Takashi Owada - Limit Theorems for Topological Invariants of the Dynamic Multi-Parameter Simplicial Complex
Explore topological invariants in dynamic multi-parameter simplicial complexes, covering connectivity, probability, and limiting processes in applied algebraic topology.
Vanessa Robins Interviewed by Elizabeth Bradley
Explore applied topology with Vanessa Robins as she discusses her career journey, research interests, and insights on granular materials, collaborations, and emerging areas in the field.
Tamal Dey - A Framework for Topological Summarization
Explore topological summarization frameworks, covering concepts like nerve complexes, multiscale mapper, persistence diagrams, and stability in hierarchical covers. Gain insights into advanced computational topology techniques.
Omer Bobrowski - Maximally Persistent Cycles in Random Geometric Complexes
Explore persistent cycles in random geometric complexes, examining key tools, processes, and results. Gain insights into random graph theory and future research directions.
Applied Algebraic Topology Network - Student Seminar Introduction to JavaPlex
Learn to use JavaPlex for topological data analysis, covering key concepts like witness complexes, data representation, and visualization tools. Ideal for students exploring computational topology.
Benjamin Schweinhart - Statistical Topology of Random Cell Complexes, and Applications
Explore statistical topology of random cell complexes, covering curvature flow, grain growth, graph limits, and computational evidence for steady state hypothesis.
Farzana Nasrin - Random Persistence Diagram Generator
Explore a method for generating random persistence diagrams using parametric models and Markov chain Monte Carlo algorithms, with applications in material science and topology.
Emergent Network Geometry
Explore emergent network geometry, from complex networks to quantum spacetime, revealing hidden structures and applications in diverse fields like neuroscience and collaboration networks.
Computing Optimal Homotopies
Explore optimal curve similarity measures using homotopy-based approaches, examining structural properties, algorithms, and open questions in topology and computational geometry.
Žiga Virk - Information Encoded in Persistence Diagrams
Explore topological data analysis through persistence diagrams, uncovering spatial properties like homology, geodesics, and proximity in various mathematical contexts.
Herbert Edelsbrunner Interviewed by Dmitriy Morozov - September 30, 2020
Insightful interview with Herbert Edelsbrunner, exploring his contributions to computational geometry and topology, career journey, and perspectives on the field's evolution and future directions.
Efficient Approximation of the Matching Distance for 2-Parameter Persistence
Explore efficient algorithms for approximating matching distance in 2-parameter persistence modules, focusing on quad-tree refinement and local adaptive bounds for improved computational performance.
Solving Equations and Optimization Problems with Uncertainty
Explore algorithms for detecting zeros of imprecise continuous functions and approximating worst-case optima in optimization problems with uncertain feasible domains.
Frédéric Chazal Interviewed by Steve Oudot
Entretien approfondi avec Frédéric Chazal, pionnier de la topologie appliquée, explorant son parcours, ses contributions majeures et sa vision pour l'avenir du domaine.
Kathryn Hess Interviewed by Peter Bubenik - August 11, 2021
Engaging interview with mathematician Kathryn Hess, exploring her research, insights, and contributions to applied algebraic topology and related fields.
Cell Complex Neural Networks
Exploring neural network computations on cell complexes, generalizing graph neural networks and introducing inter-cellular message passing schemes that consider underlying topology.
Approximate and Discrete Vector Bundles
Explore synchronization problems and dimensionality reduction through vector bundles. Learn how characteristic classes provide insights into global synchronization and data analysis challenges.
Gunnar Carlsson Interviewed by Vin de Silva - February 3, 2021
Insightful interview with Gunnar Carlsson, exploring topics in applied algebraic topology and its applications in data analysis and machine learning.
Universality of Persistence Diagrams and Bottleneck & Wasserstein Distances
Explore the mathematical foundations of persistence diagrams and their associated metrics, focusing on universality properties and distance calculations in topological data analysis.
RipsNet- Fast and Robust Estimation of Persistent Homology for Point Clouds
Estimating persistence diagrams of point clouds using neural networks for faster, more robust topological analysis. Overcomes computational complexity and sensitivity to outliers in practical applications.
Claudia Landi - Multi-parameter Persistence from the Viewpoint of Discrete Morse Theory
Explore multi-parameter persistence through discrete Morse theory, connecting combinatorial properties of critical cells to algebraic properties of persistence modules in topological data analysis.