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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:20251106T160000
DTEND;TZID=America/New_York:20251106T170000
DTSTAMP:20260413T032738
CREATED:20251031T155331Z
LAST-MODIFIED:20251107T191723Z
UID:6628-1762444800-1762448400@ds.dfci.harvard.edu
SUMMARY:Statistical Challenges in Long COVID Research: Examples from the RECOVER Cohort Studies
DESCRIPTION:HSPH Biostatistics & DFCI Data Science Colloquium Seminar Series \nNovember 6\, 2025 at 4:00pm\nHSPH FXB-301 \nAndrea Foulkes\, ScD\, Director\, Biostatistics\, Professor of Medicine\, Harvard Medical School\nProfessor\, Department of Biostatistics\, Harvard T.H. Chan School of Public Health \nA considerable proportion of individuals with a history of SARS-COV-2 infection experience persistent and often debilitating symptoms\, varying from cardiopulmonary and neuro-cognitive symptoms to myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) and dysautonomia.\nThese symptomologies\, collectively known as Long COVID\, manifest differently across individuals\, wax and wane over time\, and range from mild to incapacitating\, with profound effects on quality of life. At the same time\, the rapid materialization of large-scale observational data\, including the Researching COVID to Enhance Recovery (RECOVER) meta-cohort\, has MASSACHUSETTS GENERAL HOSPITAL generated enormous opportunity for novel discovery and enhanced clinical decision-making tools that could lead to effective prevention strategies and improved outcomes. The statistical challenges inherent in effectively and appropriately leveraging these novel data resources are numerous. In this talk\, I discuss my role as the Pl of the Data Resource Core for RECOVER and highlight some of the specific statistical challenges in the study of Long COVID. \n 
URL:https://ds.dfci.harvard.edu/event/statistical-challenges-in-long-covid-research-examples-from-the-recover-cohort-studies/
CATEGORIES:Symposium
ATTACH;FMTTYPE=image/png:https://ds.dfci.harvard.edu/wp-content/uploads/2025/10/andreaf_crop-copy.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20251113T160000
DTEND;TZID=America/New_York:20251113T170000
DTSTAMP:20260413T032738
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/
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:20260413T032738
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/
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:20251218T110000
DTEND;TZID=America/New_York:20251218T120000
DTSTAMP:20260413T032738
CREATED:20251125T173544Z
LAST-MODIFIED:20251125T173544Z
UID:6686-1766055600-1766059200@ds.dfci.harvard.edu
SUMMARY:Data Science Postdoctoral Fellows Program - Information Session
DESCRIPTION:The Department of Data Science at Dana-Farber Cancer Institute is thrilled to announce our third annual Data Science Postdoctoral Fellows Program. We invite recent Ph.D. graduates and doctoral candidates who are nearing graduation and have a passion for applied statistics\, machine learning\, or computational biology to consider this exciting opportunity. \nThis is an incredible opportunity to advance your career in data science while making a meaningful impact on cancer research. We encourage you to join us at the Informational Session on December 18th from 11am-12pm EST to learn more about how you can be a part of our dynamic research community. Please visit our postdoctoral page to register & more information: https://ds.dfci.harvard.edu/postdocs
URL:https://ds.dfci.harvard.edu/event/data-science-postdoctoral-fellows-program-information-session/
CATEGORIES:Recruitment
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:20260130T080000
DTEND;TZID=America/New_York:20260130T170000
DTSTAMP:20260413T032738
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/
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:20260413T032738
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/
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:20260413T032738
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/
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:20260413T032738
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:20260413T032738
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:20260413T032738
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:20260413T032738
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:20260413T032738
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:20260413T032738
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:20260413T032738
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:20260413T032738
CREATED:20260403T130934Z
LAST-MODIFIED:20260410T141349Z
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:20260413T032738
CREATED:20260403T131453Z
LAST-MODIFIED:20260410T141330Z
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
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