Maysam Behmanesh

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About me

I did my Postdoctoral Research in the GeomeriX group at the LIX research laboratory of École Polytechnique IP-Paris, from April 2022 to Jan. 2025, working with Prof. Maks Ovsjanikov. My research focused on machine learning, particularly geometric deep learning, with an emphasis on graphs and multimodal data.

I earned my PhD in Computer Engineering-Artificial Intelligence from the University of Isfahan (UI), where I conducted research in the ILS-Lab under the supervision of Prof. Peyman Adibi. My doctoral research concentrated on geometric multimodal learning, leveraging the geometric structure of data for multimodal manifold learning and applying geometric deep learning to graph multimodal data. A part of my PhD research was completed at GIPSA-Lab in Grenoble Institute of Technology, Grenoble, where I was a visiting resercher under the supervision of Prof. Jocelyn Chanussot from 2019 to 2020.

Previously, I have worked on various topics, including chaotic time-series prediction, imbalanced data classification, neuro-fuzzy inference systems, and evolutionary computation.

News

• Two papers accepted at ICML 2026:
  • "Beyond ReLU: Bifurcation, Oversmoothing, and Topological Priors" (Spotlight), with Erkan Turan, Gaspard Abel, Emery Pierson, and Maks Ovsjanikov.
  • "Graph Alignment via Dual-Pass Spectral Encoding and Latent Space Communication", with Erkan Turan and Maks Ovsjanikov.

• I gave a talk about my recent papers in geometric deep learning at SAMOVAR - Télécom SudParis, Institut Polytechnique de Paris

• I gave a talk at Inria Paris, hosted by the Argo research team. My talk was on "Enhancing Graph Neural Networks with Geometric Structure Analysis".
• Our new paper, Smoothed Graph Contrastive Learning via Seamless Proximity Integration, with Maks Ovsjanikov has been accepted at Learning on Graphs Conference LoG-2024 PDF.

• Our new paper, Cross-Modal and Multimodal Data Analysis Based on Functional Mapping of Spectral Descriptors and Manifold Regularization, with Peyman Adibi, Jocelyn Chanussot and Sayyed Mohammad Saeed Ehsani is published in Neurocomputing PDF.

• I gave a talk at Télécom Paris, hosted by the S2A team. My talk was on "Graph Representation Learning for Multimodal Data - Challenges and Innovative Methods".

• Our paper "TIDE: Time Derivative Diffusion for Deep Learning on Graphs", with Maximilian Krahn and Maks Ovsjanikov has been accepted at ICML 2023, PDF.