260407T210809
CREATED:20250211T172609Z
LAST-MODIFIED:20250219T130227Z
UID:5846-1739966400-1739970000@ds.dfci.harvard.edu
SUMMARY:Current Methods in Single Cell FFPE Analysis
DESCRIPTION:CompBio Connections\nFebruary 19\, 2025 at 12pm\nDFCI Center for Life Science Building\, Zelen Commons \nAnthony Anselmo\nLead Bioinformatician\nCenter for Cancer Genomics\, DFCI \nLunch is provided.
URL:https://ds.dfci.harvard.edu/event/current-methods-in-single-cell-ffpe-analysis/
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/jpeg:https://ds.dfci.harvard.edu/wp-content/uploads/2025/02/anthony_anselmo_phsweb-e1739294749828.jpeg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20250227T160000
DTEND;TZID=America/New_York:20250227T170000
DTSTAMP:20260407T210809
CREATED:20250219T130212Z
LAST-MODIFIED:20250228T180912Z
UID:5867-1740672000-1740675600@ds.dfci.harvard.edu
SUMMARY:Single-cell Multi-sample Multi-condition Data Integration to Uncover Disease Signatures
DESCRIPTION:HSPH Biostatistics and DFCI Data Science Colloquium\nThursday February 27th at 4pm\nHSPH FXB Room G13 \nYingxin Lin\, PhD\nPostdoctoral Associate in the Department of Biostatistics at the Yale School of Public Health \nThe recent emergence of multi-sample multi-condition single-cell multi cohort studies allows researchers to investigate different cell states. The effective integration of multiple large-cohort studies promises biological insights into cells under different conditions that individual studies cannot provide. In this talk\, I will present scMerge2\, a scalable algorithm that allows data integration of atlas-scale multi-sample multi-condition single-cell studies. scMerge2 is generalized to enable the merging of millions of cells from single-cell studies generated by various single-cell technologies. Using a large data collection with over five million cells from 1000+ individuals\, we demonstrate that the integration of multi-sample multi-condition scRNAseq from multiple cohorts reveals signatures derived from cell-type expression that are more accurate in discriminating disease progression.
URL:https://ds.dfci.harvard.edu/event/single-cell-multi-sample-multi-condition-data-integration-to-uncover-disease-signatures/
LOCATION:Harvard TH Chan School of Public Health\, FXB G13\, 677 Huntington Ave\, Boston\, MA\, United States
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/jpeg:https://ds.dfci.harvard.edu/wp-content/uploads/2025/02/headshot-scaled-e1739970101940.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20250303T160000
DTEND;TZID=America/New_York:20250303T170000
DTSTAMP:20260407T210809
CREATED:20250221T180945Z
LAST-MODIFIED:20250304T155554Z
UID:5888-1741017600-1741021200@ds.dfci.harvard.edu
SUMMARY:How Do Neural Networks Learn Features From Data?
DESCRIPTION:HSPH Biostatistics and DFCI Data Science Colloquium\nMonday March 3rd at 4:00pm\nHSPH Kresge G2 \nAdityanarayanan Radhakrishnan\nEric and Wendy Schmidt Center Postdoctoral Fellow\, Broad Institute of MIT and Harvard\, Harvard School of Engineering and Applied Sciences \nAbstract: Understanding how neural networks learn features\, or relevant patterns in data\, is key to accelerating scientific discovery. In this talk\, I will present a unifying mechanism that characterizes feature learning in neural network architectures. Namely\, features learned by neural networks are captured by a statistical operator known as the average gradient outer product (AGOP). More generally\, the AGOP enables feature learning in machine learning models that have no built-in feature learning mechanism (e.g.\, kernel methods). I will present two applications of this line of work. First\, I will show how AGOP can be used to steer LLMs and vision-language models\, guiding them towards specified concepts and shedding light on vulnerabilities in these models. I will then discuss how AGOP can be used to discover cellular programs (sets of genes whose expressions exhibit dependencies across cell subpopulations) from millions of sequenced cells. I will show how AGOP identified programs that reflect the heterogeneity found in various cell types\, subtypes\, and states in this data. Overall\, this line of work advances our fundamental understanding of how neural networks extract features from data\, leading to the development of novel\, interpretable\, and effective methods for use in scientific applications.
URL:https://ds.dfci.harvard.edu/event/how-do-neural-networks-learn-features-from-data/
LOCATION:Harvard TH Chan School of Public Health\, 677 Huntington Ave\, Boston\, MA\, 02115
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/jpeg:https://ds.dfci.harvard.edu/wp-content/uploads/2025/02/headshot-e1740161254828.jpeg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20250306T160000
DTEND;TZID=America/New_York:20250306T170000
DTSTAMP:20260407T210809
CREATED:20250207T140313Z
LAST-MODIFIED:20250307T160147Z
UID:5819-1741276800-1741280400@ds.dfci.harvard.edu
SUMMARY:Universal Prediction of Cell-cycle Position Using Transfer Learning
DESCRIPTION:HSPH Biostatistics and DFCI Data Science Colloquium\nThursday\, March 6\, 2025\n4:00pm\nHarvard TH Chan School of Public Health\, FXB G13 \nKasper Hansen\, PhD\nAssociate Professor\, McKusick-Nathans Insitute of Genetic Medicine\, Department of Biostatistics\, Johns Hopkins University \nA significant barrier to progress in biomedical data science is the development of prediction models that work across contexts such as different instruments\, facilities or hospitals. This is particularly difficult for predictions based on genomics data. Here\, we present an example of a generalizable prediction model. \nThe cell cycle is a highly conserved\, continuous process which controls faithful replication and division of cells. Single-cell technologies have enabled increasingly precise measurements of the cell cycle both as a biological process of interest and as a possible confounding factor. Despite its importance and conservation\, there is no universally applicable approach to infer position in the cell cycle with high-resolution from single-cell RNA-seq data. \nHere\, we present tricycle\, an R/Bioconductor package\, which addresses this challenge by leveraging key features of the biology of the cell cycle\, the mathematical properties of principal component analysis of periodic functions\, and the use of transfer learning. We estimate a cell-cycle embedding using a fixed reference dataset and project new data into this reference embedding\, an approach that overcomes key limitations of learning a dataset-dependent embedding. Tricycle then predicts a cell-specific position in the cell cycle based on the data projection. The accuracy of tricycle compares favorably to gold-standard experimental assays\, which generally require specialized measurements in specifically constructed in vitro systems. Using internal controls which are available for any dataset\, we show that tricycle predictions generalize to datasets with multiple cell types\, across tissues\, species\, and even sequencing assays.
URL:https://ds.dfci.harvard.edu/event/universal-prediction-of-cell-cycle-position-using-transfer-learning/
LOCATION:Harvard TH Chan School of Public Health\, FXB G13\, 677 Huntington Ave\, Boston\, MA\, United States
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/jpeg:https://ds.dfci.harvard.edu/wp-content/uploads/2025/02/khansen.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20250310T160000
DTEND;TZID=America/New_York:20250310T170000
DTSTAMP:20260407T210809
CREATED:20250226T134218Z
LAST-MODIFIED:20250311T161944Z
UID:5905-1741622400-1741626000@ds.dfci.harvard.edu
SUMMARY:Decoding Aging at Spatial and Single-cell Resolution with Machine Learning
DESCRIPTION:HSPH Biostatistics and DFCI Data Science Colloquium\nMonday March 10th at 4:00pm\nHSPH Kresge G2 \nEric Sun\nPhD Candidate\, Department of Biomedical Informatics\nStanford University \nAging 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 enabled the high-dimensional profiling of complex biology including that underlying aging. As such\, new machine learning and computational methods are needed to unlock important insights from spatial and single-cell omics datasets. First\, I present the development of high-resolution machine learning models (‘spatial aging clocks’) that can measure the aging of individual cells in the brain. Using these spatial aging clocks\, I discovered that some cell types can dramatically influence the aging of nearby cells. Next\, I present new computational and statistical methods for overcoming the gene coverage limitations of existing spatially resolved single-cell omics technologies\, which have enabled the discovery of gene pathways underlying the spatial effects of brain aging. \n 
URL:https://ds.dfci.harvard.edu/event/decoding-aging-at-spatial-and-single-cell-resolution-with-machine-learning/
LOCATION:Harvard TH Chan School of Public Health\, 677 Huntington Ave\, Boston\, MA\, 02115
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/jpeg:https://ds.dfci.harvard.edu/wp-content/uploads/2025/02/Eric_Sun-scaled-e1740577294700.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20250311T160000
DTEND;TZID=America/New_York:20250311T170000
DTSTAMP:20260407T210809
CREATED:20250226T134543Z
LAST-MODIFIED:20250312T130019Z
UID:5910-1741708800-1741712400@ds.dfci.harvard.edu
SUMMARY:Dissecting Tumor Transcriptional Heterogeneity from Single-cell RNA-seq Data by Generalized Binary Covariance Decomposition
DESCRIPTION:HSPH Biostatistics and DFCI Data Science Colloquium\nTuesday March 11th at 4:00pm\nHSPH FXB G12 \nYusha Liu\, PhD\nResearch Assistant Professor\nDepartment of Biostatistics\nThe University of North Carolina at Chapel Hill \nProfiling tumors with single-cell RNA sequencing has the potential to identify recurrent patterns of transcription variation related to cancer progression\, and to produce therapeutically relevant insights. However\, strong inter-tumor heterogeneity can obscure more subtle patterns that are shared across tumors. In this talk\, I will introduce a novel statistical method\, generalized binary covariance decomposition (GBCD)\, to address this problem. GBCD can decompose transcriptional heterogeneity into interpretable components — including patient-specific\, dataset-specific and shared components relevant to disease subtypes — and that\, in the presence of strong inter-tumor heterogeneity\, it can produce more interpretable results than existing methods. Applied to data on pancreatic ductal adenocarcinoma\, GBCD produced a refined characterization of existing tumor subtypes\, and identified a gene expression program prognostic of poor survival independent of tumor stage and subtype. The gene expression program is enriched for genes involved in stress responses\, and suggests a role for the integrated stress response in pancreatic ductal adenocarcinoma.
URL:https://ds.dfci.harvard.edu/event/dissecting-tumor-transcriptional-heterogeneity-from-single-cell-rna-seq-data-by-generalized-binary-covariance-decomposition/
LOCATION:Harvard TH Chan School of Public Health\, 677 Huntington Ave\, Boston\, MA\, 02115
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/png:https://ds.dfci.harvard.edu/wp-content/uploads/2025/02/headshot-copy-e1740577495994.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20250327T160000
DTEND;TZID=America/New_York:20250327T170000
DTSTAMP:20260407T210809
CREATED:20250314T163931Z
LAST-MODIFIED:20250328T121320Z
UID:5985-1743091200-1743094800@ds.dfci.harvard.edu
SUMMARY:Data Integration in Spatial and Single Cell Omics:  What is Erased\, and Can you Recover it?
DESCRIPTION:HSPH Biostatistics and DFCI Data Science Colloquium\nThursday\, March 27\, 2025\n4:00pm\nHarvard TH Chan School of Public Health\, FXB G13 \n\nNancy Zhang\, PhD\nGe Li and Ning Zhao Professor\, Professor of Statistics and Data Science\, Vice Dean of Wharton Doctoral Programs\,  The Wharton School\, University of Pennsylvania \nIn single-cell and spatial biology\, data integration refers to the alignment of cells across samples and modalities\, and is an ubiquitous challenge affecting all downstream analyses. The goal in cell integration is to find cells across data sets that share the same biological state that may be obscured by technical differences. \nIn this talk\, I will cast the cell integration problem on a continuum of weak to strong linkage\, depending on the strength of feature sharing between experiments. First\, I will examine integration across data modalities of weak linkage. This arises when there are few shared features between the data being integrated\, for example\, between single-cell RNA sequencing data and spatial proteomics data. For this\, I will present MaxFuse\, a method that leverages higher order relationships between all features\, including unshared features\, to achieve accurate integration. Next\, we consider the scenario of data alignment across the same modality in clinical scale studies. For this setting\, I will show that existing paradigms are overly aggressive\, erasing disease and treatment effects and introducing severe data distortion. I will introduce a “pool-of-controls” experimental design concept to disentangle biological variation from unwanted variation. Based on this\, I will describe CellANOVA\, a novel statistical model and scalable algorithm that recovers biological signals lost during batch integration and corrects integration related data distortion. Through these two contrasting paradigms\, I will share the key lessons learned and the remaining challenges in this field.
URL:https://ds.dfci.harvard.edu/event/data-integration-in-spatial-and-single-cell-omics-what-is-erased-and-can-you-recover-it/
LOCATION:Harvard TH Chan School of Public Health\, 677 Huntington Ave\, Boston\, MA\, 02115
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/jpeg:https://ds.dfci.harvard.edu/wp-content/uploads/2025/03/zhang-crop-e1741970356597.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20250403T160000
DTEND;TZID=America/New_York:20250403T170000
DTSTAMP:20260407T210809
CREATED:20250314T164130Z
LAST-MODIFIED:20250328T121330Z
UID:5991-1743696000-1743699600@ds.dfci.harvard.edu
SUMMARY:Fréchet Regression of Random Objects on Vector Covariates and Its Applications for  Single Cell RNA-seq Data Analysis
DESCRIPTION:HSPH Biostatistics and DFCI Data Science Colloquium\nThursday\, April 3\, 2025\n4:00pm\nHarvard TH Chan School of Public Health\, FXB G13 \nHongzhe Li\, PhD\nPerelman Professor of Biostatistics\, Epidemiology and Informatics\nDirector\, Center for Statistics in Big Data Vice Chair for Research Integration\, Department of Biostatistics\, Epidemiology and Informatics\, University of Pennsylvania \nPopulation-level single-cell RNA-seq data captures gene expression profiles across thousands of cells from each individual in a sizable cohort. This data facilitates the construction of cell-type- and individual-specific gene co-expression networks by estimating covariance matrices. Investigating how these co-expression networks relate to individual-level covariates provides critical insights into the interplay between molecular processes and biological or clinical traits. This talk introduces Fréchet regression\, modeling covariance matrices as outcomes and vector covariates as predictors\, using the Wasserstein distance between covariance matrices as a metric instead of the Euclidean distance. A test statistic is proposed based on the Fréchet mean and covariate-weighted Fréchet mean\, with its asymptotic null distribution derived. Analysis of large-scale single-cell RNA-seq data reveals an association between the co-expression network of genes in the nutrient-sensing pathway and age\, highlighting perturbations in gene co-expression networks with aging. Additionally\, a robust local Fréchet regression approach\, leveraging neural unbalanced optimal transport\, is briefly discussed to explore how cells are temporally organized during the differentiation of human embryonic stem cells into embryoid bodies.
URL:https://ds.dfci.harvard.edu/event/frechet-regression-of-random-objects-on-vector-covariates-and-its-applications-for-single-cell-rna-seq-data-analysis/
LOCATION:Harvard TH Chan School of Public Health\, 677 Huntington Ave\, Boston\, MA\, 02115
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/jpeg:https://ds.dfci.harvard.edu/wp-content/uploads/2025/03/li-crop.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20250415T160000
DTEND;TZID=America/New_York:20250415T170000
DTSTAMP:20260407T210809
CREATED:20250409T114825Z
LAST-MODIFIED:20250411T114358Z
UID:6027-1744732800-1744736400@ds.dfci.harvard.edu
SUMMARY:Modeling Multiscale Genome and Cellular Organization
DESCRIPTION:HSPH Biostatistics and DFCI Data Science Seminar\nTuesday April 15 at 4:00pm\nDana-Farber Cancer Institute\nCenter for Life Sciences Building\, 11th Floor\, Room 11081 \nJian Ma\, PhD\nRay and Stephanie Lane Professor of Computational Biology\nCarnegie Mellon University \n  \nThe intersection of Al/ML and biomedicine is entering a transformative era\, with growing potential to\nimpact both basic research and translational medicine. Yet\, despite remarkable advances in high-\nthroughput technologies across genomics and cell biology\, our understanding of the diverse cell types\nin the human body and the underlying principles of intracellular molecular organization and\nintercellular spatial interactions remains incomplete. A central challenge lies in developing\ncomputational frameworks that can integrate molecular\, cellular\, and tissue-level data to advance cell\nbiology at an unprecedented scale. In this talk\, I will present our recent work on machine learning\napproaches for regulatory genomics\, with a focus on single-cell 3D epigenomics. We introduce methods\nthat connect different layers of 3D genome architecture and cellular function at single-cell resolution\,\nincluding graph- and hypergraph-based models that capture spatial genome organization. I will also\nhighlight our latest efforts in developing self-supervised learning frameworks to delineate multiscale\ncellular interactions within complex tissues\, enabling the discovery of previously unrecognized spatially\norganized patterns. Together\, these Al-driven models provide a foundation for integrative\, multiscale\nrepresentations of cellular systems\, offering new insights into genome structure\, gene regulation\, and\ncell-cell communication. This line of work opens new opportunities toward building cohesive multiscale\ncellular models applicable across a broad range of contexts in health and disease.
URL:https://ds.dfci.harvard.edu/event/modeling-multiscale-genome-and-cellular-organization/
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/png:https://ds.dfci.harvard.edu/wp-content/uploads/2025/04/jian-ma-copy-e1744199268316.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20250429T110000
DTEND;TZID=America/New_York:20250429T120000
DTSTAMP:20260407T210809
CREATED:20250417T114534Z
LAST-MODIFIED:20250417T114534Z
UID:6047-1745924400-1745928000@ds.dfci.harvard.edu
SUMMARY:Complex Disease Modeling And Efficient Drug Discovery With Large Language Models
DESCRIPTION:HSPH Biostatistics and DFCI Data Science Seminar\nTuesday April 29 from 11:00-12:00pm\nZoom only (Link to be posted shortly) \nYu Li\, PhD\nAssistant Professor\, CSE\nThe Chinese University of Hong Kong \nLarge language models\, which can integrate and process large amounts of data in biomedicine\, have great potential in modeling complex diseases and discovering functional biomolecules for potential therapeutics. To model complex diseases and identify the potential drug targets for such diseases\, we built a language model trained on the insurance claims of around 123 million US people. With the model\, we can give a unified representation of all the common complex diseases\, which enables us to predict the genetic parameters of the diseases and discover unique genetic loci related to them efficiently. Then\, we developed models based on protein language models to efficiently discover remote homologs and functional biomolecules from nature\, such as signal peptides and antimicrobial peptides. With the model\, we can identify remote homologs 22 times faster than PSI-BLAST and discover diverse functional peptides with sequence similarity lower than 20% against the known ones. Finally\, we developed an RNA language model to model the RNA sequence and structure relation\, which enables us to perform RNA structure prediction and reverse design effectively. Within two months\, we designed and experimentally validated 19 RNA aptamers that are structurally similar\, yet sequence dissimilar\, to known light-up aptamers. More importantly\, 10 designed aptamers show higher fluorescence than the native Mango-I. The above projects demonstrate the great potential of large language models in promoting fundamental computational biological research and potential transformational development.
URL:https://ds.dfci.harvard.edu/event/complex-disease-modeling-and-efficient-drug-discovery-with-large-language-models/
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/jpeg:https://ds.dfci.harvard.edu/wp-content/uploads/2025/04/headshot.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20250911T160000
DTEND;TZID=America/New_York:20250911T170000
DTSTAMP:20260407T210809
CREATED:20250903T113817Z
LAST-MODIFIED:20250912T120312Z
UID:6428-1757606400-1757610000@ds.dfci.harvard.edu
SUMMARY:Preference Inference for Language Models Debiased by Fisher Random Walk Models
DESCRIPTION:HSPH Biostatistics & DFCI Data Science Colloquium Series\nSeptember 11 at 4:00PM\nHarvard TH Chan School of Public Health\, FXB-301 \nJunwei Lu\, PhD\nAssociate Professor of Biostatistics\, Harvard TH Chan School of Public Health \nHuman preference alignment has been shown to be effective in training the large language models (LMs). It allows the LLM to understand human feedback and preferences. Despite the extensive literature dealing with algorithms aligning the rank of human preference\, uncertainty quantification for the ranking estimation still needs to be explored and is of great practical significance. For example\, it is important to overcome the problem of hallucination for LLM in the medical domain\, and an inferential method for the ranking of LM answers becomes necessary. In this talk\, we will present a novel framework called “Fisher random walk” to conduct semi-parametric efficient preference inference for language models and illustrate its application in the language models for medical knowledge.
URL:https://ds.dfci.harvard.edu/event/preference-inference-for-language-models-debiased-by-fisher-random-walk-models/
LOCATION:Harvard TH Chan School of Public Health\, 677 Huntington Ave\, Boston\, MA\, 02115
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/jpeg:https://ds.dfci.harvard.edu/wp-content/uploads/2025/09/junweilarger.jpeg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20250918T160000
DTEND;TZID=America/New_York:20250918T170000
DTSTAMP:20260407T210809
CREATED:20250912T174007Z
LAST-MODIFIED:20250918T235342Z
UID:6513-1758211200-1758214800@ds.dfci.harvard.edu
SUMMARY:Reproducible Research - Tools and a case study with NHANES
DESCRIPTION:HSPH Biostatistics & DFCI Data Science Colloquium Series \nSeptember 18\, 2025\n4:00 PM\nHSPH FXB-301 \nRobert Gentleman\, PhD\nPrincipal Research Scientist\nHarvard T.H. Chan School of Public Health and Dana-Farber Cancer Institute \nI will discuss how new technologies and statistical methodologies can help enhance our ability to perform reproducible research. I will demonstrate how these could be used in a real world setting by examining questions\, primarily of an epidemiological nature\, using data from the NHANES surveys. I will describe one version of an Environment Wide Association Study (EnWAS) and show how this methodology can potentially be employed to interrogate large complex data resources. \n 
URL:https://ds.dfci.harvard.edu/event/reproducible-research-tools-and-a-case-study-with-nhanes/
LOCATION:Harvard TH Chan School of Public Health\, 677 Huntington Ave\, Boston\, MA\, 02115
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/jpeg:https://ds.dfci.harvard.edu/wp-content/uploads/2025/09/Robert-Gentlemen-850x430-2-e1757698738137.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20251002T160000
DTEND;TZID=America/New_York:20251002T170000
DTSTAMP:20260407T210809
CREATED:20251001T170551Z
LAST-MODIFIED:20251003T124253Z
UID:6556-1759420800-1759424400@ds.dfci.harvard.edu
SUMMARY:Navigate the Crossroad of Statistics\, Generative AI and Genomic Health
DESCRIPTION:HSPH Biostatistics & DFCI Data Science Colloquium Series \nThursday October 2\, 2025\n4:00pm ET\nHSPH FXB-301 \nXihong Lin\, PhD\, Department of Biostatistics and Department of Statistics\, Harvard University \nIntegrating 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 statistical principles that account for uncertainty and enhance interpretability. In this talk\, I will discuss the challenges and opportunities as we navigate the crossroad of statistics\, generative Al\, and genomic health science. I will highlight how synthetic data from generative models\, such as diffusion models and transformers\, can be used to enable robust and powerful statistical analyses\, while ensuring valid inference even when generative Al models are misspecified and treated as black-box tools. I will illustrate such synthetic data powered statistical inference with generative ML/Al through large scale analyses of the UK biobank in the presence of missing data\, and discuss its connection with prediction powered inference (PPI). I will also discuss how to build an end-to-end autonomous\, scalable and interpretable large-scale whole genome sequencing (WGS) analysis ecosystem. These efforts will be illustrated using the analysis of the TOPMed WGS samples of 200\,000 samples\, the UK biobank of 500\,000 subjects on the cloud platform RAP and as well the All of Us data of 400\,000 subjects in the NIH cloud platform AnVIL. \n 
URL:https://ds.dfci.harvard.edu/event/navigate-the-crossroad-of-statistics-generative-ai-and-genomic-health/
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/png:https://ds.dfci.harvard.edu/wp-content/uploads/2025/10/xihong_lin_crop.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20251009T160000
DTEND;TZID=America/New_York:20251009T170000
DTSTAMP:20260407T210809
CREATED:20251001T170830Z
LAST-MODIFIED:20251014T115427Z
UID:6561-1760025600-1760029200@ds.dfci.harvard.edu
SUMMARY:Flexible Adaptive Procedures for Testing Multiple Treatments\, Endpoints or Populations in Confirmatory Clinical Trials
DESCRIPTION:HSPH Biostatistics & DFCI Data Science Colloquium Series \nThursday October 9\, 2025\n4:00pm ET\nHSPH FXB-301 \nCyrus Mehta\, President and Co-Founder of Cytel\, Inc\, Adjunct Professor\, Department of Biostatistics\, Harvard TH Chan School of Public Health \nThe statistical methodology for the classical two-arm group sequential design has advanced vastly over the past three decades to incorporate\, adaptive design changes\, multiple treatments and multiple endpoints\, while nevertheless preserving strong control of the family wise error rate. The graph based approach to multiple testing is an intuitive method that enables a clinical trial study team to represent clearly\, through a directed graph\, its priorities for hierarchical testing of multiple hypotheses\, and for propagating the available type-1 error from rejected or dropped hypotheses to hypotheses yet to be tested. Although originally developed for single stage non-adaptive designs\, we show how it may be extended to two-stage designs that permit early identification of efficacious treatments\, adaptive sample size re-estimation\, dropping of hypotheses\, and changes in the hierarchical testing strategy at the end of stage one. We will present the statistical methodology for controlling the family wise error rate in the presence of these adaptive changes\, and will generate the operating characteristics of different underlying scenarios and adaptive decision rules through a large simulation experiment.
URL:https://ds.dfci.harvard.edu/event/flexible-adaptive-procedures-for-testing-multiple-treatments-endpoints-or-populations-in-confirmatory-clinical-trials/
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/jpeg:https://ds.dfci.harvard.edu/wp-content/uploads/2025/10/cyrus-square-e1759338489996.jpg
END:VEVENT
END:VCALENDAR