Daniel Buckland

Assistant Professor

Duke University

United States

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Daniel Buckland is an Assistant Professor at Duke University in the United States. His research areas encompass space exploration, medical risk assessment, and the integration of advanced technologies in emergency response. Recent articles authored by Professor Buckland explore topics such as the optimization of drone networks for healthcare delivery, privacy-preserving frameworks in federated systems, and the application of machine learning in medical settings.

Articles (13)

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2024

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2024

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2024

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2023

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2023

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2023

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2022

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2022

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2022

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2021

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2021

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2021

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2021

Collaborators (7)

Siobhan Oca

Duke University

UNITED STATES

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Erik Antonsen

Associate Professor

Baylor College of Medicine

UNITED STATES

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Bibhas Chakraborty

National University of Singapore (NUS)

SINGAPORE

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Cynthia Green

Associate Professor

Duke University School of Medicine

UNITED STATES

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Nan Liu

Associate Professor

National University of Singapore (NUS)

SINGAPORE

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Feng Xie

Assistant Professor

University of Minnesota Twin Cities

UNITED STATES

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Hamed Zaribafzadeh

Duke University

UNITED STATES

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Daniel Buckland

Research Interests

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Articles (13)

Incorporation of Drone Technology Into the Chain of Survival for OHCA: Estimation of Time Needed for Bystander Treatment of OHCA and CPR Performance

Expiration analysis of the International Space Station formulary for exploration mission planning

Levels of evidence for human system risk evaluation

FedScore: A privacy-preserving framework for federated scoring system development

Updates to the NASA human system risk management process for space exploration

Optimizing a Drone Network to Respond to Opioid Overdoses

Development and Testing of a Durable and Novel Breast Phantom for Robotic Autonomous Ultrasound Systems

Estimation of Surgery Durations Using Machine Learning Methods-A Cross-Country Multi-Site Collaborative Study

Estimating medical risk in human spaceflight

Development and Validation of Machine Learning Models to Predict Admission From Emergency Department to Inpatient and Intensive Care Units

CPT to RVU conversion improves model performance in the prediction of surgical case length

Creation of an Automated Fluorescence Guided Tumor Ablation System

Touch-Point Detection Using Thermal Video With Applications to Prevent Indirect Virus Spread

Collaborators (7)

Siobhan Oca

Erik Antonsen

Bibhas Chakraborty

Cynthia Green

Nan Liu

Feng Xie

Hamed Zaribafzadeh

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