School of Computing Seminar with Federico Iuricich, Clemson University
"Towards the analysis of multidimensional data through topology-based techniques"
Topological Data Analysis (TDA) is an active research area providing robust and data-agnostic tools for analyzing and visualizing data. The idea at the base of TDA is that of extracting compact descriptors, called topological features, carrying structural information about data. The most widely used tool in TDA is persistent homology which provides a way to compute topological features in a multi-scale manner and to summarize them in the so-called persistence diagram. Very recently, a growing community is focusing on incorporating persistence diagrams in classic machine learning pipelines. From a learning perspective, the use of topological descriptors is beneficial for multiple reasons. First, topological descriptors are data agnostic, i.e. they can be computed on different types of data such as images, meshes, graphs or high dimensional point clouds. Second, topological descriptors are robust to noise, which is a necessary property for a descriptor to be used with real data. In this talk, I will introduce the key aspects behind TDA and I will explain how it can be used for data analysis and visualization. Then, I will describe the current challenges in developing effective topological approaches for the analysis of multidimensional data. I will conclude presenting an overview of our ongoing projects on the topology-based analysis of medical, geospatial and environmental data.
Federico Iuricich is currently an Assistant Professor in Visual Computing within the School of Computing at Clemson. His primary research interest lies in topological methods for the analysis and visualization of complex data. He received his Ph.D. in Computer Science from the University of Genova in 2014 defending a thesis on “Multi-resolution shape analysis based on discrete Morse decompositions”. Since 2014, he has been a post-doctoral fellow at the University of Maryland working in both the Computer Science and the Geographical Sciences department and in 2018 he has been a visiting researcher at Queen’s College (CUNY) working on applications of computational topology to machine learning.
Friday, November 30 at 2:30pm
McAdams Hall, 119
821 McMillan Rd., Clemson, SC 29634, USA