I'm Dr. Seyedmostafa Safavi, an AI and cybersecurity researcher with 15+ years across academia and industry. My work advances human-centric, trustworthy AI: explainability, fairness, privacy-preserving federated learning, and the robustness of AI in safety-critical domains such as healthcare and digital finance.
Accomplished AI and cybersecurity researcher with a track record of leading interdisciplinary teams, securing competitive funding, publishing in Q1 journals, and supervising postgraduate students to completion. My research grounds trustworthy AI, explainability, fairness, calibrated uncertainty, and privacy, in solid statistical and data-science methodology.
Former Associate Fellow (Associate Professor) at the National University of Malaysia (UKM) and currently Assistant Professor at Asia Pacific University (APU), where I serve as Principal Investigator of the MATCH multi-agent healthcare-AI project. I hold the Taiwan Employment Gold Card and am a nominated Microsoft Innovative Educator Expert (2025/2026).
From AI assurance to hands-on security, each engagement is crafted to strengthen your posture and meet global standards.
Explainability, fairness, calibrated uncertainty and robustness evaluation for AI in safety-critical systems.
Federated learning, differential privacy and secure aggregation for sensitive data across organizations.
Evaluation protocols, assurance cases, auditing and policy frameworks for responsible AI deployment.
Customized security strategy, risk assessment and incident readiness aligned with your objectives.
Practical, expert-led sessions on AI, cybersecurity and emerging threats, from keynote to hands-on lab.
Privacy-preserving diagnostics and models that stay robust under real-world distribution shift.
I deliver precision-driven, evidence-based solutions aligned with your vision, from academic collaboration to applied security.
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