The last decade saw an enormous boost in the field of computational topology: methods and concepts from algebraic and differential topology, formerly confined to the realm of pure mathematics, have demonstrated their utility in numerous areas such as computational biology, personalised medicine, materials science, and time-dependent data analysis, to name a few.
The newly-emerging domain comprising topology-based techniques is often referred to as topological data analysis (TDA). Next to their applications in the aforementioned areas, TDA methods have also proven to be effective in supporting, enhancing, and augmenting both classical machine learning and deep learning models.
We believe that it is time to bring together theorists and practitioners in a creative environment to discuss the goals beyond the currently-known bounds of TDA. We want to start a conversation between experts, non-experts, and users of TDA methods to debate the next steps the field should take. We also want to disseminate methods to a broader audience and demonstrate how easy the integration of topological concepts into existing methods can be.
Important links:
- Gather.Town (for poster sessions)
- Rocket.Chat (for asking questions)
- Slack (for asking questions)
| Gather.Town (for poster sessions) (Link) | |
| Rocket.Chat (for asking questions to panellists) (Link) | |
| Slack (for asking questions to panellists) (Link) | |
| Opening Remarks | |
| Keynote: Kathryn Hess: Topological Insights in Neuroscience (Keynote) | |
| Invited Talk: Vidit Nanda: Singularity Detection in Data (Invited Talk) | |
| Invited Talk: Yuzuru Yamakage: Industrial Application of TDA-ML technology: Achievement so far and expectations of future (Invited Talk) | |
| Invited Talk: Katharine Turner (Invited Talk: Wasserstein Stability for Persistence Diagrams) | |
| Invited Talk: Manohar Kaul: Solving Partial Assignment Problems using Random Simplicial Complexes (Invited Talk) | |
| Invited Talk: Yasuaki Hiraoka: Characterizing Rare Events in Persistent Homology (Invited Talk) | |
| Invited Talk: Serguei Barannikov: Topological Obstructions to Neural Networks’ Learning (Invited Talk) | |
| Invited Talk: Ulrich Bauer: The Representation Theory of Filtered Hierarchical Clustering (Invited Talk) | |
| Spotlight: Topo Sampler: A Topology Constrained Noise Sampling for GANs (Spotlight) | |
| Spotlight: Weighting Vectors for Machine Learning: Numerical Harmonic Analysis Applied to Boundary Detection (Spotlight) | |
| Spotlight: Hypothesis Classes with a Unique Persistence Diagram are Nonuniformly Learnable (Spotlight) | |
| Spotlight: Quantifying Barley Morphology Using the Euler Characteristic Transform (Spotlight) | |
| Spotlight: giotto-tda: A Topological Data Analysis Toolkit for Machine Learning and Data Exploration (Spotlight) | |
| Poster Session I & Break (Poster Session) | |
| Discussion I (Discussion Panel) | |
| Keynote: Gunnar Carlsson (Keynote) | |
| Lunch Break (Break) | |
| Invited Talk: Lida Kanari: A Topological Insight on Neuronal Morphologies (Invited Talk) | |
| Invited Talk: Peter Bubenik (Invited Talk) | |
| Invited Talk: Andrew J. Blumberg (Invited Talk) | |
| Invited Talk: Bei Wang: Topology and Neuron Activations in Deep Learning (Invited Talk) | |
| Demo: Teaspoon Package (Demo Session) | |
| Invited Talk: Lorin Crawford: A Machine Learning Pipeline for Feature Selection and Association Mapping with 3D Shapes (Invited Talk) | |
| Invited Talk: Chao Chen (Invited Talk) | |
| Invited Talk: Brittany Terese Fasy: Searching in the Space of Persistence Diagrams (Invited Talk) | |
| Invited Talk: Mathieu Carrière: Probabilistic and Statistical Aspects of Reeb spaces and Mappers (Invited Talk) | |
| Invited Talk: Don Sheehy (Invited Talk) | |
| Spotlight: k-simplex2vec: A Simplicial Extension of node2vec (Spotlight) | |
| Spotlight: Sheaf Neural Networks (Spotlight) | |
| Spotlight: Characterizing the Latent Space of Molecular Generative Models with Persistent Homology Metrics (Spotlight) | |
| Spotlight: Permutation Invariant Networks to Learn Wasserstein Metrics (Spotlight) | |
| Spotlight: Multidimensional Persistence Module Classification via Lattice-Theoretic Convolutions (Spotlight) | |
| Poster Session II & Break (Poster Session) | |
| Discussion II (Discussion Panel) | |
| Invited Talk: Laxmi Parida: TDA on Covid 19 OMICS data (Invited Talk) | |
| Invited Talk: Jose Perea: TALLEM – Topological Assembly of LocalLy Euclidean Models (Invited Talk) | |
| Invited Talk: Yusu Wang: Discrete Morse-based Graph Reconstruction and Data Analysis (Invited Talk) | |
| Invited Talk: Robert Ghrist: The Tarski Laplacian (Invited Talk) | |
| Invited Talk: Elizabeth Munch: Persistent Homology of Complex Networks for Dynamic State Detection in Time Series (Invited Talk) | |
| Invited Talk: Leland McInnes: UMAP + MAPPER = UMAPPER (Invited Talk) | |
| Invited Talk: Facundo Mémoli: Spatiotemporal Persistent Homology for Dynamic Metric Spaces (Invited Talk) | |
| Poster Session III & Break (Poster Session) | |
| Discussion III (Discussion Panel) | |
| Closing Remarks | |