Scholar
Maysam Behmanesh
Google Scholar ID: XegLXtoAAAAJ
École Polytechnique
Citations & Impact
Citations
59
H-index
4
i10-index
2
Publications
12
Co-authors
9
list available
Contact
Publications
Resume (English only)
Academic Achievements
- - November 2024: Gave a talk at Inria Paris, hosted by the Argo research team, on "Enhancing Graph Neural Networks with Geometric Structure Analysis"
- - June 2024: New paper, "Cross-Modal and Multimodal Data Analysis Based on Functional Mapping of Spectral Descriptors and Manifold Regularization," published in Neurocomputing
- - March 2024: Gave a talk at Télécom Paris, hosted by the S2A team, on "Graph Representation Learning for Multimodal Data - Challenges and Innovative Methods"
- - April 2023: Paper "TIDE: Time Derivative Diffusion for Deep Learning on Graphs" accepted at ICML 2023
- - October 2022: Paper, "Geometric Multimodal Deep Learning With Multiscaled Graph Wavelet Convolutional Network," published in IEEE Transactions on Neural Networks and Learning Systems
Research Experience
- - Since April 2022, Postdoctoral Researcher in the GeomeriX group at the LIX research laboratory of École Polytechnique IP-Paris, working with Prof. Maks Ovsjanikov
- - Previously worked on various topics including chaotic time-series prediction, imbalanced data classification, neuro-fuzzy inference systems, and evolutionary computation
Education
- - Ph.D. in Computer Engineering-Artificial Intelligence from the University of Isfahan (UI), supervised by Prof. Peyman Adibi
- - Visiting researcher at GIPSA-Lab, Grenoble Institute of Technology, supervised by Prof. Jocelyn Chanussot, 2019 to 2020
Background
- A Postdoctoral Researcher in the GeomeriX group at the LIX research laboratory of École Polytechnique IP-Paris, since April 2022, working with Prof. Maks Ovsjanikov. My current research focuses on machine learning, particularly geometric deep learning, with an emphasis on graphs and multimodal data.
