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<span style="color: rgb(40, 109, 192);"><u><a href="https://statistics.yale.edu/" id="OWAfa45974a-c1f9-7386-e1e9-8a01a8dfb395" class="OWAAutoLink" title="Department of Statistics and Data Science" data-auth="NotApplicable" style="color: rgb(40, 109, 192); margin: 0px;"><img alt="Department of Statistics and Data Science" id="x_logo" width="150" height="48" style="width: 1.5625in; height: 0.5104in; max-width: 553px; margin: 0px;" data-outlook-trace="F:1|T:1" src="cid:c273523d-1a59-4806-95ea-9241dd32b842"></a></u></span>
<span style="color: rgb(40, 109, 192);"><b><u><a href="https://statistics.yale.edu/" id="OWA1f35deeb-33f3-dc06-ca4f-1d3123677dc1" class="OWAAutoLink" title="Home" data-auth="NotApplicable" style="color: rgb(40, 109, 192); margin: 0px;">Department of Statistics
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<span style="line-height: normal;"><b>Eric Sun, Stanford University</b></span></div>
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Date: Monday, January 27, 2025</div>
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Time: 12:00PM to 1:00PM</div>
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Location: Kline Tower, 13th Floor, Rm. 1327A <span style="color: rgb(70, 120, 134);"><u><a href="http://maps.google.com/?q=219+Prospect+Street%2C+New+Haven%2C+CT%2C+06511%2C+us" id="OWA05e4e081-f158-de51-e77c-2d6f00975998" class="OWAAutoLink" data-auth="NotApplicable" style="color: rgb(70, 120, 134);">See
map</a></u></span> </div>
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219 Prospect Street</div>
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New Haven, CT 06511</div>
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Webcast: <u><a href="https://yale.hosted.panopto.com/Panopto/Pages/Viewer.aspx?id=092a7568-7c71-47b0-b0da-b233012bcd25" target="_blank" id="LPlnkOWAfaec4dfd-f68c-4790-53c7-23dfaf115220" class="in-cell-link OWAAutoLink">https://yale.hosted.panopto.com/Panopto/Pages/Viewer.aspx?id=092a7568-7c71-47b0-b0da-b233012bcd25</a></u></div>
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<b>Machine Learning For Aging And Spatial Omics</b></div>
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Information and Abstract: </div>
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Aging is a highly complex process and the greatest risk factor for many chronic diseases including cardiovascular disease, dementia, stroke, diabetes, and cancer. Recent spatial and single-cell omics technologies have enabled the high-dimensional profiling
of complex biology including that underlying aging. As such, new machine learning and computational methods are needed to unlock important insights from spatial and single-cell omics datasets. First, I present the development of high-resolution machine learning
models (‘spatial aging clocks’) that can measure the aging of individual cells in the brain. Using these spatial aging clocks, I discovered that some cell types can dramatically influence the aging of nearby cells. Next, I present new computational and statistical
methods for overcoming the gene coverage limitations of existing spatial omics technologies, which have enabled the discovery of gene pathways underlying the spatial effects of brain aging. Finally, I introduce several methods for improving the reliability
and robustness of high-dimensional data visualizations.</div>
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Lunch at 11:30am in room 1307<br>
Talk at 12:00-1:00pm in room 1327A</div>
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<i> </i></div>
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For more details and upcoming events visit our website at <span style="color: rgb(70, 120, 134);">
<u><a href="https://statistics.yale.edu/calendar" id="OWAd4ac1fa0-0cd5-5189-f8d9-d74aa7590488" class="OWAAutoLink" data-auth="NotApplicable" style="color: rgb(70, 120, 134); margin: 0px;">https://statistics.yale.edu/calendar</a></u></span>.</div>
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Department of Statistics and Data Science</div>
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Yale University<br>
Kline Tower</div>
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219 Prospect Street<br>
New Haven, CT 06511</div>
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