Organizers:
Huitao Feng (Nankai, China)
Fei Han (NUS, Singapore)
Wilderich Tuschmann (KIT, Germany)
Kelin Xia (NTU, Singapore)
Scientific committee:
David Gu (Stony Brook, US)
Jürgen Jost (Max Planck Institutes, Germany)
Kefeng Liu (UCLA, US)
Guowei Wei (MSU, US)
Scope:
Data-driven sciences are widely regarded as the fourth paradigm that can fundamentally change sciences and pave the way for a new industrial revolution. The great success of AlphaFold 2 in protein folding ushers in a new era for machine learning models in natural sciences. However, efficient representations and featurization are still one of the central challenges for AI-based data analysis at present. Computational and discrete geometry has achieved great success in data characterization and modelling. In particular, geometric deep learning has significantly advanced the capability of learning models for data with complicated topological and geometric structures. The combination of geometric methods with learning models has great potential to fundamentally change the data sciences. As the field is driven by a combination of deep mathematical methods and challenging data, it is important to bring both sides together. This workshop will focus on the recent progresses of geometric models in data applications. The topics include but are not limited to:
Discrete exterior calculus and its application, discrete Laplace Operators, discrete Optimal Transport, discrete mapping, discrete parametric surface
Geometric flow and applications (Ricci curvature flow, mean curvature flow, etc)
Geometric modelling
Discrete Ricci curvatures, Ollivier Ricci curvature, Forman Ricci curvature
Conformal geometry
Combinatorial Hodge theory, Hodge Laplacian, discrete Dirac operator
Dimension reduction (manifold learning, Isomap, Laplacian eigenmaps, diffusion maps, UMAP, MAPPER, hyperbolic geometry, Poincaré embedding, etc)
Geometric signal processing
Geometric deep learning, graph neural network, simplex neural network
Geometric analysis of deep learning, geometric GAN, explainable deep learning, geometric optimal transportation
Index theory
Gromov-Hausdorff distance
Information geometry
Metaverse: 3D vision, SLAM, digital geometry processing, digital manufacturing
The conference will be held in a hybrid form at Mathematical Science Research Center, Chongqing University of Technology, July 25-29, 2022. The zoom meeting details will be updated soon!
Confirmed Speakers: (On-going)
Chandrajit Bajaj, University of Texas Austin, USA
Shi-Bing Chen, University of Science and Technology of China, China
Mathieu Desbrun, California Institute of Technology, USA
Marzieh Eidi, Max Planck Institute for Mathematics, Germany
Michael Farber, Queen Mary University of London, UK
Mustafa Hajij, Santa Clara University, USA
Bobo Hua, Fudan, China
Parvaneh Joharinad, Max Planck Institute for Mathematics, Germany
Christian Kuehn, Technical University of Munich, Germany
Jiakun Liu, University of Wollongong, Australia
Shiping Liu, University of Science and Technology of China, China
Hong Van Le Prague, Czech Academy of Sciences, Czech Republic
Areejit Samal, The Institute of Mathematical Sciences, India
Emil Saucan,ORT Braude & Technion, Israel
Anna Wienhard, Heidelberg University, Germany
Hao Xu, Zhejiang University, China
Dong Zhang, Max Planck Institute for Mathematics, Germany
Jie Wu, BIMSA, China
Seminar Schedule:(Coming soon)
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