June 9, 2022

Henry Adams -- Colorado State University
Web page

The unreasonably effective interaction between math and applications:
A case study on persistence images

10am, Sorbonne University, Room 2300 (Zoom Link)

Abstract: Wigner described the unreasonable effectiveness of mathematics in the natural sciences: ideas from mathematics are unreasonably effective in advancing applications, and ideas from applications are unreasonably effective in advancing mathematics. We describe a case study on persistent images, a stable vector representation of persistent homology. If you combine topology with data, you get persistent homology. If you combine persistent homology with machine learning, you might get persistent landscapes or persistence images or a host of other options. The first attempt at persistence images were not stable (i.e. continuous), but by making them stable, their machine learning performance improves, as we will describe on examples ranging from materials science to biology. This ping-ponging behavior of injecting ideas from mathematics, then injecting ideas from applications, etc, leads to robust applied tools and new mathematical questions. Joint work with Sofya Chepushtanova, Tegan Emerson, Eric Hanson, Michael Kirby, Francis Motta, Rachel Neville, Chris Peterson, Patrick Shipman, and Lori Ziegelmeier.

Bio: Henry Adams is an Associate Professor of Mathematics at Colorado State University. His research interests are in computational topology and geometry, quantitative topology, combinatorial topology, and topological data analysis. He has applied topology to problems arising in machine learning, computer vision, minimal sensing, collective motion models, and chemical energy landscapes. Professor Adams is the Executive Director of the Applied Algebraic Topology Research Network (AATRN).

May 13, 2022 [Postponed]

Brian Summa -- Tulane University
Web page

Is Bigger Data Always Better?

4pm, Online (Zoom Link)

Abstract: Scientific datasets have continually grown in size, driven by the perceived need for higher fidelities to model or measure complex phenomena correctly. This trend comes at a high cost. It requires significant effort and resources to acquire, process, and store this ever-increasing collection of produced data. In this talk, I’ll discuss our ongoing efforts to reduce this cost through novel image acquisition, records and analyses of user behavior during exploration, and concise descriptors of data features.

Bio: Brian Summa is an Assistant Professor of Computer Science at Tulane University, where he is the head of the visualization and graphics lab. His research interests focus on large scale imaging and data analysis.

January 4, 2022

Michael Aupetit -- Qatar Computing Research Institute
Web page - Twitter

Visual Analytics, Machine Learning and Topological Models to support multidimensional data analysis

2pm, Sorbonne University, Room 24-25/405 (Zoom Link)

Abstract: I will give an overview of my work to support the analyst exploring and discovering patterns in multidimensional data. Topology Data Analysis (TDA) (including clustering) and Multidimensional Projection (MDP) techniques are core computational approaches to summarize multidimensional (MD) data. But Visualization is a crucial component to link the summarized data to the end-user who generates knowledge. It relies on finding the most efficient graphical encoding and interactions to support the analytical tasks and to account for the perceptual and cognitive bottlenecks to fit with the scale of the MD data. I will first present generative models for TDA, to extract a summary of the MD data before visualizing it. Then, I will expose MDP techniques and their distortions that a new supervised MDP exploits to separate classes only if they do not overlap in the MD space. I will switch to visualization techniques showing how visual enrichment can solve some of the MDP distortions. Then, show how we can start the analytic process from scratch with interactive Voronoi treemaps. Finally, I will show how to scale the process with perceptual-data-driven visual quality measures and discuss future research tracks.

Bio: Dr. Michaël Aupetit has worked at the Qatar Computing Research Institute since 2014. He is a Senior Scientist with the Social Computing group. Before joining QCRI, Michaël worked for ten years as a research scientist and senior expert in data mining and visual analytics at CEA LIST in Paris Saclay. He designed decision support systems to solve complex industrial problems in health and security domains. Michaël initiated and co-organized five international workshops, including the first workshop on Topology Learning at NIPS 2007, and created and chaired the first Visualization and Computer-Human Interaction conference (VisCHI 2019) in the Middle East. He has been a PC member of the leading visualization conferences IEEE VIS and Eurovis. He also reviewed hundreds of papers for top-tier journals and conferences, doing regular reviews for IEEE TVCG, Computer Graphics Forum, and Neurocomputing. He contributed more than a hundred publications and holds 3 WO , 2 US, and 1 EP patent. He obtained the Habilitation for Research Supervision (HDR) in Computer Science from Paris 11 Orsay University in 2012, and the Ph. D degree in Industrial Engineering from Grenoble National Polytechnic Institute (INPG) in 2001.