Johannes Lutzeyer -- Personal Website

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.

News

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]

Publications

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.