Brian Eubanks

OverviewAI-generated summary

Brian Eubanks' research focuses on the reliability of computational systems, particularly in the context of machine learning and cyber-physical systems. His work investigates methods to ensure the accuracy and dependability of machine learning algorithms, even when faced with errors. This includes examining the performance of tiny machine learning (TinyML) algorithms under specific error conditions, such as control flow errors.

Eubanks also studies techniques for improving the resilience of cyber-physical systems. He has explored co-recovery mechanisms, specifically a solo-checkpointing approach, designed to enhance system reliability. His collaborations include work with Khalil Oumimoun and William Kwabla at the University of Central Arkansas, with whom he has co-authored publications. Eubanks' recent publications in 2021 highlight his engagement with these topics, including applications in high-energy physics event classification using boosted decision trees on FPGAs.

Metrics

• h-index: 2
• Publications: 3
• Citations: 31