Data Management Seminar: Fuheng Zhao, Advancing Approximate Queries with Innovative Data Summaries and Generative Models
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Advancing Approximate Queries with Innovative Data Summaries and Generative Models
Speaker: Fuheng Zhao
Abstract: The exponential growth of data has introduced significant challenges for traditional query processing systems, creating a pressing need for faster and more resource-efficient approaches. Approximation techniques have emerged as a promising solution, striking an optimal balance between accuracy and performance. Data summaries, such as samples, sketches, and histograms, play a crucial role in this paradigm by condensing large datasets into compact representations and maintaining critical insights. Additionally, recent advances in generative models (including large language models) open new possibilities for handling incomplete information, accommodating diverse data types, and approximating complex computations at scale. In this talk, I will discuss my research on theoretically grounded data summarization methods, as well as my latest efforts to integrate generative models into data systems. Together, these contributions advance approximate query processing toward realistic, high-impact applications in modern data analytics.
Bio: Fuheng Zhao is a PhD candidate at the University of California Santa Barbara, advised by Professor Divyakant Agrawal and Professor Amr El Abbadi. His research has been recognized at top database and machine learning conferences such as VLDB, NeurIPS, and CIDR. He is a recipient of the Microsoft PhD Fellowship, the Charles Dana Fellowship, and was honored with the Outstanding Paper Award from UCSB’s Computer Science Department.