Workshop: Topological Data Analysis and Beyond
Bastian Rieck, Frederic Chazal, Smita Krishnaswamy, Roland Kwitt, Karthi Natesan Ramamurthy, Yuhei Umeda, Guy Wolf
Fri, Dec 11th, 2020 @ 07:00 – 20:00 GMT
Abstract: 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.
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.
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Schedule
07:00 – 07:15 GMT
Opening Remarks
Frederic Chazal, Smita Krishnaswamy, Roland Kwitt, Karthi Natesan Ramamurthy, Bastian Rieck, Yuhei Umeda, Guy Wolf
07:15 – 07:45 GMT
Keynote: Kathryn Hess: Topological Insights in Neuroscience
Kathryn Hess
08:00 – 08:15 GMT
Invited Talk: Yuzuru Yamakage: Industrial Application of TDA-ML technology: Achievement so far and expectations of future
Yuzuru Yamakage
08:30 – 08:45 GMT
Invited Talk: Manohar Kaul: Solving Partial Assignment Problems using Random Simplicial Complexes
Manohar Kaul
08:45 – 09:00 GMT
Invited Talk: Yasuaki Hiraoka: Characterizing Rare Events in Persistent Homology
Yasuaki Hiraoka
09:00 – 09:15 GMT
Invited Talk: Serguei Barannikov: Topological Obstructions to Neural Networks’ Learning
Serguei Barannikov
09:15 – 09:30 GMT
Invited Talk: Ulrich Bauer: The Representation Theory of Filtered Hierarchical Clustering
Ulrich Bauer
09:30 – 09:33 GMT
Spotlight: Topo Sampler: A Topology Constrained Noise Sampling for GANs
Adrish Dey, Sayantan Das
09:33 – 09:36 GMT
Spotlight: Weighting Vectors for Machine Learning: Numerical Harmonic Analysis Applied to Boundary Detection
Eric Bunch, Jeff Kline, Daniel Dickinson, Glenn Fung
09:36 – 09:39 GMT
Spotlight: Hypothesis Classes with a Unique Persistence Diagram are Nonuniformly Learnable
Nicholas Bishop, Long Tran-Thanh, Thomas Davies
09:39 – 09:42 GMT
Spotlight: Quantifying Barley Morphology Using the Euler Characteristic Transform
Erik Amézquita, Elizabeth Munch, Michelle Quigley, Tim Ophelders, Jacob Landis, Daniel Chitwood, Daniel Koenig
09:42 – 09:45 GMT
Spotlight: giotto-tda: A Topological Data Analysis Toolkit for Machine Learning and Data Exploration
Guillaume Tauzin, Umberto Lupo, Kathryn Hess, Lewis Tunstall, Julian Pérez, Matteo Caorsi, Wojtek Reise, Anibal Medina-Mardones, Alberto Dassatti
09:45 – 10:15 GMT
Poster Session I & Break
10:15 – 11:00 GMT
Discussion I
11:30 – 12:30 GMT
Lunch Break
12:00 – 12:30 GMT
Invited Talk: Lida Kanari: A Topological Insight on Neuronal Morphologies
Lida Kanari
13:00 – 13:15 GMT
Invited Talk: Bei Wang: Topology and Neuron Activations in Deep Learning
Bei Wang
13:15 – 13:30 GMT
Invited Talk: Lorin Crawford: A Machine Learning Pipeline for Feature Selection and Association Mapping with 3D Shapes
Lorin Crawford
13:45 – 14:00 GMT
Invited Talk: Brittany Terese Fasy: Searching in the Space of Persistence Diagrams
Brittany Terese Fasy
13:45 – 14:00 GMT
Invited Talk: Mathieu Carrière: Probabilistic and Statistical Aspects of Reeb spaces and Mappers
Mathieu Carrière
14:30 – 14:33 GMT
Spotlight: k-simplex2vec: A Simplicial Extension of node2vec
Celia Hacker
14:36 – 14:39 GMT
Spotlight: Characterizing the Latent Space of Molecular Generative Models with Persistent Homology Metrics
Yair Schiff, Payel Das, Vijil Chenthamarakshan, Karthi Natesan Ramamurthy
14:39 – 14:42 GMT
Spotlight: Permutation Invariant Networks to Learn Wasserstein Metrics
Arijit Sehanobish, Neal G Ravindra, David van Dijk
14:42 – 14:45 GMT
Spotlight: Multidimensional Persistence Module Classification via Lattice-Theoretic Convolutions
Hans Riess
14:45 – 15:15 GMT
Poster Session II & Break
15:15 – 16:00 GMT
Discussion II
16:15 – 16:30 GMT
Invited Talk: Jose Perea: TALLEM – Topological Assembly of LocalLy Euclidean Models
Jose Perea
16:30 – 16:45 GMT
Invited Talk: Yusu Wang: Discrete Morse-based Graph Reconstruction and Data Analysis
Yusu Wang
17:00 – 17:15 GMT
Invited Talk: Elizabeth Munch: Persistent Homology of Complex Networks for Dynamic State Detection in Time Series
Elizabeth Munch
17:30 – 17:45 GMT
Invited Talk: Facundo Mémoli: Spatiotemporal Persistent Homology for Dynamic Metric Spaces
Facundo Mémoli
17:45 – 18:30 GMT
Poster Session III & Break
18:30 – 19:15 GMT
Discussion III
19:15 – 19:30 GMT
Closing Remarks
Frederic Chazal, Smita Krishnaswamy, Roland Kwitt, Karthi Natesan Ramamurthy, Bastian Rieck, Yuhei Umeda, Guy Wolf