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

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]
June 2023: I gave a seminar entitled “Recent Trends in Graph Representation Learning” at the Laboratoire Modélisation, Information & Systèmes in Amiens upon inivitation of Fabio Morbidi.
May 2023: [Applications are now closed.] We invite applications for two Postdoctoral Reseacher/Research Engineer Positions on “Multimodal Graph Generative Models” and “Graph Representation Learning with Biomedical Applications”. Please consider applying!
February 2023: I gave a seminar entitled “Advances in Graph Representation Learning: The Graph Ordering Attention Networks” at the Seminaire Palaisien organised by Thomas Moreau and Victor-Emmanuel Brunel.
January 2023: I gave a talk entitled “Different Approaches To Message Passing In Graph Neural Networks” at the Reading Group of the Amazon Graph Machine Learning Team upon invitiation of Vassilis Ioannidis.
October 2022: I gave a seminar entitled “Graph Representation Learning via Graph Neural Networks” at the LIX Seminar Series organised by Sylvie Putot. [Recording & Slides]

Publications

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.