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Dear all,<br>
<br>
On behalf of The McGill initiative in Computational Medicine (MiCM) we are pleased to invite Dr. Rumi Chunara, PhD, Assistant Professor, NYU Computer Science & Engineering, NYU College of Global Public Health (joint appointment), NYU Center for Data Sciences (affiliate),
Center for Urban Science and Progress (affiliate).<br>
<br>
<strong><span style="font-family:Helvetica">McGill's Seminar Series in Quantitative Life Sciences and Medicine</span></strong><b><br>
<strong><span style="font-family:Helvetica">Sponsored by CAMBAM, QLS, MiCM and the Ludmer Centre</span></strong></b><br>
</span></p>
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<strong><span style="font-size:12.0pt; line-height:150%; font-family:Helvetica; color:#202020">Title:</span></strong><span style="font-size:12.0pt; line-height:150%; font-family:Helvetica; color:#202020"> Data and machine learning in population and public health<br>
<strong><span style="font-family:Helvetica">Speaker:</span></strong> Dr. Rumi Chunara<br>
<strong><span style="font-family:Helvetica">When: </span></strong>Tuesday, November 12, 2019 (12-1pm)<br>
<strong><span style="font-family:Helvetica">Where:</span></strong> McIntyre Medical Sciences Building,<br>
room 1034, McGill University </span></p>
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<strong><span style="font-size:12.0pt; line-height:150%; font-family:Helvetica; color:#202020">Abstract:</span></strong><span style="font-size:12.0pt; line-height:150%; font-family:Helvetica; color:#202020"></span></p>
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<span style="font-size:12.0pt; line-height:150%; font-family:Helvetica; color:#202020">New unstructured Internet and mobile-sourced data from satellite imagery to precisely-geolocated text and images from mobile phones can provide high-resolution spatial and
temporal views into daily life factors and prevention efforts important in public health. The low-frequency, low-resolution data commonly used (e.g. surveys, government reports) only provide low-precision behavior and outcome measures on the specific populations
sampled. This limits how effectively we can spend public health budgets and target intervention efforts. While it is clear that new data sources have the potential to transform public health efforts, there are critical computational and statistical challenges
to making the data useful in aggregate, including non-continuous data shared whenever a person likes, noisy/unstructured observations that may not spatially representative of the underlying (latent) process, and data generated by ad-hoc, non-representative
groups.<br>
<br>
In this talk I will elucidate such challenges and work towards addressing them relevant to public health priorities. Examples will span local and global efforts both in communicable and non-communicable disease.</span></p>
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<span style="font-size:9.0pt; line-height:150%; font-family:Helvetica; color:#656565">For more information:<a href="https://mcgill.us19.list-manage.com/track/click?u=b1e9a3f512a2eb13406ade675&id=323f34fe33&e=c114290167" target="_blank"><span style="color:#007C89"> https://mcgill.ca/micm/events/fall-2019-qls</span></a></span></p>
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