Research Scientist at fsdm/usmba, Morocco.
Chafik Boulealam is a Moroccan data scientist and full-stack developer with over five years of experience specializing in artificial intelligence, deep learning, and machine learning. Currently pursuing a PhD in Deep Learning at Sidi Mohamed Ben Abdellah University in Fes, he has led and contributed to projects ranging from real-time face recognition and object detection to intelligent chatbots and Arabic text mining. Fluent in Arabic, French, and English, Chafik combines strong technical skills in Python, Java, and modern frameworks like Django and React with a passion for innovation, problem-solving, and applying AI to real-world challenges.
🎓 Educational Background:
Chafik Boulealam pursued his higher education at Sidi Mohamed Ben Abdellah University in Fes, Morocco, where he built a strong foundation in mathematics, computer science, and artificial intelligence. He first earned a Bachelor of Science in Mathematical Sciences and Computer Science, followed by a Master of Science in Business Intelligence and Intelligent Vision, which deepened his expertise in machine learning and intelligent systems. Currently, he is advancing his academic journey as a PhD student specializing in Deep Learning, with his research focusing on video analysis and hybrid machine learning models. In addition to his formal degrees, Chafik has completed several professional certifications, including courses in Neural Networks, Convolutional Neural Networks, Machine Learning Specialization, and Text Retrieval, demonstrating his commitment to continuous learning and staying current in the fast-evolving field of AI.
Profile:
Professional Experience:
Chafik has over five years of experience in technology and research. He is a Project Manager, Backend Developer, and Full Stack Developer at Dr Stone in Casablanca, where he manages complex AI projects, streamlines API integrations, and develops scalable applications. As a freelancer on Upwork, he has delivered innovative solutions to clients worldwide. His experience also includes research roles at Sidi Mohamed Ben Abdellah University, where he contributed to AI and deep learning projects.
🧠 Research and Innovation:
Driven by curiosity and a passion for AI, Chafik has worked on diverse projects including face recognition systems, behavior analysis, object detection, and Arabic text mining. His work often blends advanced deep learning models with traditional computer vision techniques, such as Kalman filtering and YOLO frameworks. This combination allows him to design systems that are both precise and efficient.
Skills and Expertise:
Chafik is highly skilled in programming languages such as Python, Java, C++, and JavaScript. He is proficient in frameworks like Django, React, Flask, and Next.js, enabling him to build end-to-end solutions. His expertise covers project management, system modeling with UML and MERISE, and developing intelligent systems that solve real-world problems.
Certifications and Professional Development:
Committed to continuous learning, Chafik has earned several respected certifications from Coursera. These include Neural Networks and Deep Learning, Convolutional Neural Networks, Machine Learning Specialization, and Text Retrieval and Search Engines. These credentials reflect his dedication to staying at the forefront of AI and data science innovation.
Publications:
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Filali, H., Riffi, J., Boulealam, C., Mahraz, M. A., & Tairi, H. (2022). Multimodal emotional classification based on meaningful learning. Big Data and Cognitive Computing, 6(3), 95. https://doi.org/xxx (Add DOI if available)
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Filali, H., Boulealam, C., El Fazazy, K., Mahraz, A. M., Tairi, H., & Riffi, J. (2025). Meaningful multimodal emotion recognition based on capsule graph transformer architecture. Information, 16(1), 40. https://doi.org/xxx
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Boulealam, C., Filali, H., Riffi, J., Mahraz, A. M., & Tairi, H. (2024). Feedback-Based Validation Learning (FBVL): A novel mechanism for performance enhancement in neural network architectures. (Manuscript/Journal name if known — provide full citation when available).
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Chafik, B., Hajar, F., Jamal, R., Mahraz, A. M., & Hamid, T. (2025). Deep multi-component neural network architecture. Computation, 13(4), 93. https://doi.org/xxx