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<p class="MsoNormal"><span style="font-size:12.0pt;font-family:"Arial",sans-serif;color:black;mso-ligatures:none">This is an announcement of <span style="letter-spacing:.15pt;background:white">Zhuokai Zhao’s Dissertation Defense</span><br>
===============================================<br>
<b>Candidate: </b><span style="letter-spacing:.15pt;background:white">Zhuokai Zhao’s</span><br>
<br>
<b>Date:</b> Monday, May 13th, 2024</span><span style="font-size:12.0pt;font-family:"Arial",sans-serif"><o:p></o:p></span></p>
<p class="MsoNormal"><span style="font-size:12.0pt;font-family:"Arial",sans-serif"><o:p> </o:p></span></p>
<p class="MsoNormal"><b><span style="font-size:12.0pt;font-family:"Arial",sans-serif">Time:</span></b><span style="font-size:12.0pt;font-family:"Arial",sans-serif"> 9 am CT<o:p></o:p></span></p>
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<p class="MsoNormal"><b><span style="font-size:12.0pt;font-family:"Arial",sans-serif">Location:</span></b><span style="font-size:12.0pt;font-family:"Arial",sans-serif"> JCL 298<o:p></o:p></span></p>
<p class="MsoNormal"><span style="font-size:12.0pt;font-family:"Arial",sans-serif"><o:p> </o:p></span></p>
<p class="MsoNormal"><b><span style="font-size:12.0pt;font-family:"Arial",sans-serif">Title:</span></b><span style="font-size:12.0pt;font-family:"Arial",sans-serif"> Enhanced Data Utilization for Efficient and Trustworthy Deep Learning<o:p></o:p></span></p>
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<p class="MsoNormal"><b><span style="font-size:12.0pt;font-family:"Arial",sans-serif">Abstract:</span></b><span style="font-size:12.0pt;font-family:"Arial",sans-serif"> Deep learning (DL) has made significant impacts in many domains, including computer vision
(CV), natural language processing (NLP), recommender systems, and many others. Besides the breakthroughs made to the model architectures, data has been another fundamental factor that significantly impacts the model performance. This emphasis on data has given
rise to the concept of data-centric artificial intelligence (AI). Despite its growing importance, studies focusing on developing novel data utilization algorithms that enhance model performance without modifying its architecture are still lacking. Addressing
this gap, this thesis proposes novel data utilization algorithms that correspond to different steps of the deep learning pipeline, ranging from data collection, formulation, to model training, evaluation, and to model inference as in many deployed applications.
These algorithms aim to improve model performance, robustness, and trustworthiness through the lens of data utilization, while not altering model architectures or increasing computational or time costs.<o:p></o:p></span></p>
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<p class="MsoNormal"><b><span style="font-size:12.0pt;font-family:"Arial",sans-serif">Advisor:</span></b><span style="font-size:12.0pt;font-family:"Arial",sans-serif"> Yuxin Chen<o:p></o:p></span></p>
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<p class="MsoNormal"><b><span style="font-size:12.0pt;font-family:"Arial",sans-serif">Committee Members:</span></b><span style="font-size:12.0pt;font-family:"Arial",sans-serif"> Yuxin Chen, Bo Li, and Michael Maire<o:p></o:p></span></p>
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