I am an AI governance and healthcare data privacy researcher with a legal-technical background and over three years of experience working at the intersection of artificial intelligence, regulatory compliance, and healthcare systems. My work focuses on auditing and monitoring AI systems in clinical and public-sector contexts, with particular emphasis on algorithmic bias, explainability, risk management, and regulatory alignment with frameworks such as HIPAA, FDA oversight, and international data protection standards.

I have authored multiple peer-reviewed publications on healthcare AI governance, clinical decision support systems, and ethical failures in high-risk AI, and I am an inventor on patented frameworks for governing AI in medical diagnostics through bias detection, explainability, and automated compliance verification. My research combines policy analysis with technical evaluation, including assessment of model robustness, data integrity, and real-world clinical impact.

In addition to my research, I actively serve as a peer reviewer for internationally recognized journals, evaluating manuscripts for methodological rigor, ethical compliance, statistical validity, and practical relevance in areas including healthcare AI, medical informatics, pharmacovigilance, and applied machine learning. I am particularly interested in reviewing work related to AI in healthcare, responsible AI, health policy, data governance, and patient safety.

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