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YAYA ETIABI

@yayaetiabi

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I build privacy-preserving machine learning for wireless and IoT systems.

Canada

Message

What I'm looking for

I'm looking to advance applied machine learning research for wireless systems and IoT, especially federated learning, privacy-preserving localization, and intelligent wireless networks.

I'm developing and benchmarking federated learning and meta-reinforcement learning algorithms for wireless resource management at Université du Québec à Montréal. My work evaluates performance across heterogeneous wireless network scenarios and explores joint sensing, communication, and computation optimization.

During my Ph.D. research at Mohammed VI Polytechnic University, I built and benchmarked machine learning pipelines for cooperative, privacy-preserving wireless localization in IoT. I published eight peer-reviewed papers spanning federated learning, meta-learning, graph neural networks, and deep reinforcement learning.

At LISITE Laboratory–ISEP, I designed federated meta-learning frameworks for privacy-preserving indoor localization and produced two IEEE journal publications. I also teach programming, machine learning, and AI, and support an international AI learning community through Black in AI.

Experience

Work history, roles, and key accomplishments

Current

Postdoctoral Researcher

Current

Université du Québec à Montréal

Aug 2025 - Present (1 year 1 month)

Developing and benchmarking federated learning and meta-reinforcement learning algorithms for wireless resource management. Designing experimental evaluation pipelines and researching joint optimization of sensing, communication, and computation in intelligent wireless networks.

Federated Learning Meta Reinforcement Learning Wireless Resource Management Experimental Design Python PyTorch

Ph.D. Researcher

Mohammed VI Polytechnic University

Jan 2020 - Jan 2025 (5 years)

Conducted doctoral research on ML-driven cooperative and privacy-preserving wireless localization in IoT, spanning federated learning, meta-learning, graph neural networks, and deep reinforcement learning. Designed, implemented, and benchmarked ML pipelines in Python and PyTorch, publishing 8 peer-reviewed papers.

Federated Learning Meta Learning graph neural networks Deep reinforcement learning Python PyTorch Wireless Localization IoT

Visiting Researcher

ISEP - Institut Supérieur d'Électronique de Paris

Jan 2023 - Jun 2023 (5 months)

Designed and evaluated federated meta-learning frameworks for generalizable, privacy-preserving indoor localization in IoT. Implemented and compared multiple ML baselines across real-world heterogeneous building datasets using Python and PyTorch, producing two IEEE publications.

Federated Meta Learning Indoor Localization IoT Python PyTorch Model Evaluation

Research Intern

Mohammed VI Polytechnic University

Feb 2020 - Aug 2020 (6 months)

Developed a cooperative IoT localization algorithm through systematic literature review, MATLAB simulations, and experimental data validation.

Matlab IoT Localization literature review Experimental Validation

Education

Degrees, certifications, and relevant coursework

Mohammed VI Polytechnic University

Doctor of Philosophy, Computer Science

2020 - 2025

Ph.D. in Computer Science with a thesis on advanced machine learning for collaborative and privacy-preserving wireless localization in IoT networks.

Hassan II University

Bachelor of Engineering, Electrical Engineering

2017 - 2020

Electrical Engineering Degree with a focus on Embedded Systems and Telecommunications, including a thesis on distributed cooperative localization in IoT environments.