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UID:20250512T193604EDT-58516kCzra@132.216.98.100
DTSTAMP:20250512T233604Z
DESCRIPTION:Data and machine learning in population and public health\n\nRu
 mi Chunara (NYU)\n	Tuesday November 12\, 12-1pm\n	McIntyre Medical Building\
 , Room 1034\n\nAbstract: New unstructured Internet and mobile-sourced data
  from satellite imagery to precisely-geolocated text and images from mobil
 e phones can provide high-resolution spatial and temporal views into daily
  life factors and prevention efforts important in public health. The low-f
 requency\, low-resolution data commonly used (e.g. surveys\, government re
 ports) only provide low-precision behavior and outcome measures on the spe
 cific populations sampled. This limits how effectively we can spend public
  health budgets and target intervention efforts. While it is clear that ne
 w data sources have the potential to transform public health efforts\, the
 re are critical computational and statistical challenges to making the dat
 a useful in aggregate\, including non-continuous data shared whenever a pe
 rson likes\, noisy/unstructured observations that may not spatially repres
 entative of the underlying (latent) process\, and data generated by ad-hoc
 \, non-representative groups. In this talk I will elucidate such challenge
 s and work towards addressing them relevant to public health priorities. E
 xamples will span local and global efforts both in communicable and non-co
 mmunicable disease.\n
DTSTART:20191112T170000Z
DTEND:20191112T180000Z
LOCATION:Room 1034\, McIntyre Medical Building\, CA\, QC\, Montreal\, H3G 1
 Y6\, 3655 promenade Sir William Osler
SUMMARY:QLS Seminar Series - Rumi Chunara
URL:/qls/channels/event/qls-seminar-series-rumi-chunar
 a-302068
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