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johannes [dot] lutzeyer

[at] polytechnique [dot] edu

I am Johannes Lutzeyer an Assistant Professor in the Data Science and Mining Team at the Laboratoire d'Informatique of École Polytechnique in France. Previously, I completed a 2.5 year postdoc, under the supervision of Prof. Michalis Vazirgiannis, at École Polytechnique and a PhD thesis on the spectral properties of the adjacency and Laplacian matrices under the supervision of Prof. Andrew Walden at Imperial College London.

My current research focuses on *Graph Neural Networks* and *Spectral Properties of Graph Shift Operator Matrices.* As such, I work in the area of Graph Representation Learning in the intersection of Statistics and Computer Science.

My academic CV can be accessed here.

**August 2024**: We invite applications for a CIFRE PhD position in collaboration with CMA-CGM on Graph Neural Networks for Maritime Trade. Please consider applying!**July 2024**: I am very happy to receive one of the ICML Best Reviewer Awards.**June 2024**: I gave a keynote talk entitled “Graph Learning for and with Graph Neural Networks” at the GdR Graph Learning Day organised by Arnaud Breloy and Benjamin Girault. [Slides]**May 2024**: I gave a seminar entitled “Recent Advances in Graph Neural Network Robustness” at the AIDRC Seminar Series organised by Mohamed El Amine Seddik. [Slides]**December 2023**: I am very happy to receive one of the NeurIPS Top Reviewer Awards.**November 2023**: Please come join us at the Paris Learning on Graphs Meetup accompanying the Learning on Graphs Conference.**July 2023**: I gave a plenary talk entitled “Path Neural Networks: Expressive and Accurate Graph Neural Networks” at the 2nd ELLIS UnConference organised by Alain Durmus and colleagues.**July 2023**: I gave a seminar entitled “Recent Advances in Graph Neural Networks” at the Graphs Guild of Lloyds Banking Group upon inivitation of Jack Evan Rodwell.**June 2023**: I gave a talk entitled “Message Passing In Graph Neural Networks” at the Learning on Graphs Meetup Paris organised by Alexandre Duval and Fragkiskos Malliaros. [Slides]

- Y. Abbahaddou, S. Ennadir, J. F. Lutzeyer, M. Vazirgiannis & H. Boström, “Bounding the Expected Robustness of Graph Neural Networks Subject to Node Feature Attacks,”
*International Conference on Learning Representations(ICLR)*, 2024. - S. Ennadir, Y. Abbahaddou, J. F. Lutzeyer, M. Vazirgiannis & H. Boström, “A Simple and Yet Fairly Effective Defense for Graph Neural Networks,”
*AAAI Conference on Artificial Intelligence (AAAI)*, 2024. - Y. Abbahaddou, J. F. Lutzeyer & M. Vazirgiannis, “Graph Neural Networks on Discriminative Graphs of Words,”
*NeurIPS New Frontiers in Graph Learning Workshop*, 2023. - G. Michel, G. Nikolentzos, J. F. Lutzeyer & M. Vazirgiannis, “Path Neural Networks: Expressive and Accurate Graph Neural Networks,”
*Proceedings of the 40th International Conference on Machine Learning (ICML)*, 2023. - B. Doerr, A. Dremaux, J. F. Lutzeyer & A. Stumpf, “How the move acceptance hyper-heuristic copes with local optima: drastic differences between jumps and cliffs,”
*Proceedings of the Genetic and Evolutionary Computation Conference (GECCO)*, 2023. - M. Chatzianastasis, J. F. Lutzeyer, G. Dasoulas & M. Vazirgiannis, “Graph Ordering Attention Networks,”
*AAAI Conference on Artificial Intelligence (AAAI)*, 2023. - G. Salha-Galvan, J. F. Lutzeyer, G. Dasoulas, R. Hennequin & M. Vazirgiannis, “New Frontiers in Graph Autoencoders: Joint Community Detection and Link Prediction,”
*NeurIPS New Frontiers in Graph Learning Workshop*, 2022. - A. R. Ramos Vela, J. F. Lutzeyer, A. Giovanidis & M. Vazirgiannis, “Improving Graph Neural Networks at Scale: Combining Approximate PageRank and CoreRank,”
*NeurIPS New Frontiers in Graph Learning Workshop*, 2022. - A. Qabel, S. Ennadir, G. Nikolentzos, J. F. Lutzeyer, M. Chatzianastasis, H. Bostrom & M. Vazirgiannis, “Structure-Aware Antibiotic Resistance Classification Using Graph Neural Networks,”
*NeurIPS AI for Science Workshop*, 2022. - G. Salha-Galvan, J. F. Lutzeyer, G. Dasoulas, R. Hennequin & M. Vazirgiannis, “Modularity-Aware Graph Autoencoders for Joint Community Detection and Link Prediction,”
*Neural Networks*, vol. 153, pp. 474–495, 2022. - J. F. Lutzeyer*, C. Wu* & M. Vazirgiannis, “Sparsifying the Update Step in Graph Neural Networks,”
*ICLR Workshop on Geometrical and Topological Representation Learning*, 2022. - M. E. A. Seddik, C. Wu, J. F. Lutzeyer & M. Vazirgiannis, “Node Feature Kernels Increase Graph Convolutional Network Robustness,”
*International Conference on Artificial Intelligence and Statistics (AISTATS)*, 2022. - G. Dasoulas*, J. F. Lutzeyer* & M. Vazirgiannis, “Learning Parametrised Graph Shift Operators,”
*International Conference of Learning Representations (ICLR)*, 2021. - J. F. Lutzeyer & A. T. Walden, “Comparing Spectra of Graph Shift Operator Matrices,”
*International Conference on Complex Networks and their Applications*, 2020. - J. F. Lutzeyer & A. T. Walden, “Extending the Davis-Kahan theorem for comparing eigenvectors of two symmetric matrices I: Theory,”
*arXiv:1908.03462*, 2019. - J. F. Lutzeyer & A. T. Walden, “Extending the Davis-Kahan theorem for comparing eigenvectors of two symmetric matrices II: Computation and Applications,”
*arXiv:1908.03465*, 2019. - J. F. Lutzeyer & E. A. K. Cohen, “Correcting the estimator for the mean vectors in a multivariate errors-in-variables regression model,”
*arXiv:1510.03600*, 2015.

* is used to denote equal contribution.

I would like to sincerely thank Guillaume Salha-Galvan for creating this website and allowing me to copy the format of his website. In turn, Guillaume would like to acknowledge the github pages theme by orderedlist.