Expert in both research and real-time AI implementation. Developing trustworthy AI systems for healthcare with proven experience in academic research and industry deployment.
Trustworthy AI systems for healthcare, maternal health prediction, and medical diagnosis
Fairness-aware ML, algorithmic bias reduction, and equitable AI systems
Medical imaging, multimodal diagnosis, and explainable AI visualization
Medical cyber-physical systems, environmental monitoring, and edge AI
Healthcare Analytics 2024, 100285
IEEE ISCC 2023, pp. 1–6
PLOS ONE (Scopus-indexed)
Open-source bias mitigation toolkit for medical AI with 4 fairness metrics, achieving 30.8% bias reduction.
ML-based risk stratification system achieving 99% accuracy with homomorphic encryption for privacy.
CheXNet-based system for 14 thoracic diseases with bias analysis and fairness constraints.
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