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DTSTART;TZID=America/New_York:20251002T160000
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DTSTAMP:20260418T073151
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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
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DTSTART;TZID=America/New_York:20251009T160000
DTEND;TZID=America/New_York:20251009T170000
DTSTAMP:20260418T073151
CREATED:20251001T170830Z
LAST-MODIFIED:20251014T115427Z
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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
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DTSTART;TZID=America/New_York:20251016T160000
DTEND;TZID=America/New_York:20251016T170000
DTSTAMP:20260418T073151
CREATED:20251014T115412Z
LAST-MODIFIED:20251017T111716Z
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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/
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/png:https://ds.dfci.harvard.edu/wp-content/uploads/2025/10/Rajarshi.png
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