Dr. Alladoumbaye Ngueilbaye | Data Science | Best Researcher Award

Associate Researcher at Shenzhen University, China

Dr. Alladoumbaye Ngueilbaye is an accomplished researcher in the field of Computer Science, currently serving as an Associate Researcher at the National Engineering Laboratory for Big Data System Computing Technology, Shenzhen University, China. His expertise spans Big Data Computing, Machine Learning, Approximate Computing, Data Mining, and Bioinformatics. With over 20 peer-reviewed publications in high-impact journals such as IEEE Transactions on Big Data, Information Sciences, and Applied Soft Computing, Dr. Ngueilbaye has made significant contributions to scalable data processing and AI applications. He also holds editorial responsibilities and is an active member of the International Artificial Intelligence Committee (IAIC). With a strong international academic foundation and a focus on high-performance systems, he is recognized as a global contributor to research in intelligent systems and computational science. His multidisciplinary knowledge, research leadership, and commitment to advancing science in emerging regions make him an exceptional candidate for prestigious academic recognition.

Professional Profile 

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Education

Dr. Ngueilbaye completed his Ph.D. in Computer Science and Technology at the prestigious Harbin Institute of Technology, China (2017–2021), where he also obtained a Master’s degree in 2016. His academic journey reflects a strong international perspective, beginning with a Bachelor’s degree in Computer Science from Ahmadu Bello University, Nigeria (2006–2010). He further enhanced his educational background with multiple professional diplomas in Data Processing, Computer Maintenance, and Business Management. These include certifications from ALISON University (Ireland) and various institutes in Nigeria. His education not only focused on core computer science principles but also emphasized applied mathematics, entrepreneurship, and scientific communication—skills crucial for multidisciplinary innovation. With exposure to global programs such as the One Belt One Road initiative and participation in international summer schools, Dr. Ngueilbaye’s educational background is both diverse and tailored for excellence in advanced research, cross-cultural academic exchange, and applied computing innovation.

Professional Experience

Dr. Ngueilbaye has held multiple roles that reflect both academic excellence and professional versatility. Since June 2022, he has been an Associate Researcher at Shenzhen University, China, contributing to major projects in Big Data and AI. His earlier positions include roles as an IT Manager, Support Supervisor, and Engineer at organizations in Chad and Nigeria, such as Huawei Technologies and Clinique LA PROVIDENCE. Additionally, he has served as a teacher and instructor, emphasizing his commitment to education and knowledge dissemination. These experiences have equipped him with a deep understanding of both research and industry, enabling him to lead and collaborate across sectors. His professional trajectory reflects a rare blend of technical expertise, leadership, and international engagement. The diversity of his roles, ranging from infrastructure-level engineering to high-end computational research, enables him to bridge gaps between academic theories and real-world applications effectively.

Research Interest

Dr. Ngueilbaye’s research interests are centered around Big Data Analytics, Machine Learning, Deep Learning, Data Quality Management, Bioinformatics, and Approximate Computing. He explores scalable solutions for processing massive, distributed datasets and focuses on improving algorithms for data clustering, recommendation systems, and time series classification. His work also addresses challenges in resource-constrained environments, with innovations such as multi-sample approximate computing for distributed systems. Furthermore, he is passionate about applying AI in conservation and public health, as seen in his contributions to elephant monitoring systems and COVID-19 data quality models. His interest in hybrid AI techniques and neural architectures positions him at the forefront of intelligent data analysis. By integrating fundamental computing concepts with practical problem-solving, Dr. Ngueilbaye contributes meaningfully to global advancements in both academic and applied data science.

Award and Honor

Dr. Ngueilbaye has received multiple prestigious scholarships and recognitions throughout his academic journey. He was awarded the Chinese Government Scholarship twice—once for his Master’s and again for his Ph.D.—highlighting his academic excellence and international competitiveness. He received the UNESCO Great Wall Scholarship and was named one of the Outstanding Doctoral Students for the “Perception of China” initiative. His honors include prizes for Outstanding Students and Excellence in Academic Performance, awarded during his graduate studies. These accolades reflect a consistent track record of merit and dedication. Beyond academic honors, he has been invited to participate in elite conferences such as the AAAI Summer Symposium and various doctoral innovation forums. These recognitions validate his contributions to scientific research and his potential as a future leader in technology and innovation.

Research Skill

Dr. Ngueilbaye possesses advanced skills in Big Data system architecture, AI model development, and approximate computing. His hands-on expertise spans Spark-based basket analysis, graph neural networks, hybrid deep learning models, and Bayesian inference techniques. He has developed innovative solutions for challenges like missing data imputation, contextual data quality issues, and long-tailed recognition in machine learning. His technical stack includes tools for distributed computing, Python-based AI frameworks, and tools for data visualization and evaluation. Dr. Ngueilbaye is also experienced in research design, scientific writing, and collaborative software development. His consistent presence in SCI-indexed journals and IEEE publications speaks to his methodological rigor, peer recognition, and commitment to reproducible science. These skills, coupled with his ability to work across disciplines and geographies, make him a valuable contributor to any forward-looking research initiative.

Publications Top Noted

• Ngueilbaye A., Wang H., Mahamat D.A., Junaidu S.B. (2021)
  “Modulo 9 Model-Based Learning for Missing Data Imputation”

  • Journal: Applied Soft Computing 103, 107167

  • Citations: 38

• Mahmud M.S., Huang J.Z., Ruby R., Ngueilbaye A., Wu K. (2023)
  “Approximate Clustering Ensemble Method for Big Data”

  • Journal: IEEE Transactions on Big Data

  • Citations: 29

• Khan M., Wang H., Ngueilbaye A., Elfatyany A. (2023)
  “End-to-End Multivariate Time Series Classification via Hybrid Deep Learning Architectures”

  • Journal: Personal and Ubiquitous Computing 27 (2), 177–191

  • Citations: 27

• Al Sibahee M.A., Abduljabbar Z.A., Ngueilbaye A., Luo C., Li J., Huang Y., et al. (2024)
  “Blockchain-Based Authentication Schemes in Smart Environments: A Systematic Literature Review”

  • Journal: IEEE Internet of Things Journal 11 (21), 34774–34796

  • Citations: 16

• Sun X., Ngueilbaye A., Luo K., Cai Y., Wu D., Huang J.Z. (2024)
  “A Scalable and Flexible Basket Analysis System for Big Transaction Data in Spark”

  • Journal: Information Processing & Management 61 (2), 103577

  • Citations: 12

• Ngueilbaye A., Wang H., Mahamat D.A., Elgendy I.A. (2021)
  “SDLER: Stacked Dedupe Learning for Entity Resolution in Big Data Era”

  • Journal: The Journal of Supercomputing 77 (10), 10959–10983

  • Citations: 12

• Khan M., Wang H., Ngueilbaye A. (2021)
  “Attention-Based Deep Gated Fully Convolutional End-to-End Architectures for Time Series Classification”

  • Journal: Neural Processing Letters 53 (3), 1995–2028

  • Citations: 11

• Ngueilbaye A., Lei L., Wang H. (2016)
  “Comparative Study of Data Mining Techniques on Heart Disease Prediction System: A Case Study for the Republic of Chad”

  • Journal: International Journal of Science and Research 5 (5), 1564–1571

  • Citations: 7

• Elahi E., Anwar S., Al-kfairy M., Rodrigues J.J.P.C., Ngueilbaye A., Halim Z., et al. (2025)
  “Graph Attention-Based Neural Collaborative Filtering for Item-Specific Recommendation System Using Knowledge Graph”

  • Journal: Expert Systems with Applications 266, 126133

  • Citations: 6

• Ngueilbaye A., Huang J.Z., Khan M., Wang H. (2023)
  “Data Quality Model for Assessing Public COVID-19 Big Datasets”

  • Journal: The Journal of Supercomputing 79 (17), 19574–19606

  • Citations: 6

• Ngueilbaye A., Wang H., Khan M., Mahamat D.A. (2021)
  RETRACTED ARTICLE: “Adoption of Human Metabolic Processes as Data Quality Based Models”

  • Journal: The Journal of Supercomputing 77 (2), 1779–1817

  • Citations: 6

Conclusion

Dr. Alladoumbaye Ngueilbaye is a highly deserving candidate for the Best Researcher Award, given his consistent scholarly contributions, multi-country collaborations, and impactful research in areas vital to modern computing and AI. His efforts in bridging academic work between developing and developed nations and promoting cutting-edge research in scalable computing, data science, and AI demonstrate a unique blend of technical depth and global relevance. With continued support and recognition, he is well-positioned to become a global leader in big data systems and AI-driven innovation, contributing not only to academia but also to society through intelligent systems and knowledge dissemination.