Mr. Akeem Kareem | Thermal Aging | Best Researcher Award
Mr. Akeem Kareem, Kumoh National Institute of Technology, South Korea
Akeem Kareem is a dynamic professional and researcher specializing in mechanical engineering, digital twin technology, and data science. Currently a Research and Development Specialist at FD&P in South Korea, Akeem is also a graduate student at Kumoh National Institute of Technology, pursuing a Doctor of Science in Mechanical Engineering. His academic interests span Digital Twin, Metaverse (VR & AR), autonomous systems, 3D animation, and reinforcement deep learning. Akeem’s diverse experience includes roles in semiconductor engineering, data engineering, and facilities management. He is a certified Machine Learning professional with a focus on electromechanical engineering. Akeem has contributed to several publications in fault diagnostics, motor reliability, and material stress analysis. With a strong foundation in both practical and theoretical aspects of engineering, Akeem is poised to make significant contributions to the field of data science and advanced technologies.
Professional Profile:
Suitability for Best Researcher Award
Akeem Kareem is a strong candidate for the Best Researcher Award due to his diverse expertise and significant contributions in various advanced fields of engineering, data science, and artificial intelligence. Below is a detailed analysis of his suitability for the award
🎓Education
Akeem Kareem holds a Master’s degree in Mechanical Engineering from Kumoh National Institute of Technology (2021-2023) and is currently pursuing a Doctor of Science in Mechanical Engineering at the same institution (2023-2026). His educational journey began with a BEng in Mechanical Engineering (Thermofluid Option) from the University of Ilorin, Nigeria (2013-2017), followed by an OND in Mechanical Engineering from Kwara State Polytechnic (2009-2011). During his Master’s studies, Akeem specialized in semiconductor engineering, data engineering, and applied mechanical engineering research. His ongoing Doctoral research focuses on the integration of Digital Twin technology, Metaverse, and data-driven AI processes to address real-world engineering challenges. Akeem’s strong academic foundation, combined with his practical experience, positions him as an expert in the fields of autonomous systems and digital engineering solutions.
🏢Experience
Akeem Kareem brings extensive professional experience in engineering and research. Currently, he serves as a Research and Development Specialist at FD&P (August 2023 – Present) in South Korea, where he is applying his expertise in Digital Twin, Metaverse, and AI-driven engineering solutions. Prior to this, he was a Graduate Student Research Assistant at Kumoh National Institute of Technology (2021-2023), working on data science and mechanical engineering projects. Akeem also held managerial positions in project management, including Lead Project Manager and Project Manager at James Cubitt Facilities Managers, where he oversaw mechanical, electrical, and civil works for facility management. His background includes working as a Graduate Mechanical Engineer at Dangote Group (2012-2013), and an Automotive Technician at CFAO Cica Nig Limited (2011-2012), where he gained hands-on experience in engineering diagnostics, maintenance, and troubleshooting. This combination of research and practical experience makes Akeem a versatile engineer with diverse skills.
🏅Awards and HonorsÂ
Akeem Kareem has earned recognition for his contributions to engineering and data science, particularly in the areas of fault diagnostics, system reliability, and data-driven engineering. His research has been featured in several esteemed publications, including his work on SMPS fault diagnostics, BLDC motor analysis, and material suitability for electric vehicles. Although specific awards and honors are not listed in his profile, Akeem’s continuous involvement in research and development, along with his involvement in cutting-edge technology areas like Digital Twin and the Metaverse, showcases his drive for excellence. He is an active member of various professional networks, contributing to engineering symposiums and conferences globally. Akeem’s publications and work have gained attention for their practical applications in mechanical engineering, data science, and autonomous systems, positioning him for future recognition in both academic and industrial circles.
🔬Research Focus
Akeem Kareem’s research interests revolve around the intersection of mechanical engineering, data science, and emerging technologies. His current doctoral research at Kumoh National Institute of Technology focuses on Digital Twin technology, Metaverse (VR & AR), autonomous systems, and reinforcement deep learning. Akeem has explored diverse topics such as fault diagnostics in power systems, reliability enhancement of electrochemical capacitors, and material stress analysis for electric vehicles. His research also emphasizes the integration of data science in mechanical engineering, using AI algorithms and machine learning for real-time monitoring and optimization of engineering systems. He is particularly interested in data-driven processes that enable predictive maintenance and enhance the efficiency and safety of critical systems. Akeem’s work aims to bridge the gap between traditional mechanical engineering and the innovative applications of digital technologies, positioning him at the forefront of research in modern engineering and technology.
Publication Top Notes:
1.A Comparative Data Augmentation-Assisted Diagnostic Framework for Industrial Centrifugal Pumps
2. A Comparative Study of Deep-Learning Autoencoders (DLAEs) for Vibration Anomaly Detection in Manufacturing Equipment
3. Enhancing Transformer Core Fault Diagnosis and Classification through Hilbert Transform Analysis of Electric Current Signals
4. Transformer Core Fault Diagnosis via Current Signal Analysis with Pearson Correlation Feature Selection
5. Hyperelastic and Stacked Ensemble-Driven Predictive Modeling of PEMFC Gaskets Under Thermal and Chemical Aging