Purdue University Graduate School
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Computing and Learning on Combinatorial Data

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posted on 2025-01-28, 19:07 authored by Simon ZhangSimon Zhang

The twenty-first century is a data-driven era where human activities and behavior, physical phenomena, scientific discoveries, technology advancements, and almost everything that happens in the world resulting in massive generation, collection, and utilization of data.

Connectivity in data is a crucial property. A straightforward example is the World Wide Web, where every webpage is connected to other web pages through hyperlinks, providing a form of directed connectivity. Combinatorial data refers to combinations of data items based on certain connectivity rules. Other forms of combinatorial data include social networks, meshes, community clusters, set systems, and molecules.

This Ph.D. dissertation focuses on learning and computing with combinatorial data. We study and examine topological and connectivity features within and across connected data to improve the performance of learning and achieve high algorithmic efficiency.

History

Degree Type

  • Doctor of Philosophy

Department

  • Computer Science

Campus location

  • West Lafayette

Advisor/Supervisor/Committee Chair

Tamal K. Dey

Additional Committee Member 2

David Gleich

Additional Committee Member 3

Bruno Ribeiro

Additional Committee Member 4

Saugata Basu

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