Soufiane Bacha | Artificial Intelligence | Best Researcher Award

Mr. Soufiane Bacha | Artificial Intelligence | Best Researcher Award

PhD Student, University of Science and Technology Beijing, Algeria

Mr. Soufiane Bacha is a promising young researcher in Artificial Intelligence and Data Quality with a strong academic background and growing international exposure. He is currently pursuing a Ph.D. in Data Quality at the University of Science and Technology Beijing (2023–ongoing) and a Ph.D. in Cancer Epidemiology at the Department of Computer Science, Ibn Khaldoun University of Tiaret, Algeria (2021–2025). He also holds a Master’s degree in Software Engineering (2019–2021), where he ranked first in his class and completed a thesis on imbalanced datasets and boosting methods, and a B.Sc. in Computer Science (2016–2019) with strong foundations in algorithms, cryptography and programming. Professionally, Mr. Soufiane Bacha gained valuable international research experience through an internship at the Faculty of Polytechnic Mons, UMONS University in Belgium, where he worked on Internet of Things (IoT) applications involving Raspberry Pi, Arduino and sensor technologies. He has served as a part-time lecturer in Graph Theory and as an ICT trainer in web development, demonstrating strong teaching, leadership, and communication skills. His research interests span artificial intelligence, data quality, machine learning for imbalanced datasets, cancer epidemiology, distributed applications and business analytics. He is proficient in Python, C/C++, Java, SQL and data analysis tools, with expertise in OLAP, data mining, and deep learning frameworks. His achievements include an NVIDIA Deep Learning Institute Certificate, participation in AI workshops, and a Scopus-indexed publication. With a dual doctoral training and interdisciplinary focus, Mr. Soufiane Bacha is well-positioned to make impactful contributions to AI-driven data quality research and healthcare analytics on a global scale.

Profile: ORCID | Google Scholar | ResearchGate

Featured Publications

1. Bacha, S., Ning, H., Mostefa, B., Sarwatt, D. S., & Dhelim, S. (2025). A novel double pruning method for imbalanced data using information entropy and roulette wheel selection for breast cancer diagnosis (arXiv:2503.12239).