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Engaging interview with mathematician Kathryn Hess, exploring her research, insights, and contributions to applied algebraic topology and related fields.
Explore parallel decomposition of persistence modules using interval bases, enhancing Topological Data Analysis with distributed computation and Hodge decomposition techniques.
Exploring neural network computations on cell complexes, generalizing graph neural networks and introducing inter-cellular message passing schemes that consider underlying topology.
Explores TDA-based models for effective molecular representation in drug design, combining persistent homology, spectral models, and Ricci curvature with machine learning for improved performance.
Explore the intersection of string theory, geometry, and machine learning in physics, examining how AI aids mathematical research across various fields.
Explore connections between Rips complexes, projective codes, and odd map zeros, bridging discrete geometry and topology. New insights into sphere Rips complexes' challenging nature.
Explore homotopic distance between maps, unifying Lusternik-Schnirelmann category and topological complexity. Learn how Morse-Bott functions and navigation functions apply to generalized motion planning problems.
Integrating graphs, topology, and geometry to analyze complex chemistry data, providing insights into multiscale correlations and structures beyond traditional methods.
Explore Vietoris-Rips complexes in geometric group theory, focusing on topological finiteness properties, hyperbolic groups, and recent developments in Morse theory and persistent homology.
Explore topological structures of graphs and hypergraphs, including recent findings on homology and persistent homology, with applications in combinatorial and metric space analysis.
Survey of emerging connections between applied and quantitative topology, focusing on Vietoris-Rips complexes and their applications in computational topology, geometric group theory, and dataset shape approximation.
Explore clustering algorithms and topological data analysis from a data science perspective, covering techniques from k-means to DBSCAN and dimensionality reduction methods like UMAP.
Explore homotopy types of Vietoris-Rips complexes in metric wedge sums and gluings, with applications to persistent homology of metric graphs and insights into open research directions.
Explores generalizations of p-Wasserstein distance for multi-parameter persistence modules, introducing d_I^p and d_M^p metrics. Examines their properties and applications in topological data analysis.
Explore quasiperiodicity in signals, persistent homology, and Kunneth theorems. Learn applications in time series analysis and computational chemistry through topological data analysis.
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