Mohammed Alabsi

Mohammed Alabsi is an Associate Professor in the Department of Mechanical Engineering at The College of New Jersey (TCNJ). His research focuses on machinery, big data analytics, cyber-physical systems, and unmanned aerial vehicle (UAV) guidance, navigation, and control (GNC). He teaches courses such as Dynamic Systems and Control, Strength of Materials, and Mechatronics.

Research Interests

• Deep learning for fault diagnosis in machinery
• Generative adversarial networks for cross-domain fault detection
• Real-time system identification and control of UAVs
• Integration of handcrafted features with deep learning models

Publications Trends
Alabsi's work emphasizes deep learning applications in fault diagnosis and control systems for mechanical and aerospace engineering. His research spans convolutional neural networks, transfer learning, and sensor fault detection in UAVs, with a focus on real-time implementation and predictive maintenance.

Scientific Awards

• Support of Scholarly Activities (SOSA) Award, TCNJ, 2023
• IEEE Transactions on Instrumentation and Measurement Outstanding Reviewer, 2022 and 2021
• UMKC Outstanding Doctoral Student, 2018
• UMKC travel grants, 2017–2018

Grants and Research Funding

• NSF grant for STEM education, 2022–2025
• NSF integrated advanced manufacturing education grant, $300,000, 2021–2024
• DURIP grant (USDOD), $235,855, 2022