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Gina Cody Research and Innovation Fellowships Recipients

These researchers have been selected for the Gina Cody Research and Innovation Fellowships to support their research and innovation projects with the potential to build meaningful partnerships with industrial and government agencies.

From left: Mohsen Ghafouri, Fuzhan Nasiri, Mourad Debbabi, Govind Gopakumar, Abdelwahab Hamou-Lhadj, Khaled Galal, Sébastien Le Beux, Nhat Truong Nguyen, Emad Shihab, Arash Mohammadi, Jerin John and Jinqiu Yang.

2023-2024 fellowship recipients

Khaled Galal
Department of Building, Civil and Environmental Engineering
Innovative Low-Carbon Building System

Mohsen Ghafouri
Concordia Institute for Information Systems Engineering
Emerging Solutions for Secure Operation of Large-Scale Wind Energy Sources in Smart Grids

Govind Gopakumar
Centre for Engineering in Society
Community-Led Technology Innovation: Partnership with Indigenous Community to Develop a Sustainable and Sovereign Agri-Food Hub

Abdelwahab Hamou-Lhadj
Department of Electrical and Computer Engineering
TRUST-SoS: Improving the Trustworthiness of Systems-of-Systems Using Statistical Model Checking and Runtime Analysis

Mahdi Hosseini
Department of Computer Science and Software Engineering
Foundational Modelling of Histopathology Tissues in Computational Pathology

Jerin John
Department of Mechanical, Industrial and Aerospace Engineering
Design and Development of a GOX/GCH4 Torch Igniter for First-Stage Rocket Propulsion System

Ida Karimfazli
Department of Mechanical, Industrial and Aerospace Engineering
Unleashing Solar Energy: The Potential of Perovskite Solar Cells and the Challenge of Temperature Management

Sébastien Le Beux
Department of Electrical and Computer Engineering
Toward a Fault Tolerant Hyper Dimensional Computing Accelerator for Satellite Network Management Systems

Arash Mohammadi
Concordia Institute for Information Systems Engineering
Audiovisual Perceptual Systems for Sport Analytics

Fuzhan Nasiri
Department of Building, Civil and Environmental Engineering
Mapping of the Characteristics of HVAC Duct Insulation Used in Building Sector in Canada Using a Life Cycle Costing/Assessment (LCCA)

Nhat Truong Nguyen
Department of Chemical and Materials Engineering
Economically Beneficial Coating Processes for Self-Lubricating Systems in Gas Turbine Engines

Jinqiu Yang
Department of Computer Science and Software Engineering
Robust Internet-of-Things System

Previous recipients

Nizar Bouguila
Professor, Concordia Institue for Information Systems Engineering
AI for Historical Tourism
Tse-Hsun Chen
Assistant Professor, Computer Science and Software Engineering
Intelligent Software Development in Continuous Integration
Mojtaba Kheiri
Assistant Professor, Mechanical, Industrial and Aerospace Engineering
Fluid-Structure Interactions in Soft Surgical Robots for Minimally Invasive Surgery
Yan Liu
Associate Professor, Electrical and Computer Engineering
Recommend Proactive Planning Strategy Based on Heterogeneous Spatial-Temporal Data
Suryadipta Majumdar
Assistant Professor, Concordia Institute for Information Systems Engineering
Security Monitoring for Autonomous Smart Things in IoT: Application to Smart Cities
Farnoosh Naderkhani
Assistant Professor, Concordia Institute for Information Systems Engineering
Advanced and intelligent quality control and CBM frameworks via high-dimensional and multi-modal streaming sensory data
Muthukumaran Packirisamy
Professor, Mechanical, Industrial and Aerospace Engineering
Noninvasive Remote Distance Printing of Body Parts inside Body using Direct Sound Printing
Ahmed Soliman
Associate Professor, Building, Civil, and Environmental Engineering
5G-Friendly Eco-Building Materials for Smart Cities
Yang Wang
Associate Professor, Computer Science and Software Engineering
Learning AI Models for Edge-Device Applications via Cloud-Edge Collaboration
Zhibin Ye
Professor, Chemical and Materials Engineering
Developing Novel Polyolefin Oils for Fluid-Based Accommodating Intraocular Lenses
Wei-Ping Zhu
Professor, Electrical and Computer Engineering
Machine Learning-Enhanced RF Sensing for 5G and beyond Wireless Networks
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