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Vienna, Austria,
from 26.05.26 to 29.05.26.
Geometry and Machine Learning conference serves as a central meeting point for researchers and practitioners exploring the intersection of geometry, topology, and machine learning. It aims to foster collaboration between mathematicians and computer scientists interested in studying machine learning and deep learning from a geometric and mathematical perspective, broadly encompassing algebraic, topological, continuous, and discrete structures.
Geometry and Machine Learning conference serves as a central meeting point for researchers and practitioners exploring the intersection of geometry, topology, and machine learning. It aims to foster collaboration between mathematicians and computer scientists interested in studying machine learning and deep learning from a geometric and mathematical perspective, broadly encompassing algebraic, topological, continuous, and discrete structures.
27 May
27 May
Opening Day
28 May
28 May
Second Day
29 May
29 May
Closing day
Tolga Birdal
Tolga Birdal
Imperial College London
Complexes, Sheaves, Topological
Deep Learning and All that Jazz
Alex Bronstein
Alex Bronstein
Institute of Science and Technology Austria
Title to be annonced
Ioana Ciuclea
West University of Timișoara
Nonlinear flag manifolds and shape spaces
Ioana Ciuclea
West University of Timișoara
Nonlinear flag manifolds and shape spaces
Rita Fioresi
Rita Fioresi
University of Bologna
Title to be annonced
Simone Foti
Simone Foti
Imperial College London
Geometry-Aware Learning and
Optimization on 3D Meshes
François Gay-Balmaz
François Gay-Balmaz
Nanyang Technological University
Lie-Poisson Neural Networks (LPNets): Data-based computing of Hamiltonian systems with symmetries
Minh Ha Quang
Minh Ha Quang
RIKEN - Tokyo
(to be confirmed)
Marzieh Eidi
Marzieh Eidi
Max Planck Institute for Mathematics in the Sciences
Geometric learning in complex networks
Simone Melzi
Simone Melzi
University of Milano - Bicocca
Attention for 3D Shapes: Insights and Challenges in Transformer-Based Geometry Processing
Shirin Salehi
Shirin Salehi
RWTH Aachen University
Resource-Efficient Deep Learning: Scaling to Large Models for Edge Intelligence
Hông Vân Lê
Hông Vân Lê
Czech Academy of Sciences
Manifold Learning with Spectral Geometry and Hodge Homotopy
Organizers
Organizers
Giorgio Longari
Giorgio Longari
Wolfgang Pauli Institut and
Technische Universität Wien
Alice Barbora Tumpach
Alice Barbora Tumpach
Wolfgang Pauli Institut and
University of Lille
With the support of
With the support of
Zum Straba, Dampfschiffhaufen 7, 1220 Wien.
Zum Straba, Dampfschiffhaufen 7, 1220 Wien.
The workshop takes place at Zum Straba, Dampfschiffhaufen 7, 1220 Wien.
It is a well-known venue situated in the "Alte Donau" leisure centre in Kaisermühlen, Vienna. Located directly at the Wiener Linien beach resort, alongside the waters of the Alte Donau, it offers a relaxed, green setting away from the busy city centre.
A shuttle will take the participants from the university of Vienna to the venue. Meeting point in front of Zum Roten Bären, Berggasse 39, 1090 Vienna.
A shuttle will take the participants from the university of Vienna to the venue. Meeting point in front of Zum Roten Bären, Berggasse 39, 1090 Vienna.
See you there! 👋
See you there! 👋
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