Daniel Nayo
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Research Areas
Biography and Research Information
OverviewAI-generated summary
Daniel Nayo's research focuses on developing data-driven frameworks for improving healthcare resilience and mitigating losses in complex systems. His work includes a KPI-driven approach for the predictive allocation of temperature-sensitive medications, aiming to enhance shortage resilience in underserved U.S. ZIP codes. This research also explores actuarial-machine learning bridges for catastrophe loss mitigation, translating grid reliability concepts. Nayo has published on these topics, with his most recent work appearing in 2025.
Metrics
- Publications: 2
Selected Publications
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Predictive Allocation of Temperature-Sensitive Specialty Medications: A KPI-Driven Framework for Shortage Resilience in Underserved U.S. ZIP Codes (2025)
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Actuarial-ML Bridges for Catastrophe Loss Mitigation: Translating Grid Reliability (2025)
Collaboration Network
Top Collaborators
- Actuarial-ML Bridges for Catastrophe Loss Mitigation: Translating Grid Reliability
- Actuarial-ML Bridges for Catastrophe Loss Mitigation: Translating Grid Reliability
- Actuarial-ML Bridges for Catastrophe Loss Mitigation: Translating Grid Reliability
- Predictive Allocation of Temperature-Sensitive Specialty Medications: A KPI-Driven Framework for Shortage Resilience in Underserved U.S. ZIP Codes
- Predictive Allocation of Temperature-Sensitive Specialty Medications: A KPI-Driven Framework for Shortage Resilience in Underserved U.S. ZIP Codes
- Predictive Allocation of Temperature-Sensitive Specialty Medications: A KPI-Driven Framework for Shortage Resilience in Underserved U.S. ZIP Codes
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