Maysam Behmanesh

News

• 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" (slides).

• Our new work entitled "Smoothed Graph Contrastive Learning via Seamless Proximity Integration", joint with Maks Ovsjanikov is now available as a preprint.

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

• Our new work entitled "TIDE: Time Derivative Diffusion for Deep Learning on Graphs", joint with Maximilian Krahn and Maks Ovsjanikov is now available as a preprint.

• Our paper, Geometric Multimodal Deep Learning With Multiscaled Graph Wavelet Convolutional Network (joint with Peyman Adibi, Saeed Ehsani, and Jocelyn Chanussot) is published in IEEE Transactions on Neural Networks and Learning Systems. PDF

• I have started a new journey as a Postdoctoral Researcher in the GeoViC group at the LIX research laboratory of École Polytechnique.

• I successfully defended my PhD dissertation with the "Excellent" degree.

• I gave a talk on Geometric multimodal deep learning for the annual research week seminar at the University of Isfahan (slides).

• Our new work entitled "Geometric Multimodal Deep Learning with Multi-Scaled Graph Wavelet Convolutional Network", joint with Peyman Adibi, Jocelyn Chanussot, and Saeed Ehsani is now available as a preprint.

• Our new work on Cross-Modal and Multimodal Data Analysis (joint with Peyman Adibi, Jocelyn Chanussot, and Saeed Ehsani) is available as a preprint.

• I gave a talk at CE department: Geometric multimodal learning adjusted on manifolds (slides).

• Our paper, Geometric Multimodal Learning Based on Local Signal Expansion for Joint Diagonalization (joint with Peyman Adibi, Jocelyn Chanussot, Christian Jutten, and Saeed Ehsani) is published in IEEE Transactions on Signal Processing. PDF

• Our work on Imbalanced Data Classification (joint with Peyman Adibi and Hessein Karshenas) is available as a preprint.

• I started a new position as a visiting scholar in GIPSA-Lab with Grenoble Institute of Technology, Grenoble, France.

• I have been awarded a scholarship by the Ministry of Science, Research and Technology (MSRT) of Iran to carry out part of my ongoing PhD. research study as a visiting scholar. This scholarship is awarded to a limited number of students.

• I was invited by Prof. Jocelyn Chanussot to join the OSUG group in GIPSA-Lab with Grenoble Institute of Technology, Grenoble, France.

• I gave an oral presentation at 24th National CSI Computer Conference, CSICC-2019, Sharif University of Technology, Tehran.

• Our conference paper, Geometric Learning of Multimodal Data for Semisupervised Domain Adaptation with Simultaneous Diagonalization of Laplacianes (joint with Peyman Adibi) has been accepted for oral presentation at 24th National CSI Computer Conference, CSICC-2019.

• Holding a workshop on artificial intelligence: Geometric Learning: Multimodal, Multi Kernel, and Deep Perspective (joint with Peyman Adibi and Zahra Hanifelou).

• I pass my thesis proposal defense on Using Geometric Structure of Data for Multimodal Manifold Learning.

• I successfully passed comprehensive Ph.D. exam with rank 1st among all Artificial Intelligence PhD. students (2016).

• During two semesters, I successfully passed 9 PhD-courses including, Advanced in Pattern Recognition, Advanced in Data Mining, Advanced in Image processing, Advanced in Machine Learning, Advanced in Neural Networks, and Advanced in Machine Vision.

• I started my Ph.D. program in Computer Engineering (Artificial Intelligence) at University of Isfahan (UI), one of the most prestigious and beautiful universities in Iran.