Match tier
Likely match
Presence
Current · Arkansas
Last published
2026
Sources
OpenAlex · ORCID
Refreshed
2026-10-01
D
Dillon Wood
This is a likely match — the affiliation was inferred from OpenAlex, ORCID, and web sources but has not been fully confirmed. Treat with appropriate caution.
Role not yet determined Is this you? Add your title
1 pubs
Research Areas
Biography and Research Information
OverviewAI-generated summary
Dillon Wood's research focuses on machine learning applications for sustainable energy systems. Their work investigates anomaly detection in hydrogen fuel cells, aiming to enhance reliability and support the transition to cleaner energy sources. This research contributes to the development of more robust and efficient energy technologies.
Metrics
- Publications: 1
Selected Publications
-
Machine Learning-Based Anomaly Detection in Hydrogen Fuel Cells: A Path Toward Sustainable and Reliable Energy Systems (2026)
Collaboration Network
Top Collaborators
Benjamin Leon
Arkansas Tech University
1 shared publication
In database
- Machine Learning-Based Anomaly Detection in Hydrogen Fuel Cells: A Path Toward Sustainable and Reliable Energy Systems
Ethan Mashburn
Arkansas Tech University
1 shared publication
In database
- Machine Learning-Based Anomaly Detection in Hydrogen Fuel Cells: A Path Toward Sustainable and Reliable Energy Systems
Afsana Ahamed
Arkansas Tech University
1 shared publication
In database
- Machine Learning-Based Anomaly Detection in Hydrogen Fuel Cells: A Path Toward Sustainable and Reliable Energy Systems
Seyed Ehsan Hosseini
Arkansas Tech University
1 shared publication
In database
- Machine Learning-Based Anomaly Detection in Hydrogen Fuel Cells: A Path Toward Sustainable and Reliable Energy Systems
Similar Researchers
Based on overlapping research topics
Ethan Mashburn
Arkansas Tech University
Machine Learning Applications
Renewable Energy Integration
Semiconductor materials and devices
Benjamin Leon
Arkansas Tech University
Machine Learning Applications
Renewable Energy Integration
Semiconductor materials and devices
Wesley G. Schwartz
University of Arkansas at Fayetteville
Machine Learning Applications
Renewable Energy Integration
Semiconductor materials and devices
Yagub Suleymanov
Arkansas State University
Machine Learning Applications
Renewable Energy Integration
Aravind Sanikommu
Arkansas State University
Machine Learning Applications
Renewable Energy Integration
Ghaidaa Albayati
University of Arkansas at Little Rock
Renewable Energy Integration
Semiconductor materials and devices