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 | Research Excellence Award

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Dr. Mozhgan Sepahvandian | Chemistry and Materials Science | Research Excellence Award

Lorestan University | Iran

Dr. Mozhgan Sepahvandian is an accomplished inorganic chemist whose academic journey reflects a strong commitment to scientific excellence, innovation, and interdisciplinary research. With a PhD in Inorganic Chemistry, she has built expertise in advanced material synthesis, coordination compounds, and catalytic systems while contributing to both theoretical and experimental chemistry. Has published 3 peer-reviewed journal papers indexed in major scientific databases, accumulating citations- 0, documents- 3, and an h-index-0 that reflect her growing impact in the field. With 10 completed or ongoing research projects, her work spans functional materials, green chemistry, and nanostructured inorganic systems. Her research interests include inorganic synthesis, materials chemistry, photocatalysis, and the development of sustainable chemical processes. In addition to academic research, she actively engages in institutional collaborations and contributes to the scientific community through knowledge sharing and collaborative initiatives. She has participated in various academic activities, technical discussions, and research-driven initiatives that strengthen the bridge between chemistry and applied sciences. Dr. Sepahvandian’s dedication to innovation, high-quality research, and continuous professional development underscores her role as an emerging researcher in inorganic chemistry. Looking ahead, she aims to expand her contributions to advanced materials and environmentally friendly chemical applications, supporting scientific progress and societal benefit.

Profiles : Scopus | Orcid

Featured Publications

Sepahvandian, M., & Zabardasti, A. (2025). Computational study of dacarbazine–amino acid interactions. Journal of Biomolecular Structure and Dynamics.

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

Sepahvandian, M., Abd Al-Aama, Z. M., Al-Masoudi, H. Q., & Zabardasti, A. (2025). Theoretical investigation of optical properties of adducts of Aun and Cun (n = 1–3) with free base porphyrins. Bulletin of the Chemical Society of Ethiopia.