Topological data analysis (TDA) is a young and quickly-developing area of research at the intersection of mathematics, statistics, and computer science. In recent years, TDA has attracted widespread interest, not only from mathematicians, but also from scientists looking for new tools to analyze ever-increasing amounts of complex data in areas including neuroscience, digital imaging, genetics, biological aggregations, sensor networks, and cancer research. The intellectual appeal of TDA arises from its unique combination of advanced mathematics, cutting-edge algorithms, and practical applications. Yet, despite its mathematical sophistication, TDA methodology is surprisingly intuitive and lends itself well to research with students, even at the undergraduate level. This conference will introduce attendees to both the mathematical foundations and practical computational techniques of TDA.
This conference seeks to include faculty and students from liberal-arts institutions. This conference will equip attendees with not only the theoretical framework of TDA, but also practical computational tools, providing points of entry so that faculty and students from diverse settings can begin research in topological data analysis. The conference will spur new research collaborations between institutions and across disciplines.
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