Mr. Oussama El Othmani, Computer Engineering, Tunisia Polytechnic School, Tunisia
Oussama El Othmani is a dedicated computer engineer specializing in Artificial Intelligence (AI), Machine Learning (ML), and software development. Currently pursuing a Ph.D. in Engineering and Information and Communication Technologies (ETIC) at Tunisia Polytechnic School, University of Carthage, he is committed to advancing knowledge in these fields. His professional experience includes roles as a Software Engineer with the Tunisian Ministry of National Defense and internships at the Main Military Instruction Hospital of Tunis and the National Center for Cartography and Remote Sensing. Oussama’s research interests encompass AI/ML model interpretability, medical applications of AI, and data analysis. He has contributed to several publications, including a notable paper on integrating Rough Set Theory and Soft Computing for explainable AI/ML models.
Profile
Orcid
Education
Oussama El Othmani’s academic journey is marked by a strong foundation in computer engineering and AI. He is currently pursuing a Ph.D. in ETIC at Tunisia Polytechnic School, University of Carthage, La Marsa, Tunis, starting in 2024. Prior to this, he completed his undergraduate studies in Computer Engineering at the Tunisian Military Academy, Fondik Jdid, from 2020 to 2023, with a focus on AI and ML. His educational background also includes preparatory studies in Mathematics and Physics from 2018 to 2020 at the same institution. Additionally, Oussama has undertaken relevant coursework in advanced learning algorithms, artificial intelligence, computer architecture, database management, and project management fundamentals.
Professional Experience
Oussama El Othmani has accumulated valuable professional experience in both military and civilian sectors. Since August 2023, he has been serving as a Software Engineer with the Tunisian Ministry of National Defense, where he participates in the full software development lifecycle, including specifications, modeling and design, application development, unit testing, integration, formal system testing, deployment, and maintenance. He collaborates with system engineers, hardware designers, and integration/test engineers to develop and maintain complex software systems, applying Agile development methodologies and object-oriented architectures. In the summer of 2022, Oussama interned at the Main Military Instruction Hospital of Tunis, focusing on image processing, DevOps, machine learning, Kubernetes, and systems administration, contributing to enhancing hospital information systems and technological infrastructure. Earlier, in 2021, he interned at the National Center for Cartography and Remote Sensing, where he analyzed and interpreted geographic data using remote sensing techniques and developed cartographic products to visualize spatial information for various projects.
Research Interests
Oussama El Othmani’s research interests lie at the intersection of artificial intelligence, machine learning, and their applications in real-world scenarios. He is particularly focused on developing explainable and interpretable AI/ML models, aiming to enhance the transparency and trustworthiness of these systems. His work explores the integration of Rough Set Theory and Soft Computing methods to build models that are not only accurate but also understandable by human users. Additionally, Oussama is interested in applying AI techniques to medical fields, such as developing systems for blood anomaly detection and antibiotic resistance classification, thereby contributing to advancements in healthcare technology.
Awards
While specific awards are not listed in the provided information, Oussama El Othmani’s academic and professional achievements reflect a commitment to excellence in his field. His innovative work in AI/ML model interpretability and medical applications demonstrates his potential for recognition in the realm of computer engineering and artificial intelligence.
Publication Top Notes
Rough Set Theory and Soft Computing Methods for Building Explainable and Interpretable AI/ML Models