Omar Soufi | Artificial Intelligence | Best Researcher Award

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Dr. Omar Soufi | Artificial Intelligence | Best Researcher Award

PHD at Ehime University in Japan

Dr. Omar Soufi, né le 13 mai 1993 à Casablanca, est un expert en data science, télédétection spatiale et systèmes d’information géographique. Titulaire d’un doctorat en génie informatique de l’EMI Rabat, ses recherches se concentrent sur l’intelligence artificielle et le deep learning pour améliorer la qualité des images satellites. Avec une expérience notable dans des projets de Business Intelligence et d’applications SIG, il est polyglotte, parlant couramment arabe, français, anglais et espagnol. Dr. Soufi détient plusieurs certifications professionnelles en IA, machine learning et data analytics, renforçant son rôle clé dans l’innovation technologique et la gestion des données.

Professional Profiles

Academic Background

2023 : Doctorat – EMI Rabat Cycle doctoral à l’équipe AMIPS/E3S Option: Génie Informatique : Intelligence Artificielle Sujet: Approche par Deep Learning au profit de la télédétection spatiale : Amélioration de la qualité d’images satellites et du procédé du capteur d’étoile. 2020 : Ingénierie – Polytechnique Grenoble, ENSIMAG 3ème année cycle ingénieur Option: Ingénierie des Systèmes d’Information 2020 : Ingénierie – EMI Rabat Cycle ingénieur en Informatique Option: Ingénierie et Qualité Logicielle 2015 : Diplôme – ARM Merkens Diplôme des Études Universitaires 2014 : Licence Fondamentale – ARM Merkens Option: Sciences et Techniques Filière: Génie Mécanique 2011 : Baccalauréat – 1er LMR Option: Sciences de Vie et de Terre

Expériences Professionnelles

Chargé des missions d’Intelligence Artificielle (2024) Participation à plusieurs projets en intelligence artificielle et data science pour l’analyse et la gestion des données. Chef Pôle Géomatique & Outils décisionnels (2022) Développement d’applications SIG thématiques pour des domaines tels que l’urbanisme, l’environnement, et l’agriculture. Chef département BI & Outils décisionnels (2020) Conception et implémentation de projets de Business Intelligence et de plateformes géospatiales pour la gestion des risques des catastrophes naturelles et d’autres applications stratégiques.

Areas of Research

Data Science Télédétection Spatiale Systèmes d’Information Géographique (SIG)

Research Focus

Dr. Omar Soufi’s research focuses on the application of deep learning techniques to image processing, specifically in the field of Single Image Super-Resolution (SISR). His work involves developing and benchmarking advanced deep learning models to enhance the resolution and quality of satellite imagery. Key publications include studies on novel super-resolution approaches for multispectral satellite images and intelligent methods for spacecraft attitude control. His research contributes to improving the accessibility and analysis of high-resolution satellite imagery, which has significant implications for remote sensing, environmental monitoring, and geospatial applications. Dr. Soufi’s expertise lies at the intersection of artificial intelligence and remote sensing technologies.

Publications

  1. Benchmark of deep learning models for single image super-resolution (SISR), Publication date: 2022.
  2. Study of Deep Learning-based models for Single Image Super-Resolution. Publication date: 2022.
  3. Deep learning technique for image satellite processing, Publication date: 2023.
  4. Enhancing Accessibility to High-Resolution Satellite Imagery: A Novel Deep Learning-Based Super-Resolution Approach, Publication date: 2023.
  5. Analysis and Processing of Spatial Remote Sensing Multispectral Imagery using Deep Learning Techniques, Publication date: 2023.
  6. An intelligent deep learning approach to spacecraft attitude control: the case of satellites, Publication date: 2024.
  7. A Novel Deep Learning-Based Super-Resolution Approach, Publication date: 2023.
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Monika Nagy-Huber | Machine learning | Best Researcher Award

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Ms. Monika Nagy-Huber | Machine learning | Best Researcher Award

PHD at the University of Basel, Switzerland

Monika Timea Nagy-Huber, a Swiss national, is a PhD candidate in Computer Science at the University of Basel, specializing in Physics-informed Machine Learning Algorithms under the guidance of Prof. Dr. Volker Roth. She is part of the Biomedical Data Analysis research group. Monika holds a Master of Science in Mathematics from the University of Basel, where she focused on Numerics and Algebra-Geometry-Number Theory. Her master’s thesis explored the Local Discontinuous Galerkin Method for solving the Wave Equation. She also earned her Bachelor of Science in Mathematics from the University of Basel. Monika is passionate about integrating advanced mathematical techniques with cutting-edge computer science applications.

Professional Profiles

Education

09/2019 – Present: PhD in Computer Science, University of Basel ‣ Supervisor: Prof. Dr. Volker Roth ‣ Research group: Biomedical Data Analysis ‣ Specialisation: Physics-informed Machine Learning Algorithms 02/2016 – 02/2019: Master of Science in Mathematics, University of Basel ‣ Areas of specialisation: Numerics (Partial Differential Equations for Wave Equations), Algebra-Geometry-Number Theory (Elliptic Curves) ‣ Master’s thesis: “Das lokale diskontinuierliche Galerkin-Verfahren mit lokalem Zeitschrittverfahren zur Lösung der Wellengleichung” (translated: The Local Discontinuous Galerkin Method with Local Time Stepping Method for solving the Wave Equation), Grade 5.5 ‣ Supervisor: Prof. Dr. Marcus J. Grote 09/2011 – 02/2016: Bachelor of Science in Mathematics, University of Basel

Research Focus

Monika Timea Nagy-Huber’s research primarily focuses on the intersection of advanced computational methods and biomedical applications. Her work involves developing and applying physics-informed machine learning algorithms to solve complex problems, such as partial differential equations, relevant to biomedical data analysis. She has contributed to various projects, including studying the effects of LSD on brain connectivity, learning invariances with input-convex neural networks, and creating mesh-free Eulerian physics-informed neural networks. Her interdisciplinary approach leverages deep learning and computational science to address challenges in neuroscience, exercise science, and environmental monitoring, demonstrating a robust expertise in integrating theoretical mathematics with practical applications.

Publications

  1. Physics-informed boundary integral networks (PIBI-Nets): A data-driven approach for solving partial differential equations, Publication date: 2024.
  2. Using Machine Learning–Based Algorithms to Identify and Quantify Exercise Limitations in Clinical Practice: Are We There Yet?, Publication date: 2023.
  3. The effect of lysergic acid diethylamide (LSD) on whole-brain functional and effective connectivity, Publication date: 2023.
  4. Learning invariances with generalised input-convex neural networks, Publication date: 2022.
  5. Mesh-free eulerian physics-informed neural networks, Publication date: 2022.
  6. Mesh-free Eulerian Physics-Informed Neural Networks, Publication date: 2022.
  7. Visual Understanding in Semantic Segmentation of Soil Erosion Sites in Swiss Alpine Grasslands, Publication date: 2022.
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