My research grounds human-centric, trustworthy AI in rigorous statistical and data-science methodology, from privacy-preserving federated learning and explainability to the robustness of AI in healthcare and digital finance.
Making AI decisions transparent, fair and accountable, with calibrated uncertainty and human-AI collaboration.
Differential privacy, secure aggregation and distributed estimation for sensitive, multi-organization data.
Privacy-preserving diagnostics and models that remain robust under real-world distribution shift.
Evaluation protocols, assurance cases, auditing and policy frameworks for responsible AI.
Anomaly detection, behavioral analytics and trust-building frameworks for security decision-making.
Attack taxonomies and defence mechanisms for AI in medical imaging and other high-stakes settings.
Principal Investigator · European Commission (Horizon 2020) · MYR 300,000 · 2020–2022.
Principal Investigator · National Research Grant (FRGS) · MYR 150,000 · 2014–2017.
Co-Investigator · International Research Grant · MYR 180,000 · 2023–2025.
Co-Investigator · CREST Grant · MYR 200,000 · 2021–2024.
Author shown in bold. A complete, categorised publication list is available on request and via Google Scholar.
Safavi, S., & Shukur, Z. (2014). Conceptual privacy framework for health information on wearable devices. PLoS ONE, 9(12), e114306. Q1
Safavi, S., Abdulnabi, M. S. H., Rana, M. E., & Alizadeh, S. (2025). From black box to trustworthy AI: A secure framework for explainable cybersecurity decision-making. 2025 Int. Conf. on Advancements in Smart, Secure and Intelligent Computing (ASSIC), 1–4. IEEE.
Mohan, M. H., Seeboruth, K., Rana, M. E., Umar, U. S., Mohan, T., & Safavi, S. (2025). Enhancing fetal health assessment: Automated head circumference measurement via deep learning segmentation. 2025 ASSIC, 1–8. IEEE.
Chandran, A. L., Samual, J., Safavi, S., & Ali, A. (2025). A comparative analysis of machine learning models for detecting malware in Android devices. Journal of Cyber Security and Risk Auditing, 4, 327–346.
EL Bakkali, J., EL Bardouni, T., Safavi, S., et al. (2016). Behaviors of percentage depth dose curves: A Monte Carlo Geant4 study. Radiation Physics and Chemistry, 125, 199–204. Q2
Safavi, S., Shukur, Z., & Razali, R. (2013). Reviews on cybercrime affecting portable devices. Procedia Technology, 11, 650–657. Elsevier.
Safavi, S., & Shukur, Z. (2015). CenterYou: A permission-based privacy framework (pseudo-data technique) in Android. Malaysian Patent No. 710420-12-5,412. Patent
Attack taxonomies and defence mechanisms. Q1, in process.
Gumbel-Softmax + cascaded Swin Transformer framework. Q1, accepted.
Behavioral analytics in cybersecurity. Q2, in process.
Hands-on workshops and video series across trustworthy AI, machine learning, intrusion detection, penetration testing and more.
My curated toolkit for the full research journey, from literature discovery to publication, impact and research ethics.
I welcome research partnerships, co-supervision and joint projects across trustworthy AI, federated learning and cybersecurity.
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