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Peer-Reviewed Research & White Papers

A curated selection of published research focusing on Explainable AI (XAI), synthetic data generation, and biomedical informatics.

Towards responsible AI: an implementable blueprint for integrating explainability and social-cognitive frameworks in AI systems (2025)

• Impact: Provides an actionable framework for developers and organizations to build ethical, transparent, and socially-aware artificial intelligence systems.

• Publication: AI Perspectives & Advances

• Read Here 

Laboratories of Harm Reduction and Treatment: Determinants of States' Policy Adoptions to Address the Opioid Epidemic (2026)

• Impact: A data-driven analysis exploring how public health policies and state-level data informatics influence harm reduction strategies.

• Publication: Journal of Health Politics, Policy and Law

• Read Here

CEFEs: a CNN explainable framework for ECG signals (2021)

• Impact: Introduces a novel deep learning framework that makes convolutional neural networks interpretable for cardiac health diagnostics, ensuring clinicians can trust AI predictions.

• Publication: Artificial Intelligence in Medicine

• Read Here

Generating healthcare time series data for improving diagnostic accuracy of deep neural networks (2021)

• Impact: Demonstrates how synthetic data generation can safely overcome data scarcity and privacy constraints to train highly accurate medical AI models.

• Publication: IEEE Transactions on Instrumentation and Measurement

• Read Here

Exploring a global interpretation mechanism for deep learning networks when predicting sepsis (2023)

• Impact: Develops global interpretability methods for deep learning models tasked with the early, life-saving prediction of sepsis in clinical settings.

• Publication: Scientific Reports (Nature)

• Read Here