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DTSTART;TZID=America/New_York:20250911T160000
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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
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DTSTART;TZID=America/New_York:20250918T160000
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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
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DTSTART;VALUE=DATE:20250919
DTEND;VALUE=DATE:20250920
DTSTAMP:20260415T041115
CREATED:20250721T165244Z
LAST-MODIFIED:20250922T133650Z
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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/
CATEGORIES:Symposium
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