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  <title><![CDATA[MS Defense by Xuzheng Tian]]></title>
  <body><![CDATA[<p><span><span><span><strong><span>THE SCHOOL OF MATERIALS SCIENCE AND ENGINEERING</span></strong></span></span></span></p>

<p><span><span><span><strong><span>GEORGIA INSTITUTE OF TECHNOLOGY</span></strong></span></span></span></p>

<p><br />
<span><span><span><span>Under the provisions of the regulations for the degree<br />
MASTER OF SCIENCE</span></span></span></span></p>

<p>&nbsp;</p>

<p><span><span><span><strong><span>on Wednesday, May 17th, 2023</span></strong></span></span></span></p>

<p><span><span><span><strong><span>1:00 PM EST</span></strong></span></span></span></p>

<p><span><span><span><span>via</span></span></span></span></p>

<p><span><span><span><span>Zoom</span></span></span></span></p>

<p>&nbsp;</p>

<p><span><span><span><span><span><a href="https://gatech.zoom.us/j/96007148801?pwd=ZmQzS2xCK0dmRmdWUjgzMERtWWNiZz09">https://gatech.zoom.us/j/96007148801?pwd=ZmQzS2xCK0dmRmdWUjgzMERtWWNiZz09</a></span></span></span></span></span></p>

<p>&nbsp;</p>

<p><span><span><span><span>Meeting ID:&nbsp;</span><span>960 0714 8801</span></span></span></span></p>

<p><span><span><span><span>Passcode:&nbsp;</span><span>911127</span></span></span></span></p>

<p><span><span><span>&nbsp;</span></span></span></p>

<p><span><span><span><span>will be held the</span></span></span></span></p>

<p><span><span><span><span>MASTER’S THESIS DEFENSE</span><br />
<span>for<br />
Xuzheng Tian<br />
&nbsp;</span></span></span></span></p>

<p><span><span><span><span>“<strong>Machine Learning Helps to Build Drug Release Kinetic Models</strong>”</span></span></span></span></p>

<p><br />
<span><span><span><span>&nbsp;<br />
&nbsp; Committee Members:</span></span></span></span></p>

<p><span><span><span><span>Dr. Karl I. Jacob</span></span></span></span></p>

<p><span><span><span><span>Dr. Youjiang Wang</span><br />
<span>Dr. Donggang Yao</span></span></span></span></p>

<p><span><span><span><span>Dr. </span><span><span>Hamid&nbsp;</span></span><span>Garmestani </span></span></span></span></p>

<p>&nbsp;</p>

<p><span><span><span>&nbsp;</span></span></span></p>

<p><span><span><span><span>&nbsp;<strong>Abstract</strong>: </span></span></span></span></p>

<p><span><span><span>Long-acting injectables (LAI) are one of the most promising drug delivery systems for the treatment of chronic diseases. Since they can maintain the drug concentration in the target tissue, thus reducing dose frequency and adverse effects as well as improving patient compliance. The use of polymer matrices delivery systems shows an extraordinary diversity in drug development research. But due to the time-consuming experiments and complicated drug release mechanisms, the efficiency of LAI development is strongly restricted.&nbsp;</span></span></span></p>

<p><span><span><span>This thesis used machine learning to predict the long-period in vitro test profiles based on the datasets collected from published literature. In addition to comparing the accuracy performance of different machine learning algorithms, a combination of empirical mathematic models and machine learning algorithms is further studied in the case to improve the model evaluability.&nbsp;</span></span></span></p>
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