Assistant professor at Alliant International University, United States
Shams Al Ajrawi is a Lead Software Engineer and academic researcher with over a decade of experience in web application and backend development. His expertise spans across full-stack development, artificial intelligence (AI), data science, and Brain-Computer Interface (BCI) technologies. With a keen focus on solving intricate challenges, Shams has successfully led numerous industry and academic projects that have resulted in substantial financial savings and technological advancements. He has been actively involved in teaching, curriculum development, and research, playing a pivotal role in mentoring the next generation of engineers and computer scientists. His work bridges the gap between theoretical research and practical implementation, contributing to both corporate innovation and academic progress.
Profile:
SCOPUS
Education:
Shams Al Ajrawi holds a Ph.D. in Electrical and Computer Engineering from a joint program between the University of California, San Diego, and San Diego State University, where his research focused on Brain-Computer Interface (BCI) applications. Prior to his Ph.D., he earned a Master’s degree in Electrical and Computer Engineering from the New York Institute of Technology and a Bachelor of Science in Computer Engineering from the Technological University. His academic journey is marked by a strong foundation in electrical engineering, computer science, and AI, with a specific focus on innovative applications in neuroscience and data processing.
Experience:
Shams has held prominent roles in both industry and academia. As a Lead Software Engineer at John Wiley & Sons, he led initiatives to enhance technology efficiency and reduce costs, including the integration of AI-based solutions like ChatGPT. His role also involved collaborating with corporate clients and managing cross-functional teams using Agile methodologies. In academia, he has served as an Associate Professor and Graduate Program Manager at Alliant International University, where he developed curricula, conducted research, and managed grants. Additionally, Shams is a Researcher Affiliate at UC San Diego’s Qualcomm Institute, focusing on BCI signal interpretation, and he has taught at several institutions, including San Diego State University and National University.
Research Interest:
Shams Al Ajrawi’s primary research interests lie in Brain-Computer Interface (BCI) technology, artificial intelligence, and signal processing. His work in the BCI domain has focused on improving signal extraction and classification, using techniques such as hierarchical recursive feature elimination and flexible wavelet transformation. His research aims to enhance the efficiency and accuracy of interpreting brain signals, particularly for applications related to assisting individuals with spinal cord injuries. Additionally, he explores the integration of AI and machine learning techniques in software development, cybersecurity, and data analytics, striving to develop innovative solutions that merge computational efficiency with real-world applications.
Awards:
Shams has been recognized for his contributions in both industry and academia. He received promotions and excellence awards for two consecutive years at John Wiley & Sons for his leadership and innovative approach in software engineering. In 2023, he was appointed as an Associate Professor at Alliant International University in recognition of his contributions to academia. He has also earned several professional certifications, including the ISACA certification (2023–2028) and Cisco’s CCNA certification, further solidifying his expertise in software engineering and networking.
Publications:
Shams Al Ajrawi has authored numerous papers in prestigious journals, focusing on BCI applications, RFID, and AI. Some of his notable publications include:
“Investigating Feasibility of Multiple UHF Passive RFID Transmitters Using Backscatter Modulation Scheme in BCI Applications” (2017) – Published in IEEE International Symposium on Performance Evaluation of Computer and Telecommunication Systems Cited by 35 articles.
“Bi-Directional Channel Modeling for Implantable UHF-RFID Transceivers in BCI Application” (2018) – Published in Journal of Future Generation Computer Systems, Elsevier Cited by 42 articles.
“Efficient Balance Technique for Brain-Computer Interface Applications Based on I/Q Down Converter and Time Interleaved ADCs” (2019) – Published in Informatics in Medicine Unlocked, Elsevier Cited by 30 articles.
“Hybrid MAC Protocol for Brain-Computer Interface Applications” (2020) – Published in IEEE Systems Journal Cited by 27 articles.
“Cybersecurity in Brain-Computer Interfaces: RFID-Based Design-Theoretical Framework” (2020) – Published in Informatics in Medicine Unlocked, Elsevier Cited by 22 articles.
Conclusion:
Shams Al Ajrawi stands out as a highly accomplished candidate for a “Best Researcher Award.” His rich experience, cutting-edge research, and impactful contributions across both industry and academia position him as a leading figure in his field. However, by narrowing his research focus and expanding interdisciplinary and mentorship efforts, he could enhance his candidacy even further. Overall, he appears highly suitable for the award.