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<p class="MsoNormal"><b><span style="font-size:12.0pt;color:black">Title</span></b><span style="font-size:12.0pt;color:black">: Learning-Based High-Dimensional Query Processing<o:p></o:p></span></p>
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<p class="MsoNormal"><b><span style="font-size:12.0pt;color:black">Abstract</span></b><span style="font-size:12.0pt;color:black">: With the emergence of deep learning techniques, more and more applications are utilizing neural embeddings which are high-dimensional
numeric vector representations. A recent example is the retrieval-augmented generation (RAG) for Large Language Model. Accordingly, efficient and effective query processing over high-dimensional vectors is demanded. However, the performance of traditional
methods has approached the bottleneck with the rapid increase of data scale. In this talk, Yifan will introduce his studies that apply machine learning techniques to speeding up commonly used high-dimensional data operations, as well as his future research
plans.<o:p></o:p></span></p>
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<p class="MsoNormal"><b><span style="font-size:12.0pt;color:black">Bio</span></b><span style="font-size:12.0pt;color:black">: Yifan Wang is a final-year PhD student in the Department of Computer & Information Science & Engineering at the University of Florida.
His primary research interests focus on database systems, high-dimensional data management, information retrieval and application of AI techniques in these areas. His work has been published on top-tier database and data science conferences.</span><o:p></o:p></p>
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