I am a Ph.D. student at ÉTS Montréal supervised by professors Georges Kaddoum, specializing in Artificial Intelligence (AI) and 6G vehicular networks (V2X). My research focuses on leveraging AI-driven technologies to address challenges in next-generation network management, particularly in the areas of digital twin and multimodal collaborative perception for 6G V2X network management and radio resource management. My recent work includes the development of AI solutions using Vision Transformers for 6G vehicular networks.
My academic journey has been shaped by hands-on experiences across various industries, including the development of AI-based property valuation models and NLP solutions for healthcare. I have also contributed to research on the robustness of AI classifiers against adversarial attacks during my time as a visiting student at the University of California, Irvine. With a strong background in deep learning, my work bridges practical AI applications with theoretical advancements.
Outside of research, I have a passion for teaching and mentoring, currently serving as a Teaching Assistant at ÉTS. With a commitment to advancing the field of AI and its real-world applications, I aim to continue contributing to both academia and industry.
I am a Ph.D. student at ÉTS Montréal supervised by professors Georges Kaddoum, specializing in Artificial Intelligence (AI) and 6G vehicular networks (V2X). My research focuses on leveraging AI-driven technologies to address challenges in next-generation network management, particularly in the areas of digital twin and multimodal collaborative perception for 6G V2X network management and radio resource management. My recent work includes the development of AI solutions using Vision Transformers for 6G vehicular networks. GPA: 3.96/4.3.
Quebec Inter-University Transfer (IUT) student. Courses: COMP596(A-) ECSE507(A+) ECSE610(A-).
Wireless Communication, 6G V2X RRM, GPA 3.84/4.3.
Main courses: Stochastic Processes, Operational Research, Mathematical Optimization, Numerical Analysis, Data Analysis, Machine Learning, Signal Processing, Wireless Communication. Graduation Grade: 18/20.
Developed a data-driven property valuation solution for the real estate industry using deep learning as part of a Mitacs Accelerate internship.
Conducted research on AI classifiers’ robustness to adversarial attacks for my bachelor graduation project under the supervision of Professor Zhiying Wang.
Developed a graph partitioning optimization solution using deep learning for FPGA-based processor emulation.
Developed a credit decision-making solution using machine learning, focusing on the imbalanced data problem.
Developed a machine learning-based framework for customer satisfaction analysis and churn detection for a banking institution.
For the most up-to-date list of publications, see my Google Scholar profile.
| 2024 | MVX-ViT: Multimodal Collaborative Perception for 6G V2X Network Management Decisions Using Vision Transformer. G. Gharsallah and G. Kaddoum. |
| 2023 | ViT LoS V2X: Vision Transformers for Environment-aware LoS Blockage Prediction for 6G Vehicular Networks. G. Gharsallah and G. Kaddoum. |
| 2020 | Bachelor Thesis: AI Classifiers Robustness to Adversarial Attacks: Manipulating Output of Classifiers by Small Input Perturbations. G. Gharsallah and Z. Wang. |
MVX is the world’s first configurable and scalable co-simulation framework that incorporates realistic physical world simulation with a differentiable accurate ray tracing wireless simulation that generates multi-agent and multimodal datasets for AI-driven digital twin applications in vehicular communication systems.
LaCIME - ÉTS - École de technologie supérieure
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