Brian Eubanks Data-verified
Affiliation confirmed via AI analysis of OpenAlex, ORCID, and web sources.
Researcher
grad_student
Research Areas
Biography and Research Information
OverviewAI-generated summary
Brian Eubanks is a graduate student at the University of Central Arkansas whose research interests span diverse topics, including smart grid security, particle physics, and blockchain technology. His work emphasizes both theoretical analysis and practical applications, as seen in his exploration of advanced queuing theory and radiation effects in electronics. Eubanks has recently published on machine learning event classification using boosted decision trees in high energy physics. He also investigates the reliability of tiny machine learning algorithms and co-recovery mechanisms for cyber-physical systems. His current research is focused on enhancing the security and resilience of critical infrastructure through interdisciplinary approaches.
Metrics
- h-index: 1
- Publications: 3
- Citations: 30
Selected Publications
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Reliability Assessment of Tiny Machine Learning Algorithms in the Presence of Control Flow Errors (2021)
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A Solo-Checkpointing Co-Recovery Mechanism for Reliability Improvement of Cyber-Physical Systems (2021)
Collaboration Network
Top Collaborators
- A Solo-Checkpointing Co-Recovery Mechanism for Reliability Improvement of Cyber-Physical Systems
- Reliability Assessment of Tiny Machine Learning Algorithms in the Presence of Control Flow Errors
- Nanosecond machine learning event classification with boosted decision trees in FPGA for high energy physics
- Nanosecond machine learning event classification with boosted decision trees in FPGA for high energy physics
- Nanosecond machine learning event classification with boosted decision trees in FPGA for high energy physics
- Nanosecond machine learning event classification with boosted decision trees in FPGA for high energy physics
- Nanosecond machine learning event classification with boosted decision trees in FPGA for high energy physics
- Nanosecond machine learning event classification with boosted decision trees in FPGA for high energy physics
- A Solo-Checkpointing Co-Recovery Mechanism for Reliability Improvement of Cyber-Physical Systems
- A Solo-Checkpointing Co-Recovery Mechanism for Reliability Improvement of Cyber-Physical Systems
- A Solo-Checkpointing Co-Recovery Mechanism for Reliability Improvement of Cyber-Physical Systems
- A Solo-Checkpointing Co-Recovery Mechanism for Reliability Improvement of Cyber-Physical Systems
- Reliability Assessment of Tiny Machine Learning Algorithms in the Presence of Control Flow Errors