Dr. Noha Hassan, P.Eng.,  SMIEEE

AI, Optimization, and Quantum Computing.

I develop machine learning, artificial intelligence (AI), and optimization methods for safety-critical, large-scale wireless and intelligent transportation systems, working with real-world operational data to translate theory into deployable solutions that improve system safety, efficiency, and resilience.

My experience as a grant proposal reviewer has given me a strong understanding of how research programs are evaluated in terms of clarity, feasibility, student training, and long-term impact. 

My research program is structured to be competitive for NSERC funding, while providing rich supervision opportunities for STEM graduate students interested in applied AI, optimization, and safety-critical systems. 

My research agenda is focused on building a sustainable, externally funded program in applied AI and optimization, with strong opportunities for partnerships with municipal authorities, public-sector stakeholders, local industry, and entrepreneurial services.

noha.hassan@torontomu.ca
Dr. Noha Hassan, P.Eng. - Quantum Machine Learning & Wireless Networks Expert

About Dr. Hassan

Dr. Noha Hassan is a distinguished researcher and educator with 18 years of research experience and 11 years of teaching experience. Currently serving as a Senior Research Associate at Carleton University's NTN Lab, working with Chancellor's Professor Halim Yanikomeroglu. Her work focuses on applying machine learning and AI models to improve global connectivity applications.
She is also a Cross-University Postdoctoral Fellow (Toronto Metropolitan University and Florida Polytechnic University), analyzing connected vehicle (CV) real-world sensitive dataset from the US Department of Transportation (USDOT) to study system timing.

Her research includes developing machine learning models that predict warning time insufficiency before vehicle entry to the workzone, revealing systematic temporal failures.

Her research addresses real-world challenges faced by public-sector stakeholders, including transportation safety, spectrum efficiency, and resilient wireless systems. By working with operational data from government and municipal partners, she translates theory into solutions that inform policy, infrastructure planning, and safety-critical decision-making.

Education & Credentials

  • Ph.D. Electrical & Computer Engineering (2019, GPA 4.25/4.35)
  • Professional Engineer License (P.Eng.), Ontario

Professional Memberships

SMIEEE • OSPE • PEO


Quantum Machine Learning

Artificial Intelligence & LLMs

5G/6G Networks

Wireless Communications

Edge Computing

Evolutionary Computation

VANET Safety Systems

Optimization Algorithms

Research grounded in theory,
Driven by real-world impact.

I

believe that impactful research lies at the intersection of rigorous analytical foundations and real-world relevance. My work focuses on distilling actionable insight from large-scale systems, advancing AI-driven optimization methods that are both theoretically grounded and practically deployable.



Current Research & Leadership Roles

Research Associate

July 2025 - Current

Carleton NTN Lab
Senior Research Associate working with Chancellor's Professor Halim Yanikomeroglu on Non-Terrestrial Networks, pioneering NTN-terrestrial integration technology to enable seamless global connectivity and next-generation communication systems.

Academic Associate

Aug. 2025 - Current

Toronto Metropolitan University-
Florida Polytechnic University

Cross University Postdoc fellow on connected vehicle (CV) safety, analyzing real-world CV data of the 27 Milion project in Tampa and Wyoming from the US Department of Transportation (USDOT) to study warning time sufficiency, and highway safety risks.

Academic Associate

Sept. 2025 - Current

Puducherry Technological University

Internationally supervising and providing technical advice for master’s and Ph.D. students on a research project focused on developing a SUMO-based simulation framework and applying machine learning techniques to predict the performance of the Tampa Connected Vehicle (CV) pilot project..

Active Research Frontiers

  • Developing quantum machine-learning algorithms for massive Reconfigurable Intelligent Surfaces (RIS) deployments to revolutionize wireless network optimization
  • Analyzing real-world Connected Vehicle (CV) V2X data to enhance highway safety systems and reduce collision rates through intelligent predictive analytics
  • Proposing quantum meta-learning frameworks to prevent AI hallucinations in wireless communication systems, ensuring reliable and trustworthy AI-driven networks

Research Impact & Publications

7+ Papers Under Review
10+ Journals
12+ Int'l Conferences
Under Review

A Unified Association Method to Leverage
Multi-Band Capabilities in 6G

IEEE Transactions on Wireless Communications

(Under second round of review with promising potential for acceptance)

Under Review

Warning Time Sufficiency: A Field Study of the
Tampa THEA Connected Vehicle Pilot

IEEE Transactions on Intelligent Vehicles

(Under second round of revie with promising potential for acceptance)


Under Review

Detection: A Case Study on GPT-Neo

IEEE Transactions on Emerging Topics in Computing


Under Review

Quantum Episodic Meta-Learning for AI-Enabled
Simultaneously Transmitting and Reflecting
Reconfigurable Intelligent Surfaces

IEEE Transactions on Machine Learning in Communications and Networking


Under Review

Joint Optimization of Element Spacing and Phase
Shifts in Double-Sided Reconfigurable Intelligent
Surfaces Using Quantum Graph Neural Networks

ICC 2026


Under Review

Path-Based Quantum Meta-Learning for Adaptive
Optimization of Reconfigurable Intelligent
Surfaces

IEEE Wireless Communications Letters


Published

Interference Mitigation and Dynamic UserAssociation for Load Balancing in
Heterogeneous Networks

IEEE Transactions on Vehicular Technology, 2019


Let's Collaborate

-Interested in research collaboration, academic partnerships, or consulting opportunities? I'm always excited to discuss innovative projects in quantum computing, wireless networks, and AI applications.

-I welcome MSc and PhD students interested in applied AI, optimization, wireless communications, and safety-critical systems. Students in my group gain experience working with real operational data, interdisciplinary teams, and funding applications.

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Current Positions

Senior Research Associate (Carleton NTN Lab)

Available for virtual meetings and consultations by appointment.

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Ready to Innovate Together

Let's explore cutting-edge research opportunities