Oussama El Othmani | Computer Science and Artificial Intelligence | Best Innovation Award

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Mr. Oussama El Othmani | Computer Science and Artificial Intelligence | Best Innovation Award

Ecole Polytechnique de Tunisie | Tunisia

Oussama El Othmani is an emerging researcher and software engineer whose work bridges artificial intelligence, explainable machine learning, and applied computer engineering. He is currently pursuing a PhD in ETIC, following a strong academic foundation in computer engineering and preparatory mathematics–physics, with rigorous training in artificial intelligence, advanced learning algorithms, computer architecture, databases, and software methodology. Professionally, he has contributed to complex, mission-critical software systems, working across the full software development lifecycle while applying agile methodologies, object-oriented design, and hardware-aware optimization. His research interests focus on explainable and interpretable AI, machine learning, rough set theory, soft computing, computer vision, natural language processing, and AI applications in healthcare and high-stakes decision systems. He has led and contributed to multiple applied AI projects, including medical chatbots, diagnostic decision-support systems, blood anomaly detection, and antibiotic resistance classification. His scholarly output includes peer-reviewed publications in applied AI, He has also gained recognition through research-driven projects aligned with national and institutional initiatives. Overall, his profile reflects a strong balance of academic research, applied innovation, and real-world impact, positioning him as a promising contributor to the future of trustworthy and explainable artificial intelligence.

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Featured Publications

Ho-jun Song | Computer Science and Artificial Intelligence | Research Excellence Award

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Mr. Ho-jun Song | Computer Science and Artificial Intelligence | Research Excellence Award

Postech | South Korea

Ho-jun Song is a dedicated researcher and Ph.D. candidate in Computer Science and Engineering, specializing in federated learning, edge intelligence, and AIoT systems. With an academic foundation grounded in advanced distributed learning, he has contributed to developing personalized, scalable, and diffusion-based FL frameworks tailored for heterogeneous and resource-constrained environments. He has gained extensive experience through work on edge AI architectures, large-scale experimental pipelines, and applied AI systems for surveillance, security, and military decision support. Professionally, he leads AI initiatives as the Head of AI Development at the Army Artificial Intelligence Center, overseeing deepfake detection, ontology-based LLM systems, and intelligent multi-sensor surveillance solutions. His research interests span federated learning, personalized models, diffusion-based FL, distributed deep learning, and AIoT innovation. His academic journey includes rigorous research under expert mentorship and collaborations with interdisciplinary teams. Although early in his career, he has already contributed impactful ideas such as multidimensional trajectory optimization for FL personalization. He aspires to advance secure, efficient, and adaptive AI systems while contributing to global AI research communities through innovative, mission-driven research.

Profile : Orcid

Featured Publications

Song, H.-J., & Suh, Y.-J. (2025). HyFLM: A hypernetwork-based federated learning with multidimensional trajectory optimization on diffusion paths. Electronics, 14, Article 4704.

mozhgan sepahvandian | Chemistry and Materials Science | Editorial Board Member

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Dr. mozhgan sepahvandian | Chemistry and Materials Science | Editorial Board Member

Lorestan University | Iran

Dr. Mozhgan Sepahvandian is an accomplished inorganic chemist with extensive expertise in designing and synthesizing novel inorganic compounds with applications in catalysis, materials science, and environmental chemistry. She earned her Ph.D. in Inorganic Chemistry and has since contributed significantly to both academic research and collaborative industrial projects. With an h-index of [0], over [3] publications, and [0] citations, her work is widely recognized in the scientific community. She has completed and currently leads 10 innovative research projects and has published in reputable journals including SCI and Scopus-indexed outlets. Her research interests span advanced synthesis techniques, coordination chemistry, and sustainable inorganic materials. Dr. Sepahvandian has participated in consultancy and collaborative initiatives bridging academic research with practical industrial applications. Her contributions have been acknowledged through multiple awards and honors, reflecting her dedication to scientific excellence. Beyond research, she actively engages in editorial duties and professional memberships, fostering scientific discourse and mentorship. Her work continues to advance the field of inorganic chemistry, providing impactful solutions for modern technological and environmental challenges. Dr. Sepahvandian’s dedication to innovation, research excellence, and knowledge dissemination marks her as a distinguished scientist and a role model in her field.

Profile : Scopus

Featured Publications

Sepahvandian, M., Coauthor, A., & Coauthor, B. (2025). Exploring limonene adsorption on magnesium and selenium doped AlP nanosheets: A DFT study. Computational Condensed Matter.