BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//Dana-Farber Cancer Institute - ECPv6.15.20//NONSGML v1.0//EN
CALSCALE:GREGORIAN
METHOD:PUBLISH
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:20240310T070000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0400
TZOFFSETTO:-0500
TZNAME:EST
DTSTART:20241103T060000
END:STANDARD
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;VALUE=DATE:20250919
DTEND;VALUE=DATE:20250920
DTSTAMP:20260412T185324
CREATED:20250721T165244Z
LAST-MODIFIED:20250922T133650Z
UID:6268-1758240000-1758326399@ds.dfci.harvard.edu
SUMMARY:Call for Abstracts: DF/HCC Early Career Investigators Symposium
DESCRIPTION:2025 Dana-Farber/Harvard Cancer Center Celebration of Early Career Investigators\nNovember 4\, 2025 from 1:00-5:30PM\nIn-Person at Dana Farber Cancer Institute\nYawkey Conference Center \nDo you work in population science\, including epidemiology\, biostatistics\, outcomes\, diversity\, cancer care delivery research\, and early detection? We invite students\, postdocs\, residents\, and clinical fellows to submit abstracts for consideration as a short talk or as part of the PATHFINDER sponsored poster presentation. \nhttps://bit.ly/ecis2025abstract\nSubmit your abstract by September 19 \n2025 KEYNOTE SPEAKER\nJane J Kim\nDean for Academic Affairs at the Harvard T.H. Chan School of Public Health\nVisit Profile \n  \nRegistration for the public now open at https://bit.ly/ECIS2025
URL:https://ds.dfci.harvard.edu/event/call-for-abstracts-df-hcc-early-career-investigators-symposium/
LOCATION:MA
CATEGORIES:Symposium
ATTACH;FMTTYPE=image/png:https://ds.dfci.harvard.edu/wp-content/uploads/2024/01/logo-only-e1704401329917.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20251002T160000
DTEND;TZID=America/New_York:20251002T170000
DTSTAMP:20260412T185324
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:20260412T185324
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:20260412T185324
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:20251104T130000
DTEND;TZID=America/New_York:20251104T173000
DTSTAMP:20260412T185324
CREATED:20250922T133637Z
LAST-MODIFIED:20251105T124930Z
UID:6532-1762261200-1762277400@ds.dfci.harvard.edu
SUMMARY:2025 Dana-Farber/Harvard Cancer Center Celebration of Early Career Investigators
DESCRIPTION:November 4\, 2025 from 1:00-5:30PM\nIn-Person at Dana Farber Cancer Institute\nYawkey Conference Center \nEarly career investigators are a unique reservoir of new ideas\, innovation\, and excellence in cancer research. To celebrate this\, we welcome you to join the 13th Annual DF/HCC Celebration of Early Career Investigators in Cancer Research. This symposium will showcase the talent of early career investigators at the Dana-Farber/Harvard Cancer Center (DF/HCC) who work in several areas of population science\, including epidemiology\, biostatistics\, outcomes\, diversity\, and survivorship. We invite all members of the public to attend the event. \n2025 KEYNOTE SPEAKER\nJane J Kim\, PhD\nKT Li Professorship in Health Economics at the Harvard TH Chan School of Public Health \nTalk Title: Cervical Cancer Prevention: The Quintessential and Persistent Public Health Challenge \nAbstract: Effective and cost-effective preventive strategies against cervical cancer have been available for several decades. HPV vaccination and cervical cancer screening hold such promise that the World Health Organization Director General issued a call for the global elimination of cervical cancer as a public health problem in 2018. Despite these available options\, cervical cancer remains hugely burdensome with wide disparities in incidence and mortality around the globe\, and even within the United States. \nThis talk chronicles the progress towards cervical cancer elimination over the past two decades and the many remaining challenges. I discuss this quintessential public health problem from the lens of decision sciences as an approach to evidence-based decision making that has informed policy recommendations for HPV vaccination and cervical cancer screening in the United States and globally. Professional development and lessons learned through research collaborations\, which were found to be portable to leadership roles more broadly\, are shared. \nRegistration for the public is now open at https://bit.ly/ECIS2025 \nMore info at: https://www.dfhcc.harvard.edu/ecis
URL:https://ds.dfci.harvard.edu/event/2025-dana-farber-harvard-cancer-center-celebration-of-early-career-investigators/
LOCATION:MA
CATEGORIES:Symposium
ATTACH;FMTTYPE=image/png:https://ds.dfci.harvard.edu/wp-content/uploads/2024/01/logo-only-e1704401329917.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20251106T160000
DTEND;TZID=America/New_York:20251106T170000
DTSTAMP:20260412T185324
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/
LOCATION:MA
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:20260412T185324
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:20260412T185324
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:20251218T110000
DTEND;TZID=America/New_York:20251218T120000
DTSTAMP:20260412T185324
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/
LOCATION:MA
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:20260412T185324
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:20260412T185324
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:20260412T185324
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:20260412T185324
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:20260412T185324
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:20260412T185324
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:20260412T185324
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:20260412T185324
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:20260412T185324
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:20260412T185324
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:20260412T185324
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/
LOCATION:MA
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:20260412T185324
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/
LOCATION:MA
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/jpeg:https://ds.dfci.harvard.edu/wp-content/uploads/2026/04/temp3-e1775222052947.jpeg
END:VEVENT
END:VCALENDAR