BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//Dana-Farber Cancer Institute - ECPv6.15.13//NONSGML v1.0//EN
CALSCALE:GREGORIAN
METHOD:PUBLISH
X-WR-CALNAME:Dana-Farber Cancer Institute
X-ORIGINAL-URL:https://ds.dfci.harvard.edu
X-WR-CALDESC:Events for Dana-Farber Cancer Institute
REFRESH-INTERVAL;VALUE=DURATION:PT1H
X-Robots-Tag:noindex
X-PUBLISHED-TTL:PT1H
BEGIN:VTIMEZONE
TZID:America/New_York
BEGIN:DAYLIGHT
TZOFFSETFROM:-0500
TZOFFSETTO:-0400
TZNAME:EDT
DTSTART:20250309T070000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0400
TZOFFSETTO:-0500
TZNAME:EST
DTSTART:20251102T060000
END:STANDARD
BEGIN:DAYLIGHT
TZOFFSETFROM:-0500
TZOFFSETTO:-0400
TZNAME:EDT
DTSTART:20260308T070000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0400
TZOFFSETTO:-0500
TZNAME:EST
DTSTART:20261101T060000
END:STANDARD
BEGIN:DAYLIGHT
TZOFFSETFROM:-0500
TZOFFSETTO:-0400
TZNAME:EDT
DTSTART:20270314T070000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0400
TZOFFSETTO:-0500
TZNAME:EST
DTSTART:20271107T060000
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20260219T160000
DTEND;TZID=America/New_York:20260219T170000
DTSTAMP:20260408T023505
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/
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:20260408T023505
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/
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:20260408T023505
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/
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:20260408T023505
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/
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:20260408T023505
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/
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:20260408T023505
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/
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:20260408T023505
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/
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:20260408T023505
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/
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/jpeg:https://ds.dfci.harvard.edu/wp-content/uploads/2026/04/vr1_WebRez_Headshots_Harvard_Human-Flourishing-Program_OVRLD.studio-47-e1775221700850.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20260416T160000
DTEND;TZID=America/New_York:20260416T170000
DTSTAMP:20260408T023505
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/
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/jpeg:https://ds.dfci.harvard.edu/wp-content/uploads/2026/04/temp3-e1775222052947.jpeg
END:VEVENT
END:VCALENDAR