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<oembed><version>1.0</version><provider_name>Department of Computer Science</provider_name><provider_url>https://www.cs.jhu.edu</provider_url><title>Computer Science Student Defense: Peter Schulam, Johns Hopkins University &#x2013; &#x201C;Probabilistic Models for Exploring, Predicting, and Influencing Health Trajectory Data&#x201D; - Department of Computer Science</title><type>rich</type><width>600</width><height>338</height><html>&lt;blockquote class="wp-embedded-content"&gt;&lt;a href="https://www.cs.jhu.edu/event/computer-science-student-defense-peter-schulam-johns-hopkins-university-probabilistic-models-for-exploring-predicting-and-influencing-health-trajectory-data/"&gt;Computer Science Student Defense: Peter Schulam, Johns Hopkins University &#x2013; &#x201C;Probabilistic Models for Exploring, Predicting, and Influencing Health Trajectory Data&#x201D;&lt;/a&gt;&lt;/blockquote&gt;
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&lt;/script&gt;&lt;iframe sandbox="allow-scripts" security="restricted" src="https://www.cs.jhu.edu/event/computer-science-student-defense-peter-schulam-johns-hopkins-university-probabilistic-models-for-exploring-predicting-and-influencing-health-trajectory-data/embed/" width="600" height="338" title="&#x201C;Computer Science Student Defense: Peter Schulam, Johns Hopkins University &#x2013; &#x201C;Probabilistic Models for Exploring, Predicting, and Influencing Health Trajectory Data&#x201D;&#x201D; &#x2014; Department of Computer Science" frameborder="0" marginwidth="0" marginheight="0" scrolling="no" class="wp-embedded-content"&gt;&lt;/iframe&gt;</html><description>LocationMalone 228AbstractWe present novel probabilistic models for exploring, predicting, and controlling health trajectory data. The models address two important challenges that we must face when learning from health trajectory data. Solutions to these two challenges are the unifying arc of the thesis. First, we must account for unexplained heterogeneity. In many diseases, two individuals with&hellip;</description></oembed>
