Muhammad Rehman Zafar
Overview
Dr. Muhammad Rehman Zafar is an Assistant Professor in the Department of Information Technology Management at the Ted Rogers School of Management, Toronto Metropolitan University (TMU). Prior to joining the faculty, he served as a Postdoctoral Research Fellow in the Department of Electrical, Computer, and Biomedical Engineering and was a member of the TMU Multimedia Research Laboratory.
Dr. Zafar holds a Ph.D. in Electrical and Computer Engineering with a specialization in Explainable Artificial Intelligence (AI). He brings over a decade of experience spanning academia and industry, with expertise in AI, applied machine learning, data science and software engineering. His research focuses on Explainable AI, Responsible AI, Interpretable Machine Learning, and Data Science, with an emphasis on developing transparent, trustworthy, and impactful AI systems for real-world applications.
His interdisciplinary research lies at the intersection of AI and data science, where he develops AI-powered tools and platforms that enable researchers, organizations, and decision-makers to generate actionable insights and support evidence-based decision-making.
Through his research and teaching, he is committed to advancing responsible AI that promotes fairness, accountability, transparency, and human-centered decision-making in complex socio-technical systems.
Explainable Artificial Intelligence, Responsible AI, Interpretable Machine Learning, Data Science
Publications
- Muhammad Rehman Zafar, Naimul Khan (2024). Attentional Feature Fusion for Few-Shot Learning (external link) . International Joint Conference on Neural Networks (IJCNN). IEEE.
- Zeeshan Ahmad, Syeda Rabbani, Muhammad Rehman Zafar, Syem Ishaque, Sridhar Krishnan, Naimul Khan (2023). Multi-level Stress Assessment from ECG in a Virtual Reality Environment using Multimodal Fusion (external link) . IEEE Sensors Journal.
- Muhammad Rehman Zafar, Naimul Mefraz Khan (2021). Deterministic Local Interpretable Model-Agnostic Explanations for Stable Explainability (external link) . Machine Learning and Knowledge Extraction 3, 525-541.
- Muhammad Rehman Zafar, Naimul Mefraz Khan (2019). DLIME: A Deterministic Local Interpretable Model-Agnostic Explanations Approach for Computer-Aided Diagnosis Systems (external link) . In Proceedings of Anchorage’19: ACM SIGKDD Workshop on Explainable AI/ML (XAI) for Accountability, Fairness, and Transparency (Anchorage’19).
- Muhammad Rehman Zafar and Munam Ali Shah (2016). Fingerprint authentication and security risks in smart devices (external link) . 22nd International Conference on Automation and Computing (ICAC), pp. 548-553. IEEE, 2016.
Patents
- Biggs, Edward W., L. O. W. E. Brianna, Justin Robert Caguiat, Naimul Mefraz Khan, Nabila Miriam Abraham, Muhammad Rehman Zafar, Syeda Suha Shee Rabbani, Zeeshan Ahmad, Mihai Constantin Albu, and Jacky Zhang. Stress management in clinical settings. (external link, opens in new window) U.S. Patent Application 16/663,223 filed April 29, 2021.
| Name | Year | Value |
| Google Research Credits | 2026 | $7,000 |