News:

• A tentative program is now available!

• Registration has closed. Please contact one of the organizers in case of questions.

• There is the possibility of presenting a poster, which can be indicated during the registration process.

Dates: 

Mini-courses: November 4 - 5, 2024.

Workshop: November 6 - 8, 2024.

Location: Metaforum, Eindhoven University of Technology, Eindhoven, The Netherlands.

Mini-courses:

• Mini-course on Graph Signal Processing, taught by Michal Valko (DeepMind Paris).

• Mini-course on Graphical Models in Extremes, taught by Sebastian Engelke (University of Geneva).

• Mini-course on Graphical Models and Algebraic Statistics, taught by Jane Ivy Coons (University of Oxford).

Confirmed Speakers:

• Aida Abiad Monge (Eindhoven University of Technology)

• Carlos Améndola (TU Berlin)

• Gecia Bravo-Hermsdorff (University of Edinburgh)

• Angeles Carmona Mejias (UPC Barcelona)

• Jane Ivy Coons (MPI-CBG Dresden)

• Karel Devriendt (University of Oxford)

• Ignacio Echave-Sustaeta Rodríguez (Eindhoven University of Technology)

• Sebastian Engelke (University of Geneva)

• Nicola Gnecco (UCL)

• Elvin Isufi (TU Delft)

• Manuel Hentschel (University of Geneva)

• Pratik Misra (TU Munich)

• Marco Oesting (University of Stuttgart)

• Johan Segers (UC Louvain)

• Kirstin Strokorb (Cardiff University)

• Michal Valko (DeepMind Paris)

• Fabio Vitale (CENTAI Turin)

• Chen Zhou (Erasmus University Rotterdam)

Outline:

The idea for this workshop is to bring together researchers from graph signal processing, multivariate extremes and graphical models/ algebraic statistics to discuss theoretical and methodological intersections and to foster future collaborations. Some key points of interest:

• Laplacian matrices in graphical extremes and graph signal processing.

• Sparsity in graph Laplacians: Extremal conditional independence, CI ideals and sparse signals.

• Algebraic statistics and graphical extremes.

• Graph Laplacians and Euclidean distance matrices: Combinatorial and geometric tools for statistics.

• Statistical Graph Signal Processing.

• Learning in graph data, representation learning in graphs.

• Inference of graph topology.

• Geometric Deep Learning.

• Applications of Graph Signal Processing on network-structured data (such as in communication networks, health care, social networks, drug discovery).

Phishing warning: 

Scientific workshops are unfortunately frequent phishing targets. Please contact one of the organizers when in doubt.

Organizers: 

• Ignacio Echave-Sustaeta Rodríguez (Eindhoven University of Technology)

• Frank Röttger (Eindhoven University of Technology)

• Mariana Vargas Vieyra (MOSTLY AI)

• Piotr Zwiernik (University of Toronto)