Abimbola Efunogbon | 5G Networks | Best Researcher Award

Dr. Abimbola Efunogbon |5G Networks | Best Researcher Award

PhD Candidate, University of Bedfordshire, United Kingdom

Dr. Bims Efunogbon is an innovative and people-centric leader with over 15 years of expertise in software engineering, cloud computing, and AI-driven infrastructure. Based in Bedford, UK, she has successfully built and managed distributed engineering teams across remote environments, delivering cutting-edge solutions in developer tools, automation, and cloud architectures. With a strong foundation in digital transformation, she combines strategic oversight with deep technical proficiency to streamline operations and drive business growth. Passionate about AI/ML, DevOps, and cybersecurity, Dr. Efunogbon continues to leverage her expertise to modernize platforms and enhance engineering best practices.

Publication Profile

Scopus

🎓 Education

Dr. Efunogbon holds a PhD in Machine Learning (2024) from the University of Bedfordshire, where she conducted advanced research in AI and network optimization. She also earned an MSc in Computing & Entrepreneurship from the same university, blending technical expertise with business acumen. Her academic journey began with a BSc in Chemical & Polymer Engineering from Lagos State University, Nigeria, providing her with a strong analytical and problem-solving foundation. Additionally, she has completed specialized training in SOA & Service-Oriented Computing and holds certifications in Microsoft Visual Basic.NET and Accounting (Bookkeeping, SAGE).

💼 Experience

With a career spanning over a decade, Dr. Efunogbon has demonstrated exceptional leadership in managing cross-functional engineering teams, overseeing cloud infrastructure, and implementing DevOps practices. She excels in process automation, AI/ML applications, and high-performance computing, enabling seamless integration of innovative solutions across multiple industries. Her expertise includes strategic technology implementation, cloud computing, and agile methodologies, ensuring operational excellence and efficiency. She has successfully led remote engineering teams, coached talent, and driven digital transformation initiatives in various sectors, reinforcing her role as a key industry expert.

🏆 Awards and Honors

Dr. Efunogbon has received numerous accolades for her outstanding contributions, including the Outstanding Staff of the Year (2011) and Best Canvasser of the Year at Debenhams (2009). She was also recognized as the Best Female Engineering Student by APWEN (2001), highlighting her academic excellence and leadership in STEM fields.

🔬 Research Focus

Dr. Efunogbon’s research revolves around AI-driven network optimization, cloud computing, and MLOps. Her work primarily explores the orchestration of 5G network sub-slicing using machine learning in fully virtualized environments, ensuring enhanced performance and efficiency. She is passionate about leveraging emerging technologies to create scalable, intelligent, and secure infrastructure solutions that drive digital transformation.

🔍 Conclusion

Dr. Bims Efunogbon is a dynamic leader, researcher, and innovator dedicated to driving digital transformation and AI-powered solutions. With a strong background in software engineering, cloud computing, and machine learning, she continues to shape the future of AI-driven network optimization and engineering leadership. Her contributions to academia and industry reinforce her commitment to technological excellence and operational efficiency. 🚀

📚 Publications

Optimal 5G Network Sub-Slicing Orchestration in a Fully Virtualised Smart Company Using Machine Learning – Future Internet (2025) Read Here
Cited by 1 document

 

qianqian zhang | Computer application technology | Best Dissertation Award

Dr. qianqian zhang | Computer application technology | Best Dissertation Award

Doctoral student, University of Chinese Academy of Sciences, China

Qianqian Zhang is a doctoral student at the University of Chinese Academy of Sciences, specializing in computer application technology within the School of Computer Science and Technology and the National Space Science Center. With notable achievements in electronic information technology for complex space systems, Qianqian has excelled in both academic and research arenas.

Profile

ORCID

🎓 Education:

  • Bachelor’s Degree: Hefei University of Technology, School of Computer Science and Information, Electronic Information Science and Technology, 2017-2021 (Graduated with the highest average score).
  • Master’s Degree: University of Chinese Academy of Sciences, School of Computer Science and Technology/National Space Science Center, Computer Application Technology, 2021-2024 (GPA: 3.7/4.0).
  • Ph.D. Candidate: Continuing at the University of Chinese Academy of Sciences, with significant contributions including 4 patents and several research publications.

🛠 Experience:

Qianqian Zhang has 3 years of experience in the field of computer application technology, with a focus on AI, deep learning, computer vision, and electronic design. She has been mentored by leading experts and has extensive practical experience with PyTorch, PaddlePaddle, TensorFlow, and multimodal data.

🔬 Research Interests:

Her research interests include multimodal data processing, small target detection, model lightweighting, video compression, and efficient deployment of models. She has made significant contributions to the fields of intelligent computing and system-on-chip design.

🏆 Awards:

Qianqian has received numerous national and regional awards, including top prizes in robotics control competitions. Her academic excellence is recognized through various awards and honors in advanced mathematics and electronic design.

📚 Publications:

“Real-time Recognition Algorithm of Small Target for UAV Infrared Detection” (SCI Journal, 2023) [Cited by 5 articles]

“Design of H.264 Video Compression System Based on Domestic CPU+GPU” (Conference Paper, 2023) [Cited by 2 articles]

“Multi-YOLO: Small Target Detection Algorithm Based on Visible and Infrared Multimodal Fusion” (Contributing Paper, 2024) [Cited by 3 articles]