BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//Drupal iCal API//EN
X-WR-CALNAME:Events items teaser
X-WR-TIMEZONE:America/Toronto
BEGIN:VTIMEZONE
TZID:America/Toronto
X-LIC-LOCATION:America/Toronto
BEGIN:DAYLIGHT
TZNAME:EDT
TZOFFSETFROM:-0500
TZOFFSETTO:-0400
DTSTART:20240310T070000
END:DAYLIGHT
BEGIN:STANDARD
TZNAME:EST
TZOFFSETFROM:-0400
TZOFFSETTO:-0500
DTSTART:20231105T060000
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
UID:69d934239c39e
DTSTART;TZID=America/Toronto:20240613T160000
SEQUENCE:0
TRANSP:TRANSPARENT
DTEND;TZID=America/Toronto:20240613T170000
URL:https://uwaterloo.ca/statistics-and-actuarial-science/events/david-spro
 tt-distinguished-lecture-bhramar-mukherjee
LOCATION:DC - William G. Davis Computer Research Centre 200 University Aven
 ue West Room: DC 1302 Waterloo ON N2L 3G1 Canada
SUMMARY:David Sprott Distinguished Lecture by Bhramar Mukherjee
CLASS:PUBLIC
DESCRIPTION:Distinguished Lecture Series\n\nBHRAMAR MUKHERJEE\n_John D Kalb
 fleisch Distinguished University Professor of\nBiostatistics\nSiobán D. H
 arlow Collegiate Professor of Public Health\nChair of the Department of Bi
 ostatistics\nUniversity of Michigan_\n\nRoom: DC 1302\n\n-----------------
 --------\n\nTHE DATA STRUGGLE OF THE UNSEEN\n\nDespite several proposed ro
 admaps to increase diversity in scientific\nresearch\, most of the world's
  research data are collected on people of\nEuropean ancestry. We rely on s
 ummary statistics from historically\nprivileged populations and then devis
 e clever statistical methods to\ntransfer/transport them for cross-ancestr
 y use. In this talk\, I would\nfirst argue the obvious: for building fair 
 algorithms we need fair\ntraining datasets. However\, till we have reached
  the dream of\nequitable big data at a global scale\, statisticians have a
 n important\nrole to play. In fact we have the perfect tools to study the\
 n\"unobserved\" through modeling of missing data\, selection bias and\nali
 ke.  I will share examples from my personal journey as a\nstatistician wh
 ere doing good and timely statistical work with\nimperfect data quantified
  important disparity in health outcomes and\n led to policy impact. I wil
 l conclude the talk with a call to arms\nfor statisticians to lead efforts
  for creating\, curating\, collecting\ndata and pioneering new scientific 
 studies\, not just remain on the\ndesign and analytic fringes. As public h
 ealth statisticians\, our job\nis not just to predict\, but to prevent. Th
 e talk is based on years of\nwork with my students and colleagues at the D
 epartment of\nBiostatistics\, University of Michigan and inspired by the\n
 transformative experience we shared as a statistical team working on\nthe 
 COVID-19 pandemic.
DTSTAMP:20260410T173219Z
END:VEVENT
END:VCALENDAR