Muhammad Rehman Zafar
Greetings! I am Muhammad Rehman Zafar, a researcher, educator, mentor, and technology consultant with a PhD in Electrical and Computer Engineering, specializing in Explainable Artificial Intelligence (XAI). My work spans academic research, industry collaboration, and higher education, with a strong focus on ethical, interpretable, and impactful AI systems.

Over the past decade, I have led initiatives in machine learning, big data analytics, and real-time AI deployment, and have taught a wide range of courses in data science, artificial intelligence and software engineering. I am passionate about bridging the gap between technology and business, mentoring the next generation of innovators, contributing to meaningful, and human-centered digital transformation.

Let's connect and explore how we can build a more intelligent, interpretable, and inclusive digital future.
Dr. Muhammad Rehman Zafar
Academic Positions
Sessional Faculty, University of Niagara Falls, Niagara Falls, ON, Canada (Jan 2026 – Present)
Contract Lecturer, Toronto Metropolitan University, Toronto, ON, Canada (Sep 2025 – Present)
Postdoctoral Researcher, Toronto Metropolitan University, Toronto, ON, Canada (Jun 2025 – Present)
Professor - Partial Load, Humber Polytechnic, Toronto, ON, Canada (Jan 2023 – Present)
Adjunct Faculty, Odette School of Business, University of Windsor, ON, Canada (Apr 2025 – Sep 2025)
Professor - Part-time, Seneca Polytechnic, Toronto, ON, Canada (Jan 2023 – Jun 2025)
Graduate Teaching Assistant, Toronto Metropolitan University, Toronto, ON, Canada (Jan 2018 – Apr 2022)
Recent Publications
Attentional Feature Fusion for Few-Shot Learning
Muhammad Rehman Zafar, Naimul Khan
International Joint Conference on Neural Networks (IJCNN). IEEE, 2024
Multilevel Stress Assessment From ECG in a Virtual Reality Environment Using Multimodal Fusion
Zeeshan Ahmad, Suha Rabbani, Muhammad Rehman Zafar, Syem Ishaque, Sri Krishnan, Naimul Khan
IEEE Sensors Journal, 2023
Deterministic Local Interpretable Model-Agnostic Explanations for Stable Explainability
Muhammad Rehman Zafar, Naimul Khan
Machine Learning and Knowledge Extraction (3)(3), 2021
DLIME: A Deterministic Local Interpretable Model-Agnostic Explanations Approach for Computer-Aided Diagnosis Systems
Muhammad Rehman Zafar, Naimul Khan
ACM SIGKDD Workshop on Explainable AI/ML (XAI), 2019
Fingerprint authentication and security risks in smart devices
Muhammad Rehman Zafar, Munam Ali Shah
22nd International Conference on Automation and Computing (ICAC). IEEE, 2016
Patents
Stress management in clinical settings
Edward W Biggs, Lowe Brianna, Justin Robert Caguiat, Naimul Mefraz Khan, Nabila Abraham, Muhammad Rehman Zafar, Syeda Suha Shee Rabbani, Zeeshan Ahmad, Mihai Constantin Albu, Jacky Zhang
United States Patent, 2021
Industry Collaborations
Stress management in clinical settings
Collaborating with Shaftesbury VR, we developed a machine learning model for multimodal assessment of stress. The multimodal sensors can be physiological (e.g. EEG, heart-rate) and behavioural (e.g. facial expressions). The target is to use the assessed stress for Shaftesbury's Positive Distraction Entertainment System which adapts game content dynamically to reduce stress in children before a complex medical procedure, which can reduce complexity and recovery time.
Research Grants and Funding
Support: Google Research Credits
Org: Google
Recipient
$7,000 CAD
2026
Cloud credits supporting scalable AI experimentation, data processing, and deployment pipelines for the Observatory on Immigration Discourses (IDIO).
Recipient $7,000 CAD 2026
Google Research Credits (Google)
Cloud credits supporting scalable AI experimentation, data processing, and deployment pipelines for the Observatory on Immigration Discourses (IDIO).
Awards and Scholarships
PhD:
  • Toronto Met Graduate Fellowships
  • Toronto Met Graduate Development Award
  • Toronto Met International Student Scholarship
  • Toronto Met Graduate Travel Award
Master's:
  • Gold Medal for Academic Excellence
  • Open merit scholarship
  • Magna Cum Laude honor
  • Recognized in Rector’s Honor list (2015-2017)
Academic Services
Reviewer International Joint Conference on Neural Networks (IJCNN) 2026 Conference IEEE
Reviewer International Joint Conference on Neural Networks (IJCNN) 2025 Conference IEEE
Reviewer Machine Learning Journal Springer
Reviewer Expert Systems with Applications Journal Elsevier
Reviewer Big Data Mining and Analytics Journal IEEE
Reviewer Journal of Immigrant & Refugee Studies Journal Taylor & Francis
Reviewer PLOS ONE Journal PLOS
Reviewer Heliyon Journal Cell Press
Reviewer Interpretable Machine Learning with Python Book Packt
Reviewer Human-in-the-Loop Machine Learning Book Manning
Reviewer International Joint Conference on Neural Networks (IJCNN) 2026 Conference IEEE
Reviewer International Joint Conference on Neural Networks (IJCNN) 2025 Conference IEEE
Reviewer Machine Learning Journal Springer
Reviewer Expert Systems with Applications Journal Elsevier
Reviewer Big Data Mining & Analytics Journal IEEE
Reviewer Journal of Immigrant & Refugee Studies Journal Taylor & Francis
Reviewer PLOS ONE Journal PLOS
Reviewer Heliyon Journal Cell Press
Reviewer Interpretable Machine Learning with Python Book Packt
Reviewer Human-in-the-Loop Machine Learning Book Manning Publications