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DTSTART;TZID=America/Toronto:20191017T160000
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URL:https://uwaterloo.ca/statistics-and-actuarial-science/events/david-spro
 tt-distinguished-lecture-xiao-li-meng-harvard
LOCATION:STC - Science Teaching Complex 200 University Avenue West Room: 00
 50 Waterloo ON N2L 3G1 Canada
SUMMARY:David Sprott Distinguished Lecture by Xiao-Li Meng\, Harvard Univer
 sity
CLASS:PUBLIC
DESCRIPTION:BUILDING DEEP STATISTICAL THINKING FOR DATA SCIENCE 2020: PRIVA
 CY\nPROTECTED CENSUS\, GERRYMANDERING\, AND ELECTION\n\n------------------
 -------\n\nThe year 2020 will be a busy one for statisticians and more gen
 erally\ndata scientists.  The US Census Bureau has announced that the dat
 a\nfrom the 2020 Census will be released under differential privacy (DP)\n
 protection\, which in layperson’s terms means adding some noises to\nthe
  data.  While few would argue against protecting data privacy\,\nmany res
 earchers\, especially from the social sciences\, are concerned\nwhether th
 e right trade-offs between data privacy and data utility are\nbeing made. 
 The DP protection also has direct impact on redistricting\,\nan issue that
  is already complicated enough with accurate counts\, due\nto the need of 
 guarding against excessive gerrymandering.  The\ncentral statistical prob
 lem there is a rather unique one:  how to\ndetermine whether a realizatio
 n is an outlier with respect to a null\ndistribution\, when that null dist
 ribution itself cannot be fully\ndetermined?  The 2020 US election will b
 e another highly watched\nevent\, with many groups already busy making pre
 dictions. Will the\nlessons from predicting the 2016 US election be learne
 d\, or the\nfailure be repeated?  This talk invites the audience on a jou
 rney of\ndeep statistical thinking prompted by these questions\, regardles
 s\nwhether they have any interest in the US Census or politics.\n\n-------
 ------------------
DTSTAMP:20260410T173226Z
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