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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Linlin You | Computing | Best Researcher Award

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Assoc Prof Dr. Linlin You | Computing | Best Researcher Award

PHD at the University of Pavia,  Italy

Linlin You is an Associate Professor at Sun Yat-sen University, specializing in Smart Cities, Autonomous Systems, Distributed Computing, and Federated Learning. She obtained her Ph.D. from the University of Pavia and has conducted research at MIT and Singapore-MIT Alliance for Research and Technology. Linlin has authored over 70 publications and holds 21 patents. She serves as an editorial board member for prestigious journals like The Innovation and is a Senior Member of IEEE. Her contributions in advancing distributed computing and federated learning have earned her recognition, including the Smart City Technology Innovation Award, highlighting her impact in sustainable urban development and intelligent systems. computing

Professional Profiles

Academic and Professional Background

Linlin You is an Associate Professor at Sun Yat-sen University and a Research Affiliate at MIT. Formerly a senior postdoc at Singapore-MIT Alliance for Research and Technology, she earned her Ph.D. in Computer Science from the University of Pavia in 2015. Her research spans Smart Cities, Autonomous Systems, Distributed Computing, Federated Learning, and AI-driven solutions for transportation and energy. She has authored over 70 publications in top-tier journals/conferences and holds 21 patents. Linlin serves on editorial boards, including The Innovation (IF 33.2), and is an IEEE Senior Member. computing

Research and Innovations

CEO of Naftan Payesh Integrity and Corrosion Management Co., driving innovation in corrosion management and root cause failure analysis Senior Corrosion Consultant at LifeTech Engineering, advising on global energy projects Technical Manager and Chairman at Miad Tech. Co., specializing in materials characterization and corrosion solutions computing

Areas of Research

Her research focuses on systems and services, computing, and learning.

Research Focuses

Linlin You’s research focuses on Smart Cities, Autonomous Systems, Distributed Computing, and Federated Learning. Her work explores the impact of urban morphology on solar capacity in three-dimensional cities, accurate modeling of photovoltaic modules using deep learning networks, and personal mobility service systems in urban areas. She has developed innovative federated learning mechanisms and contributed to understanding solar accessibility and sustainable urban development. Linlin’s research also addresses mobility sensing systems, public sentiment analysis for smart city design, and cloud computing service quality management. Her contributions span over 70 publications, numerous patents, and editorial roles in leading journals, demonstrating leadership in advancing technologies for intelligent urban environments.

Publications

  1. From cell tower location to user location: Understanding the spatial uncertainty of mobile phone network data in human mobility research, Publication date: 2024.
  2. Precise Landmark-Map for Bundle Adjustment LiDAR Odometry, Publication date: 2024.
  3. Resource-Aware Split Federated Learning for Edge Intelligence,, Publication date: 2024.
  4. SiG: A Siamese-Based Graph Convolutional Network to Align Knowledge in Autonomous Transportation Systems.Publication date: 2024.
  5. Unravelling the effects of dynamic urban thermal environment on utility-scale floating photovoltaic electricity generation, Publication date: 2023.
  6. FedRSM: Representational-Similarity-Based Secured Model Uploading for Federated Learning, Publication date: 2023.
  7. Efficiency-Improved Federated Learning Approaches for Time of Arrival Estimation, Publication date: 2023.
  8. Enterprise oriented software engineering education: a preliminary framework, Publication date: 2023.
  9. Multi-source data management mechanism and platform,  Publication date: 2022.
  10. Integrated Mobility for Individuals in Smarter Cities: a Crowd-sourcing approach, Publication date: 2022.
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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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