Trustworthy AI · Cybersecurity · Governance
Intelligence you can verify.
Systems you can trust.
Fifteen years of research on explainable, privacy-preserving machine learning for the systems where failure is not an option. Healthcare, digital finance, critical infrastructure.
Assistant Professor, APU
PI, MATCH healthcare-AI project
TEDx speaker, 2024
Areas of work
Index / 01–06Trustworthy AI & Assurance
Explainability, fairness, calibrated uncertainty and robustness evaluation for safety-critical systems.
Privacy-Preserving ML
Federated learning, differential privacy and secure aggregation across organisations.
AI Governance & Regulation
Evaluation protocols, assurance cases, auditing and policy frameworks for responsible deployment.
Security Consulting
Security strategy, risk assessment and incident readiness aligned with your objectives.
Talks & Workshops
Expert-led sessions on AI, cybersecurity and emerging threats. Keynote to hands-on lab.
AI for Healthcare
Privacy-preserving diagnostics and models robust under real-world distribution shift.
Background
Rigour first. Rhetoric never.
My work leads interdisciplinary teams, secures competitive funding, and supervises postgraduate research to completion, grounding trustworthy AI in solid statistical methodology: explainability, fairness, calibrated uncertainty, and privacy.
Formerly Associate Fellow at the National University of Malaysia (UKM). Holder of the Taiwan Employment Gold Card; nominated Microsoft Innovative Educator Expert 2025/26.