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X-WR-CALNAME:Dana-Farber Cancer Institute
X-ORIGINAL-URL:https://ds.dfci.harvard.edu
X-WR-CALDESC:Events for Dana-Farber Cancer Institute
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BEGIN:VEVENT
DTSTART;TZID=America/New_York:20250415T160000
DTEND;TZID=America/New_York:20250415T170000
DTSTAMP:20260408T004716
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/
LOCATION:MA
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:20260408T004716
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/
LOCATION:MA
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:20260408T004716
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:20260408T004716
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:20260408T004716
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/
LOCATION:MA
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:20260408T004716
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/
LOCATION:MA
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/jpeg:https://ds.dfci.harvard.edu/wp-content/uploads/2025/10/cyrus-square-e1759338489996.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20251016T160000
DTEND;TZID=America/New_York:20251016T170000
DTSTAMP:20260408T004716
CREATED:20251014T115412Z
LAST-MODIFIED:20251017T111716Z
UID:6602-1760630400-1760634000@ds.dfci.harvard.edu
SUMMARY:Estimation and Inference of Two Doubly Robust Functionals in High Dimensions
DESCRIPTION:HSPH Biostatistics & DFCI Data Science Colloquium Series \nThursday October 16\, 2025\n4:00pm ET\nHSPH FXB-301 \nRajarshi Mukherjee\, Associate Professor of Biostatistics\, Harvard T.H. Chan School of Public Health\nWebsite
URL:https://ds.dfci.harvard.edu/event/estimation-and-inference-of-two-doubly-robust-functionals-in-high-dimensions/
LOCATION:MA
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/png:https://ds.dfci.harvard.edu/wp-content/uploads/2025/10/Rajarshi.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20251113T160000
DTEND;TZID=America/New_York:20251113T170000
DTSTAMP:20260408T004716
CREATED:20251107T191711Z
LAST-MODIFIED:20251117T185408Z
UID:6649-1763049600-1763053200@ds.dfci.harvard.edu
SUMMARY:Addressing Statistical Challenges in Long COVID Research: Auxiliary Variable-Dependent Sampling Designs and Clustering of Complex Data Types
DESCRIPTION:HSPH Biostatistics & DFCI Data Science Colloquium Seminar Series\nNovember 13\, 2025 at 4:00pm\nHSPH\, FXB 301 \nSpeakers: Joint presentation by Tony Harrison & Thaweethai Reeder
URL:https://ds.dfci.harvard.edu/event/addressing-statistical-challenges-in-long-covid-research-auxiliary-variable-dependent-sampling-designs-and-clustering-of-complex-data-types/
LOCATION:MA
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/jpeg:https://ds.dfci.harvard.edu/wp-content/uploads/2025/11/hsph.jpeg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20251120T160000
DTEND;TZID=America/New_York:20251120T170000
DTSTAMP:20260408T004716
CREATED:20251117T185354Z
LAST-MODIFIED:20251117T185503Z
UID:6669-1763654400-1763658000@ds.dfci.harvard.edu
SUMMARY:The Single Arm Changing to Randomized Design (SACRED)
DESCRIPTION:HSPH Biostatistics & DFCI Data Science Colloquium Seminar Series\nHarvard TH Chan School of Public Health\, FXB 301\nNovember 21st\, 4:00-5:00pm \nGlen Laird\, Head of Biostatistics\, Methodology and Innovation\, Vertex Pharmaceuticals
URL:https://ds.dfci.harvard.edu/event/the-single-arm-changing-to-randomized-design-sacred/
LOCATION:MA
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/jpeg:https://ds.dfci.harvard.edu/wp-content/uploads/2025/11/Glen_Laird-1-e1763405616809.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20260130T080000
DTEND;TZID=America/New_York:20260130T170000
DTSTAMP:20260408T004716
CREATED:20251222T190751Z
LAST-MODIFIED:20260129T193514Z
UID:6752-1769760000-1769792400@ds.dfci.harvard.edu
SUMMARY:Stay tuned for 2026 events!
DESCRIPTION:Please watch our Events page for the schedule of seminars and workshops starting in February 2026!
URL:https://ds.dfci.harvard.edu/event/stay-tuned-for-2026-events/
LOCATION:MA
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/png:https://ds.dfci.harvard.edu/wp-content/uploads/2020/09/10221_Facebook_360x360.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20260205T160000
DTEND;TZID=America/New_York:20260205T170000
DTSTAMP:20260408T004716
CREATED:20260129T193457Z
LAST-MODIFIED:20260129T193457Z
UID:6823-1770307200-1770310800@ds.dfci.harvard.edu
SUMMARY:Data Integration and Time-informed Methods for the Electronic Health Record
DESCRIPTION:HSPH Biostatistics and DFCI Data Science Colloquium \nThursday February 5 at 4PM\nHSPH\, FXB 301 \nSpeaker: Parker Knight\, PhD Candidate\, Harvard TH Chan School of Public Health \nSeminar Website.
URL:https://ds.dfci.harvard.edu/event/data-integration-and-time-informed-methods-for-the-electronic-health-record/
LOCATION:MA
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/jpeg:https://ds.dfci.harvard.edu/wp-content/uploads/2026/01/feb5-colloquium.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20260212T160000
DTEND;TZID=America/New_York:20260212T170000
DTSTAMP:20260408T004716
CREATED:20260206T123005Z
LAST-MODIFIED:20260206T123005Z
UID:6833-1770912000-1770915600@ds.dfci.harvard.edu
SUMMARY:Efficient Estimation of Causal Effects Under Two-Phase Sampling with Error-Prone Outcome and Treatment Measurements
DESCRIPTION:HSPH Biostatistics and DFCI Data Science Colloquium \nHSPH\, FXB 301\nSpeaker: Keith Barnatchez\, Harvard TH Chan School of Public Health \nhttps://hsph.harvard.edu/department/biostatistics/seminars-events/colloquium-seminar-series/
URL:https://ds.dfci.harvard.edu/event/efficient-estimation-of-causal-effects-under-two-phase-sampling-with-error-prone-outcome-and-treatment-measurements/
LOCATION:MA
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/jpeg:https://ds.dfci.harvard.edu/wp-content/uploads/2026/02/keith-e1770380977292.jpeg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20260219T160000
DTEND;TZID=America/New_York:20260219T170000
DTSTAMP:20260408T004716
CREATED:20260213T170557Z
LAST-MODIFIED:20260213T170557Z
UID:6849-1771516800-1771520400@ds.dfci.harvard.edu
SUMMARY:Chiseling: Powerful and Valid Subgroup Selection via Interactive Machine Learning
DESCRIPTION:HSPH Biostatistics and DFCI Data Science Colloquium\nHSPH\, FXB 301 \nNathan Cheng\, PhD Student\, Harvard TH Chan School of Public Health\nhttps://hsph.harvard.edu/department/biostatistics/seminars-events/colloquium-seminar-series/
URL:https://ds.dfci.harvard.edu/event/chiseling-powerful-and-valid-subgroup-selection-via-interactive-machine-learning/
LOCATION:MA
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/jpeg:https://ds.dfci.harvard.edu/wp-content/uploads/2026/02/nathancheng-e1771002303814.jpeg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20260226T160000
DTEND;TZID=America/New_York:20260226T170000
DTSTAMP:20260408T004716
CREATED:20260220T161310Z
LAST-MODIFIED:20260220T161310Z
UID:6862-1772121600-1772125200@ds.dfci.harvard.edu
SUMMARY:Spectral Methods for Spatial and Multi-omics data
DESCRIPTION:HSPH Biostatistics and DFCI Data Science Colloquium \nThursday February 26 at 4:00pm\nHSPH\, FXB 301 \nPhillip Nicol\, PhD Student\, Harvard TH Chan School of Public Health\nhttps://hsph.harvard.edu/department/biostatistics/seminars-events/colloquium-seminar-series/
URL:https://ds.dfci.harvard.edu/event/spectral-methods-for-spatial-and-multi-omics-data/
LOCATION:MA
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/jpeg:https://ds.dfci.harvard.edu/wp-content/uploads/2026/02/phillip.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20260305T160000
DTEND;TZID=America/New_York:20260305T170000
DTSTAMP:20260408T004716
CREATED:20260227T154510Z
LAST-MODIFIED:20260227T154510Z
UID:6868-1772726400-1772730000@ds.dfci.harvard.edu
SUMMARY:Integrating Pre-Trained Language Models into Topic Modeling
DESCRIPTION:HSPH Biostatistics and DFCI Data Science Colloquium\nThursday March 5 at 4:00pm\nHSPH\, FXB 301 \nTracy Ke\, PhD\, Associate Professor of Statistics\, Harvard University\nhttps://hsph.harvard.edu/department/biostatistics/seminars-events/colloquium-seminar-series/
URL:https://ds.dfci.harvard.edu/event/integrating-pre-trained-language-models-into-topic-modeling/
LOCATION:MA
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/jpeg:https://ds.dfci.harvard.edu/wp-content/uploads/2026/02/ke-tracy-profile-resized-e1772207070866.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20260312T160000
DTEND;TZID=America/New_York:20260312T170000
DTSTAMP:20260408T004716
CREATED:20260306T145513Z
LAST-MODIFIED:20260306T145513Z
UID:6892-1773331200-1773334800@ds.dfci.harvard.edu
SUMMARY:Inference of Tissue Architecture across Space\, Time\, and Modality
DESCRIPTION:HSPH Biostatistics and DFCI Data Science Colloquium\nThursday March 12 at 4:00pm\nHSPH\, FXB 301 \nBenjamin Raphael\, PhD\, Professor of Computer Science at Princeton University \n\nColloquium Seminar Series
URL:https://ds.dfci.harvard.edu/event/inference-of-tissue-architecture-across-space-time-and-modality/
LOCATION:MA
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/jpeg:https://ds.dfci.harvard.edu/wp-content/uploads/2026/03/Ben-Raphael.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20260326T160000
DTEND;TZID=America/New_York:20260326T170000
DTSTAMP:20260408T004717
CREATED:20260313T130013Z
LAST-MODIFIED:20260327T170931Z
UID:6918-1774540800-1774544400@ds.dfci.harvard.edu
SUMMARY:An Example to Illustrate Randomized Trial Estimands and Estimators
DESCRIPTION:HSPH Biostatistics and DFCI Data Science Colloquium\nThursday March 26 at 4:00pm\nHSPH\, FXB 301 \nLinda Harrison\, PhD\, Research Scientist\, Department of Biostatistics\, Harvard T.H. Chan School of Public Health \n\nColloquium Seminar Series
URL:https://ds.dfci.harvard.edu/event/an-example-to-illustrate-randomized-trial-estimands-and-estimators/
LOCATION:MA
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/jpeg:https://ds.dfci.harvard.edu/wp-content/uploads/2026/03/Linda_Harrison_photo-e1773406777794.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20260327T130000
DTEND;TZID=America/New_York:20260327T140000
DTSTAMP:20260408T004717
CREATED:20260319T132146Z
LAST-MODIFIED:20260320T112114Z
UID:6928-1774616400-1774620000@ds.dfci.harvard.edu
SUMMARY:An Alternative Estimator to the Cox Hazard Ratio
DESCRIPTION:Data Science Seminar \nFriday\, March 27\, 1:00 PM ET\nCenter for Life Sciences Building\, 11th floor\, room 11081\nAlso will be streamed on Zoom \nStella Karuri\, PhD\nConsulting Statistician \nZoom link: https://bit.ly/DSSeminarMar27
URL:https://ds.dfci.harvard.edu/event/an-alternative-estimator-to-the-cox-hazard-ratio/
LOCATION:MA
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/png:https://ds.dfci.harvard.edu/wp-content/uploads/2020/09/10221_Facebook_360x360.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20260402T160000
DTEND;TZID=America/New_York:20260402T170000
DTSTAMP:20260408T004717
CREATED:20260324T142131Z
LAST-MODIFIED:20260327T170915Z
UID:6935-1775145600-1775149200@ds.dfci.harvard.edu
SUMMARY:DoubleGen: Debiased Generative Modeling of Counterfactuals
DESCRIPTION:HSPH Biostatistics and DFCI Data Science Colloquium\nThursday April 2 at 4:00pm\nHSPH\, FXB 301 \nAlex Luedtke\, PhD\, Professor of Health Care Policy\, Harvard Medical School \n\nColloquium Seminar Series
URL:https://ds.dfci.harvard.edu/event/doublegen-debiased-generative-modeling-of-counterfactuals/
LOCATION:MA
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/jpeg:https://ds.dfci.harvard.edu/wp-content/uploads/2026/03/alexl_0.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20260409T160000
DTEND;TZID=America/New_York:20260409T170000
DTSTAMP:20260408T004717
CREATED:20260403T130934Z
LAST-MODIFIED:20260403T131523Z
UID:6972-1775750400-1775754000@ds.dfci.harvard.edu
SUMMARY:Factor Analysis and Questions of Causation
DESCRIPTION:HSPH Biostatistics and DFCI Data Science Colloquium\nThursday April 9 at 4:00pm\nHSPH\, FXB 301 \nTyler VanderWeele\, PhD\, John L. Loeb And Frances Lehman Loeb\, Professor of Epidemiology\, Faculty Affiliate – Department of Biostatistics\, Harvard T.H. Chan School of Public Health \nFactor analysis is often employed to evaluate the extent to which a single factor suffices to explain the variation in individual indicators. \nHowever\, often the resulting factors are interpreted as corresponding to a structural univariate latent variable that is itself causally efficacious. This assumption is so strong that it has empirically testable implications\, even though the supposed latent variable is unobserved; statistical tests are proposed that can often reject this assumption. Factor analysis also suffers from the inability to distinguish between associations arising from causal versus conceptual relations; if two supposed factors were to causally affect one another then\, over time\, the process will converge to a factor model wherein only a single factor can be detected. When both positively and negatively worded items are used\, factor analysis can also suggest that two factors are present even if the data were in fact generated by one. Examples of these various phenomena are given. \nDespite these limitations\, factor analyses can nevertheless often be informative\, but requires an appropriate reinterpretation of results as reflecting a combination of causal\, conceptual\, and distributional relations. \n\nColloquium Seminar Series \n\n 
URL:https://ds.dfci.harvard.edu/event/factor-analysis-and-questions-of-causation/
LOCATION:MA
CATEGORIES:Seminar
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DTSTART;TZID=America/New_York:20260416T160000
DTEND;TZID=America/New_York:20260416T170000
DTSTAMP:20260408T004717
CREATED:20260403T131453Z
LAST-MODIFIED:20260403T131453Z
UID:6979-1776355200-1776358800@ds.dfci.harvard.edu
SUMMARY:When Large p Is a Blessing
DESCRIPTION:HSPH Biostatistics and DFCI Data Science Colloquium\nThursday April 9 at 4:00pm\nHSPH\, FXB 301 \nZhijin Wu\, PhD\, Professor of Biostatistics\, Brown University \nBiomedical research has benefited tremendously from the breakthroughs in biotechnology in the last two decades that enabled simultaneous quantifications of a large number of biomolecules (DNA/RNA/proteins). Such data collected at the -omics scale often have a “small N large p” structure and the “large p” is often seen as a curse of Dimensionality. \nHowever\, sometimes the nature of high throughput data acquisition can be useful and provides information that is only accessible in “large p” settings. I will present several examples of our methodology development that takes advantage of the “large p” nature in genomic studies that lead to improved detection of molecular signals. \n\nColloquium Seminar Series \n\n 
URL:https://ds.dfci.harvard.edu/event/when-large-p-is-a-blessing/
LOCATION:MA
CATEGORIES:Seminar
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