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Trustworthy, privacy-preserving AI for healthcare.
My research focuses on developing trustworthy, privacy-preserving, and generalizable AI systems for healthcare and other high-impact data environments.
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View ProfileResearch statement
AI systems that are accurate, explainable, safe, and practical.
My research focuses on developing trustworthy, privacy-preserving, and generalizable AI systems for healthcare. I am interested in combining deep learning, federated learning, multimodal clinical data, agentic and LLM-based systems, and reliable data infrastructure to create models that are accurate, explainable, safe, and practical. My work examines how AI can support clinical decision-making and data collaboration while preserving privacy, reducing bias, and maintaining data integrity.
Long-term research vision: My long-term goal is to develop trustworthy artificial intelligence systems that improve healthcare decision-making through privacy-preserving machine learning, multimodal clinical analytics, and scalable data infrastructure.
Health AIDeep learningFederated learningExplainabilityFairnessData reliability
Research themes
Current and selected themes.
Collaborative Intelligence Databases
Privacy-preserving, multi-source data management using AI-based access control, federated learning, homomorphic encryption, differential privacy, and predictive analytics. Co-developed / co-authored.
Resilient Dataflow Intelligence
A proactive AI framework for anticipating anomalies in cloud databases using techniques such as LSTM and autoencoder-based detection. Co-developed / co-authored.
AI-Driven Liquid Biopsies and Multi-Omics
Research on the integration of ctDNA, microbiome, imaging, and related signals for cancer monitoring.
Agentic Clinical AI
Research interest in retrieval-augmented and tool-using systems for clinical documentation, EHR interaction, and multimodal reasoning.
Academic service
Peer-review recognition and scholarly service.
Web of Science reviewer recognition shows 6 peer review records across 6 manuscripts for 2025-2026, based on the reviewer-recognition summary supplied August 4, 2026.
Reviewer recognition
6 peer-review records
Reviewed manuscripts across Data Science Journal, International Journal on Data Science and Technology, Journal of Disease and Global Health, and Academia Environmental Sciences and Sustainability.
6records6manuscripts2025-2026period
Selected records
Web of Science peer-review service
- Jul 18, 2026Data Science Journal — KadiAssistant: A conversational AI Agent for information retrieval in Kadi4Mat.
- Mar 16, 2026International Journal on Data Science and Technology — Artificial Intelligence Adoption in Academic and Engineering College Libraries of Gujarat, India (2020-2026): A State-Level Review.
- Jan 20, 2026Journal of Disease and Global Health — Spatial Modeling of Agrochemical Exposure and Chronic Disease Risk Among U.S. Farmers: A Review of GIS-Based Epidemiological Approaches.
- Dec 17, 2025International Journal on Data Science and Technology — Divide and recombine approaches for fitting logistic regression to large-scale health surveillance data: application to diabetes risk prediction in BRFSS.
- Oct 27, 2025Academia Environmental Sciences and Sustainability — Analysis of AI Models in Weather Forecasts - A Leap Towards a Robust Future.
- Sep 23, 2025Academia Environmental Sciences and Sustainability — AI-Driven Greenhouse Gas (GHG) Emission Clustering and Forecasting.
Presentation
Selected presentation
Resilient Dataflow Intelligence: A Proactive AI Framework for Cloud Anomaly Detection - Bay Atlantic University, Washington, D.C. - 2024.
Service
Conference and professional service
Conference volunteer: IEEE CIC / TPS / CogMI 2024-2025; IEEE PES Energy & Policy Forum 2025; Coleridge Initiative 5th National Convening 2025; DevFest DC 2025.
Awards
Selected recognition.
Certificate of Academic Excellence
Bay Atlantic University
Certificate of Achievement
Bay Atlantic University
Volunteer Service Appreciation Award
IEEE CIC / CogMI / TPS