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high-hopes.md

@@ -82,6 +82,9 @@ The goal of this project is to develop methods that can utilize such higher-orde
 ## Presentations and Communication 
 Research related to the present project was presented at the following places / events.
 
+* Newton Institute Workshop "Hypergraphs: Theory and Applications", London, UK, July 2024
+* Nordita WINQ Workshop on Dynamics and Topology of Complex Network Systems, Stockholm, Sweden, May 2024
+* SIAM Conference on Applied Linear Algebra (LA 24), Paris, France, May 2024
 * Autumn school on hypergraphs, Bernoulli Society Committee on Statistical Network Science, Online, October 2023
 * ELLIIT Focus Period Network Dynamics and Control, Linköping, Sweden, September 2023 
 * International Congress on Industrial and Applied Mathematics (ICIAM 2023) -- Minisymposium on Higher order networks for complex systems, Tokyo, Japan, August 2023
@@ -96,9 +99,21 @@ Research related to the present project was presented at the following places /
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 ## Complete Publication list
 A list of all publications associated with this project is provided below:
+* Papamarkou, T.; Birdal, T.; Bronstein, M.; Carlsson, G.; Curry, J.; Gao, Y.; Hajij, M.; Kwitt, R.; Liò, P.; Lorenzo, P. D.; Maroulas, V.; Miolane, N.; Nasrin, F.; Ramamurthy, K. N.; Rieck, B.; Scardapane, S.; Schaub, M. T.; Veličković, P.; Wang, B.; Wang, Y.; Wei, G.-W. & Zamzmi, G., Position: Topological Deep Learning is the New Frontier for Relational Learning, *Proceedings of the 41st International Conference on Machine Learning (ICML 2024)*, 2024 
+* Neuhäuser, L.; Scholkemper, M.; Tudisco, F. & Schaub, M. T., Learning the effective order of a hypergraph dynamical system, *Science Advances*, 2024, 10, eadh4053 
+* Frantzen, F. & Schaub, M. T., Learning From Simplicial Data Based on Random Walks and 1D Convolutions, *International Conference on Learning Representations*, 2024
+* Grande, V. & Schaub, M. T., Disentangling the Spectral Properties of the Hodge Laplacian: Not All Small Eigenvalues Are Equal, *IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2024)*, 2024, 9896-9900 
+* Hajij, M.; Zamzmi, G.; Papamarkou, T.; Guzmán-Sáenz, A.; Birdal, T. & Schaub, M. T., Combinatorial Complexes: Bridging the Gap Between Cell Complexes and Hypergraphs, *57th Asilomar Conference on Signals, Systems, and Computers*, 2023, 799-803 
+* Hoppe, J. & Schaub, M. T., Representing Edge Flows on Graphs via Sparse Cell Complexes, *Proceedings of the Second Learning on Graphs Conference*, PMLR, 2023, 231, 1:1-1:22 (**Best Paper Award**)
+* Grande, V. P. & Schaub, M. T., Non-Isotropic Persistent Homology: Leveraging the Metric Dependency of PH, *Proceedings of the Second Learning on Graphs Conference*, PMLR, 2023, 231, 17:1-17:19  
+* Grande, V. P. & Schaub, M. T., Topological Point Cloud Clustering, *Proceedings of the 40th International Conference on Machine Learning (ICML 2023)*, PMLR, 2023, 202, 11683-11697 
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-* Hoppe, J. & Schaub, M. T., Representing Edge Flows on Graphs via Sparse Cell Complexes, Learning on Graphs 2023, 2023 (**Best Paper Award**)
-* Grande, V. P. & Schaub, M. T.,  Non-isotropic Persistent Homology: Leveraging the Metric Dependency of PH, Learning on Graphs 2023, 2023 
+### Preprints
+* Hoppe, J. & Schaub, M. T., Random Abstract Cell Complexes, 2024, [https://arxiv.org/abs/2406.01999](https://arxiv.org/abs/2406.01999)
+* Epping, B.; René, A.; Helias, M. & Schaub, M. T., Graph Neural Networks Do Not Always Oversmooth, 2024, [https://arxiv.org/abs/2406.02269](https://arxiv.org/abs/2406.02269)
+* Grande, V. P. & Schaub, M. T., Node-Level Topological Representation Learning on Point Clouds, 2024, [https://arxiv.org/abs/2406.02300](https://arxiv.org/abs/2406.02300) 
+* Hajij, M.; Papillon, M.; Frantzen, F.; Agerberg, J.; AlJabea, I.; Ballester, R.; Battiloro, C.; Bernárdez, G.; Birdal, T.; Brent, A.; Chin, P.; Escalera, S.; Fiorellino, S.; Gardaa, O. H.; Gopalakrishnan, G.; Govil, D.; Hoppe, J.; Karri, M. R.; Khouja, J.; Lecha, M.; Livesay, N.; Meißner, J.; Mukherjee, S.; Nikitin, A.; Papamarkou, T.; Prílepok, J.; Ramamurthy, K. N.; Rosen, P.; Guzmán-Sáenz, A.; Salatiello, A.; Samaga, S. N.; Scardapane, S.; Schaub, M. T.; Scofano, L.; Spinelli, I.; Telyatnikov, L.; Truong, Q.; Walters, R.; Yang, M.; Zaghen, O.; Zamzmi, G.; Zia, A. & Miolane, N., TopoX: A Suite of Python Packages for Machine Learning on Topological Domains 2024, [https://arxiv.org/abs/2402.02441](https://arxiv.org/abs/2402.02441)
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