RobIA25 International Autumn School

About RobIA'25

Robotics and Artificial Intelligence are revolutionizing key sectors such as industry, healthcare, agriculture, and transportation. These technologies not only automate complex tasks but also enable intelligent data processing and problem-solving. Fully leveraging their potential requires an interdisciplinary approach that bridges computer science, engineering, and the humanities.

RobIA’2025, organized by the LAIG Laboratory(Laboratoire d'Automatique et Informatique Guelma), aims to serve as a premier platform for fostering such collaboration and innovation. The school will explore a wide range of cutting- edge topics, from fundamental algorithms in machine learning and advances in AI and deep learning, to emerging capabilities in imaging and 3D vision. Participants will also engage with developments in autonomous robotics, including systems focused on intelligent perception, as well as collaborative robotics that facilitate human-machine interaction. In addition, the program will highlight novel approaches in micro-robotics and bio-inspired design, illustrating the convergence of biological principles with robotic engineering.

The event will feature a rich combination of activities: hands-on workshops addressing real- world problems, plenary sessions that tackle both theoretical frameworks and applied research, and a dedicated doctoral symposium—organized by the GAST team (France)—designed to empower early-career researchers. This immersive environment will offer participants a unique opportunity to share ideas, receive feedback from leading experts, and strengthen their academic and professional networks on an international scale.

Our Speakers

Abdenour Hadid received his Doctor of Science in Technology degree in electrical and information engineering from the University of Oulu, Finland, in 2005. Now, he is a Professor and PI of a CHAIR on Artificial Intelligence at Sorbonne University of Abu Dhabi. His research interests include physics-informed machine learning, forecasting, computer vision, deep learning, artificial intelligence, internet of things and personalized healthcare. He has authored more than 400 papers in international conferences and journals, and served as a reviewer for many international conferences and journals.

His research works have been well referenced by the research community with more than 29,000+ citations and an H-Index of 60. Prof. Hadid is currently a senior member of IEEE. He was the recipient of the prestigious “Jan Koenderink Prize” for fundamental contributions in computer vision. He participated and played a key role in different European projects. One of these projects has been selected as a Success Story by the European Commission.

His achievements have also been recognized by many awards including the highly competitive Academy Research Fellow position from the Academy of Finland during 2013–2018, and a very prestigious international award within the 100-Talent Program (Outstanding Visiting Professor) of Shaanxi Province, China.

We are living in the era of big data. Data is everywhere and is continuously recorded and stored in our ordinary life. The key element for using such an immense body of data to benefit the society is through Artificial Intelligence (AI). Artificial intelligence has dramatically transformed our society by showing impressive results across a large spectrum of applications ranging from biology, medicine, education, geoscience, legislation, and finance. Despite the impressive performances, AI models are closely tied to the quantity/quality of the training data (garbage in and garbage out). Also, from the trustworthiness and fairness viewpoints, some serious concerns have been raised about the potential emergence of ‘super- intelligent machines’ without adequate safeguards. Moreover, AI models usually require a large amount of high-quality, unbiased data to operate. Other equally important issues include the massive computing power that is needed to train AI models. This presentation discusses some examples and challenges related to the use of AI in healthcare, security, autonomous driving, geoscience etc.

Nida Meddouri est enseignant-chercheur à l’École Pour l’Informatique et les Techniques Avancées (EPITA), au sein du Laboratoire de Recherche en Informatique (LRE), où il coordonne le parcours recherche du cycle d’ingénieurs en apprentissage ainsi que les projets de recherche en cybersécurité. Il est également chercheur associé au laboratoire GREYC (CNRS UMR 6072) de l’Université de Caen Normandie. Titulaire d’un doctorat en informatique obtenu en 2015 à l’Université Tunis El Manar, ses travaux de recherche portent sur l’apprentissage automatique, l’analyse de concepts formels, la classification supervisée, l’intelligence artificielle explicable, ainsi que l’analyse de données temporelles et spatiales. Il est l’auteur de nombreuses publications scientifiques dans des revues et conférences internationales à comité de lecture, et a co-encadré plusieurs thèses de doctorat en cotutelle et en codirection. Son parcours académique est marqué par une solide expérience pédagogique, acquise dans dix établissements d’enseignement supérieur répartis entre la France, la Tunisie et l’Arabie Saoudite. Engagé activement dans la communauté scientifique internationale, Nida Meddouri est co-responsable du groupe de travail GAST (Gestion et Analyse de données Spatiales et Temporelles), dans le cadre duquel il a contribué à l’organisation de plusieurs ateliers et journées thématiques. Il siège également dans les comités de programme de conférences majeures telles que ECML PKDD, ICDM ou encore ECAI. Il participe à des projets de recherche collaboratifs et œuvre à la diffusion des connaissances à travers l’organisation de colloques, la production de contenus pédagogiques numériques, ainsi que le développement de modules logiciels pour des plateformes du Data Maining. Membre de plusieurs associations scientifiques francophones et internationales, il intervient régulièrement dans des séminaires de recherche en France et à l’étranger. Trilingue (français, anglais, arabe), il incarne une approche rigoureuse, interdisciplinaire et engagée de la recherche et de l’enseignement en informatique.

Améliorer les performances de l'apprentissage supervisé par des techniques d'ensemble (Boosting/Bagging)
De nombreux travaux en apprentissage supervisé se sont consacrés à l’étude des méthodes d’ensemble, dont l’objectif est d’accroître la performance des classifieurs en combinant leurs prédictions. Ces approches reposent essentiellement sur deux paradigmes : l’apprentissage séquentiel, illustré notamment par le Boosting, et l’apprentissage parallèle, tel que le Bagging. Les analyses comparatives disponibles dans la littérature mettent en évidence l’efficacité de ces stratégies, en particulier dans le traitement de problèmes réels présentant une grande complexité, permettant ainsi une amélioration substantielle des performances des ensembles de classifieurs. Ces techniques se sont développées à l’interface entre l’apprentissage automatique et la statistique, tirant parti des avancées conceptuelles et algorithmiques issues de ces deux domaines. Deux grandes catégories d’algorithmes se distinguent : d’une part, ceux fondés sur le Boosting, qui reposent sur une construction adaptative (qu’elle soit déterministe ou aléatoire) d’un ensemble de classifieurs ; d’autre part, ceux reposant sur le Bagging, caractérisés par une génération aléatoire d’un ensemble de modèles de base. Dans le cadre de cette étude, nous nous proposons de mettre en exergue les apports respectifs de ces deux paradigmes, en examinant de quelle manière ils permettent d’améliorer les performances d’un classifieur initialement considéré comme faible, en l’occurrence un classifieur fondé sur l’Analyse de Concepts Formels.

Professor at the University of Guelma and served as the Director of the Laboratory of Automation and Computer Science of Guelma (LAIG) until 2016. Graduating as an Electronics Engineer from the University of Annaba in 1984, he pursued postgraduate studies (DEA and Ph.D.) in automation and signal processing at the National Polytechnic Institute of Grenoble (INP Grenoble), France. His research, conducted at the Grenoble Automation Laboratory, focused on satellite attitude control during his DEA and on the structure and control of nonlinear systems for his doctoral studies. Throughout his career, he has led numerous national and international research projects in areas such as:
• Modeling and control of fractional-order systems
• Modeling and control of hybrid dynamic systems
• Automatic pattern recognition, particularly facial recognition
He has also contributed as a member of several national scientific and technological committees, including the Central Commission for New Information and Communication Technologies of the Ministry of Higher Education and Scientific Research (MHESR) since 2002, and the MHESR Standing Sector Committee (SSC) from 2009 to 2012.

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Title1:Everyone Knows the mean, But...
For over 5,000 years, the concept of the mean has been applied across a wide range of fields, from mathematics to agriculture. While it plays an essential role in guiding our decisions, the mean often oversimplifies complex realities, sometimes making it the subject of humor. This presentation aims to deepen our understanding of the mean and explore ways it can be used to offer a more nuanced perspective on our data.
Title2: Vision 3D
Dans cette présentation, je parlerai de l’intérêt de la vision 3D pour reconstruire la forme et la profondeur à partir d’images 2D. J’expliquerai les bases de la géométrie projective et épipolaire, essentielles pour relier les points entre deux vues. Je présenterai ensuite les principales approches de vision 3D, dont la stéréovision, qui sera illustrée comme exemple concret.

Pr. TALEB-AHMED Abdelmalik received in 1992 PhD in electronics and microelectronics from Université des Sciences et Technologies de Lille 1. He was associate professor in Calais until 2004. He joined the Université Polytechnique des Hauts de France in 2004, where he is presently Full Professor. His research focused on computer vision and artificial intelligence and machine vision.

His research interests include segmentation, classification, data fusion, pattern recognition, computer vision, and machine learning, with applications in biometrics, video surveillance, autonomous driving, and medical imaging. He has (co-)authored over 225 peer-reviewed papers and (co-)supervised 20 graduate students in these areas of research.

It is becoming quite easy to generate realistic images and videos especially using diffusion-like models (e.g. DALL-E, GLIDE, Midjourney, Imagen, VideoPoet, Sora, Genie, etc.) due to their impressive generative capabilities. This creates a huge potential for a wide range of applications such as image editing, video production, content creation and digital marketing. Moreover, synthetically generated images and videos can be very useful for enhancing the training of AI models which usually require a large amount of data. However, these advances have also raised concerns about the potential misuse of these images and videos, including the creation of misleading content such as fake news and propaganda. So, one of the critical challenges associated with these advancements is the development of effective detection methods of synthetic images and videos. In the talk, we present the advances in automatically generating and editing images and videos, and discuss the limitations and challenges of such AI generated content.

James Whidborne is Professor in Control Systems and the Head of the Dynamics, Simulation and Control (DSC) research group in the Centre for Aeronautics at Cranfield University, United Kingdom. He received his bachelors from Cambridge University and his masters and doctorate from the University of Manchester. Following a post-doctoral position at Leicester University he spent 10 years at Kings College London, moving to Cranfield in 2004.

He has published over 250 fully refereed papers, and has authored or edited three research monographs mostly in the area of advanced control. His research interests include flight control, control of UAVs, flow control, robust multi-objective control design as well as control problems in automated oil drilling.

TITLE: Control of Multirotor Aerial Robots in Gusty Conditions
Due to their mechanical simplicity, multirotor air vehicles are being increasingly used for numerous applications in aerial robotics and aviation. However, the aircraft require feedback for stable flight, hence there are many interesting control problems that require solutions. This presentation will consider several of these, in particular the problem of operating in gusty conditions. The flight dynamics and control is explored by analysis of a planar birotor aircraft, in particular, the effect of rotor tilt on the stability and zero-location.

Researcher in Machine Learning, I am interested in ML applications in Agriculture, Chemistry and Engineering. I am a lecturer at the School of Artificial Intelligence in Algiers. I was certified for several Nvidia workshops, which allowed me to train more than 300 participants and certified more than 150 of them. I am very interested recently in how to implement machine learning and deep learning projects in production.

I'm interested in machine learning theory and its application in real life (Agriculture, Chemistry, Engineering...). Recently, I have worked in deep learning and especially convolutional neural networks (CNN) for computer vision. For example, I have applied CNN for plant disease classification and human action recognition. But, I don't limit myself to these applications, and I can apply CNN in any domain. I'm working on the interpretation of CNN results using visualization methods to make it more useful in many critical applications.

This training provides a practical and accessible introduction to deep learning through real- world applications in computer vision and natural language processing. Participants will gain hands-on experience in training neural networks, improving model accuracy with data augmentation, and using transfer learning to reduce development time and data needs. Key skills developed include:
- Training deep learning models using modern frameworks like TensorFlow and Keras
- Enhancing datasets for better generalization
- Applying pre-trained models and recurrent neural networks
- Building and optimizing models for tasks such as image classification and text completion
Participants are expected to have basic knowledge of Python (functions, loops, arrays, etc.). All required tools and cloud-based GPU resources are provided. The training includes coding assessments, and a certificate is awarded upon successful completion. The course is ideal for individuals looking to start or strengthen their journey in deep learning with guidance from NVIDIA’s industry-driven content.

Sami Belkacem is a PhD in Computer Science from the University of Sciences and Technology Houari Boumediene (USTHB) and currently a member of the Computer Systems Laboratory. He received a Bachelor's degree in Computer Science in 2013, a Master’s degree in Artificial Intelligence in 2015, and a PhD in Artificial Intelligence in 2021 from USTHB University. Sami has completed two doctoral internships: University of Algarve (Portugal) and University of Lyon 2 (France).

He has published several papers in international conferences, workshops, and journals. Research Interests: Social Network Analysis and Mining, Recommender systems, Machine Learning

This training offers a comprehensive introduction to essential machine learning techniques and tools. Participants will learn how to prepare and analyze data, identify meaningful patterns, and apply feature engineering to enhance model performance.
The program also covers key machine learning methods, including classification, regression, and clustering. Attendees will gain practical experience in selecting models, training them, tuning hyperparameters, and evaluating results using performance metrics such as accuracy, precision, and recall.
The training is designed for individuals with a basic background in Python programming, mathematics, and data structures. It makes use of widely adopted tools and platforms such as Python 3, Anaconda, Jupyter Notebook, and Google Colab. Core libraries include NumPy, Pandas, Matplotlib, Seaborn, and Scikit-learn.

Dr. Mohammed En Nadhir Zighem is a senior researcher at Huawei's Finland R&D Centre, where he focuses on visual content generation, including image and video synthesis, using deep learning and generative models. He earned his Ph.D. through a joint program between Université Mohamed Khider Biskra in Algeria and Université Polytechnique Hauts-de-France (UPHF) in France. His research interests include facial analysis, biometric security, age and gender estimation, and anti-spoofing techniques.

Dr. Zighem’s expertise spans neural networks, image segmentation, and feature extraction, and he actively collaborates with international research teams. He published several research papers and patents in the field of machine learning.

This talk explores diffusion models, a class of generative models that have rapidly advanced the field of AI (Artificial Intelligence) through their ability to synthesize high- quality data, particularly in image and video generation. We will begin by examining the core architectures behind these models, including denoising diffusion probabilistic models (DDPMs) and their variants, highlighting how they leverage iterative noise removal to generate data from pure noise. The session will then survey key applications across domains especially in text-to-image synthesis, demonstrating their transformative potential. Finally, we will address current challenges, including computational cost, sampling speed, and the difficulty of controlling outputs, while discussing ongoing research directions aimed at overcoming these limitations.

Abdelmalek BELLOULA est diplômé de l’École Militaire Polytechnique, titulaire d’un diplôme d’ingénieur d’État en automatique et d’un magistère en robotique, automatique et informatique industrielle. Fort de plus de 30 années d’expérience, son parcours s’est articulé autour de la recherche scientifique, de l’enseignement universitaire et du développement technique, avec une expertise avérée dans la conception et l’intégration de systèmes complexes. Ses domaines de spécialisation incluent l’électronique, les systèmes embarqués temps réel, l’IoT, l’automatique et la robotique. Il a occupé des postes stratégiques au sein du Ministère de la Défense et a travaillé pour des entreprises de référence telles que Cevital et HB-Technologies. Très orienté vers l’innovation, il se consacre aujourd’hui à l’ingénierie des systèmes embarqués intelligents, avec un intérêt particulier pour les architectures distribuées, la robotique mobile et les applications temps réel.

Title:Microcontrôleurs et IA distribuée : vers l’autonomie des robots mobiles
L’évolution spectaculaire des microcontrôleurs, alliant une puissance de calcul sans cesse croissante à une réduction drastique des coûts, stimule l’adoption d’architectures distribuées dans les applications embarquées, et tout particulièrement en robotique mobile. En intégrant désormais des périphériques matériels spécialisés (DMA, accélérateurs dédiés, Tmers avancés, bus CAN/CANFD…), ces puces peuvent exécuter localement des tâches critiques sans solliciter le cœur de processeur, renforçant ainsi la réactivité et l’efficacité énergétique du système.
La délégation des fonctions de bas niveau — acquisition de données, pilotage des moteurs, gestion temps réel des communications — à des nœuds spécialisés libère d’importantes ressources CPU. Cellesci peuvent alors être consacrées à des algorithmes d’intelligence artificielle embarquée. L’IA n’est plus un simple atout, mais un besoin stratégique pour doter les robots mobiles d’une véritable autonomie décisionnelle, leur permettant d’analyser leur environnement, de réagir instantanément et de s’adapter à des situations complexes.
Cette présentation dressera un état de l’art des évolutions technologiques des microcontrôleurs, avec un focus particulier sur les architectures ARM, largement adoptées pour le contrôlecommande grâce à leur rendement énergétique, leur écosystème logiciel et leur intégration native des protocoles industriels.
Pour l’atelier :
Optimisation des ressources dans un réseau de contrôleurs distribués CAN/CANFD Application : commande d’un robot mobile 4 roues motrices.
Objectif de l’atelier :
- Initiation au réseau et bus de terrain : réseau CAN/CANFD opérationnel
- Initiation pratique à la mise en œuvre d’un bus CANFD
- Pilotage temps réel sur un réseau CAN :
* Envoi et réception de trames de consigne de vitesse
* Synchronisation des commandes entre les nœuds pour assurer une traction coordonnée.
* Lecture des vitesses
- Optimisation CPU et décharge des cœurs de calcul
* Délégation des tâches basniveaux (PWM, mesure de vitesse via encodeur) aux périphériques matériels (DMA, timers).
* Mesure comparative de l’occupation CPU avant/après décharge : réduction d’au moins 40 % du temps processeur consacré aux I/O

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Samir Loudni is a Professor in Data and Decision Sciences at IMT Atlantique since 2020 and has been the Head of the TASC team (UMR CNRS LS2N) since 2022. His research focuses on Constraint Programming, Data Mining, Preference Learning, and Constraint Optimization, where he has made significant contributions. He has developed innovative methods for solving complex combinatorial problems, notably by integrating Variable Neighbourhood Search (VNS) with constraint programming to enhance optimization processes. His work also includes pioneering approaches in declarative pattern discovery, leveraging constraint programming to efficiently extracting patterns, pattern sets, and skypaterns in transactional databases, thus advancing knowledge extraction techniques from large datasets. Additionally, he has contributed to the development of constraint-based languages for pattern discovery, enabling more expressive and flexible data mining processes.

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Ingénieur-diplômé en sciences de l’information géographique, et docteur en science des données de MINES Paris – PSL, Clément Iphar mène des recherches à l’intersection de l’analyse des données de mobilité, de la géovisualisation, et de l’évaluation des usages et des risques liés au transport maritime. Ses travaux actuels portent, entre autres, sur la détection d’escales portuaires à partir des données AIS, et la cartographie floue des pratiques de pêche lagonaire en Polynésie française. Il est actuellement chercheur contractuel à l’Université de Bretagne Occidentale, à Brest.

Title: Cas applicatifs de l'Intelligence Artificielle dans le cadre des réseaux du transport maritime
Dans cette intervention, nous explorons l'application de l'intelligence artificielle à l'analyse des réseaux de transport maritime, en s'appuyant avant tout sur la donnée maritime massive permise par le développement du système AIS (Automatic Identification System) et son exploitation. Nous abordons les spécificités du réseau maritime, sa structure dynamique, les obstacles qui en affectent l’organisation (naturels, réglementaires, économiques) et les moyens de les modéliser. Côté méthodologique, nous mobilisons à la fois des approches déterministes classiques (basées sur des ontologies ou des règles logiques) et des méthodes statistiques plus récentes d’apprentissage. Ces outils permettent d’identifier les routes maritimes, de prédire les comportements des navires et de quantifier et qualifier les escales portuaires, dans une perspective d’optimisation des flux, de compréhension fine des trajectoires, de détermination de l’intégrité de la donnée et d’amélioration de la gestion des réseaux maritimes.

Les graphes sémantiques sont une méthode de représentation des données qui permettent de visualiser les relations entre les entités. Les modèles de graphes spatio-temporels sont une extension de cette méthode, qui permettent de représenter les relations spatiales et temporelles entre les entités. Cela permet une modélisation plus fine et plus complète des données dans des domaines où la position et le temps sont des facteurs clés. Plusieurs exemples d'utilisation de modèles de graphes spatio-temporels seront présentés. Un exemple sera dans le domaine médical avec l'IRM fonctionnel, où les graphes spatio-temporels sont utilisés pour représenter les connexions fonctionnelles entre les différentes régions du cerveau au fil du temps. Un autre exemple sera dans le domaine environnemental avec l'occupation des sols, où les graphes spatio-temporels sont utilisés pour représenter les relations spatiales et temporelles entre les différents types d'occupation des sols au fil du temps.

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Nazha Selmaoui-Folcher est professeure des universités en informatique à l’Université de Nouvelle-Calédonie et data scientist. Spécialisée en analyse d’images et fouille de données spatio-temporelles, elle a coordonné le projet ANR FOSTER sur l’érosion des sols et contribué à plusieurs projets en santé et environnement, dont SpiRAL (leptospirose) et AGING (moustiques Aedes aegypti). Ses recherches portent sur l’intelligence artificielle, l’apprentissage automatique, l’analyse de séries d’images satellites et l’exploration de graphes. Elle est active dans de nombreux comités scientifiques internationaux et au sein du Conseil National des Universités et de l’ANR.

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I obtained an engineering degree in computer science, electronics and automatics in 2011. Having specialized during my studies on spatial remote sensing and then on close-range remote sensing by drone (UAV), I continued on a PhD in Signal & Images at Télécom ParisTech, 100% funded by the French Government Defense Directorate General of Armaments (DGA) which I defended in 2016 on the theme of high-precision underwater mapping assisted by autonomous robots (UUV & USV). My research area is close-range remote sensing and field robotics. I work in all environments (air, land and sea) with a particular focus on the underwater environment. As a professional diver, I take care of field operations involving robots and their sensors and I co-organize underwater experimentation workshop as part of the French GDR Robotique. I am currently an assistant professor at EPITA where I co-created with Laurent Beaudoin the SEAL team (Sense, Explore, Analyze and Learn) on exploration robotics, which was merged in 2022 with the Image Processing and Pattern Recognition group. I am also co-responsible for the robotics axis of EPITA. https://www.lre.epita.fr/perso/lo%C3%AFca-avanthey/

What lies beneath the surface? Why must we deepen our understanding of the deep blue? This talk explores how robotics is transforming our ability to explore, sense, and map the underwater world. From remotely operated robots to autonomous vehicles and their sensors, from image datasets to ground-truth acquisition, discover the technologies and challenges behind creating digital twins of the ocean.

Holder of a DEA in Imaging in Universe Sciences from the University of Nice Sophia- Antipolice (UNSA), I worked for a few years as a research engineer for a large European Aerospace industrialist. I then continue on a PhD in Signal & Images at Télécom ParisTech, which I defended in 2001 on the theme of spatial remote sensing. My research area is close-range remote sensing and field robotics. I work in all environments (air, land and sea) with a particular focus on the underwater environment. As a professional diver, I take care of field operations involving robots and their sensors and I co-organize underwater experimentation workshop as part of the French GDR Robotique. I am currently an assistant professor at EPITA where I co-created with Loïca Avanthey the SEAL team (Sense, Explore, Analyze and Learn) on exploration robotics, which was merged in 2022 with the Image Processing and Pattern Recognition group. I am also co-responsible for the robotics axis of EPITA. https://www.lre.epita.fr/perso/laurent-beaudoin/

ROS 2 (Robot Operating System 2) is at the core of many modern robotic systems. This talk offers an overview of what ROS 2 is, why it matters, and how it enables smarter, modular, and scalable robotic architectures. We'll explore its key features, ecosystem, and tools.

Dr. Juba Agoun est un jeune maître de conférences en science des données à l'Université Lumière Lyon 2 et membre du laboratoire ERIC – équipe Système d’information décisionnelle (SID). Aujourd’hui, ses travaux se concentrent sur l’intégration et la gestion des données pour leur analyse et exploitation, en mettant l’accent sur la qualité, la sécurité et l’interopérabilité des systèmes intelligents.

Les troubles du sommeil ont un impact majeur sur la santé et la qualité de vie des patients, mais leur diagnostic reste complexe en raison de la diversité des symptômes. Aujourd’hui, les avancées technologiques, combinées à l’analyse des données médicales, ouvrent de nouvelles perspectives pour une meilleure compréhension de ces troubles. En particulier, l’intelligence artificielle explicable (XAI) vise à rendre les décisions des modèles d’IA compréhensibles et interprétables par les utilisateurs. Dans ce talk, nous présenter notre approche qui se base sur le clustering afin de regrouper les patients selon différents profils de troubles du sommeil tout en intégrant une approche explicable. Nous identifions les facteurs clés influençant ces pathologies. Nous discuterons le protocole d’expérimentation sur des données réelles anonymisées qui illustre l’efficacité et la pertinence de notre approche.

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Registration

Registration Fees

Researchers

15,000 DA

PhD Students

10,000 DA

International

150€

Others

20,000 DA

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Pr. SEBBAGH Abdennour	University of Guelma	Algeria
Dr. BOUALLEG Abdelhalim	University of Guelma	Algeria
Mr. MADI Belgacem	University of Guelma	Algeria
Ms. KHALFAOUI Amina	University of Guelma	Algeria
Mr. ADJAL Akram	University of Guelma	Algeria
Mr. ZENTAR Mohamed Dhia El Hak	University of Guelma	Algeria
Ms. HAMOUCHI Hala	University of Guelma	Algeria
Mr. DJEBLI Mohamed Amdjed	University of Guelma	Algeria
Ms. TABA Zahra	University of Guelma	Algeria

Pr. KECHIDA Sihem	University of Guelma	Algeria
Pr. TEBBIKH Hicham	University of Guelma	Algeria
Pr. BOULOUH Messaoud	University of Guelma	Algeria
Pr. ZIOU Djemel	University Sherbrooke	Canada
Pr. SEKER Serhat	University of Istanbul	Turkey
Pr. CETIN AKINCI Tahir	University of California	USA
Dr. SAHLI Nabil	Sonatrach Company	Algeria
Dr. ZIGHEM Mohammed-En-Nadhir	Huawei's Finland R&D Centre	Finland
Pr. TALEB Ahmed Abdelmalik	Polytechnic University of Hauts de France	France
Pr. LAGNA Mohand	University of Blida	Algeria
Pr. KELAIA Ridha	University of Skikda	Algeria
Dr. BRAHIMI Mohammed	ENSIA, Algiers	Algeria
Dr. BELKACEM Sami	ENSIA, Algiers	Algeria
Dr. MEDDOURI Nida	EPITA, Paris	France
Pr. James Whidborne	Centre for Aeronautics at Cranfield University	United Kingdom
Dr. SOUANEF Toufik	Centre for Aeronautics at Cranfield University	United Kingdom
Dr. CLÉMENT Iphar	University of Western Brittany	France
Dr. LEBORGNE Aurélie	University of Strasbourg	France
Dr. SALMON Loïc	University of New Caledonia	France
Dr. SALMAOUI Nazha	University of New Caledonia	France
Pr. NIAR Smail	INSA, Polytechnic University of Hauts-de-France	France
Pr. LOUDNI Samir	IMT Atlantique	France
Dr. DEHAK Reda	EPITA, Paris	France
Dr. MHAMDI Faouzi	University of Béja	Tunisia
Pr. CHAKOUR Chouaib	University of Skikda	Algeria
Dr. BOUBIDI Assia	University of Guelma	Algeria
Dr. BENZELTOUT Boubaker	University of Guelma	Algeria
Dr. MENASRIA Azzeddine	University of Guelma	Algeria
Dr. LOUCIF Fatiha	University of Guelma	Algeria
Pr. MENDACI Sofiane	University of Guelma	Algeria
Dr. TABA Mohamed Tahar	University of Guelma	Algeria
Dr. BOUALLEG Abdelhalim	University of Guelma	Algeria
Dr. FISLI Soufiane	University of Blida	Algeria
Dr. BELLOULA Abdelmalek	Military Polytechnic School	Algeria

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laig.robia2025@gmail.com

labo-laig-fst@univ-guelma.dz

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University 8 Mai 1945 - Guelma

Laboratoire d'Automatique et Informatique Guelma

The First International Autumn School on Robotics and Artificial Intelligence

About RobIA'25

Important Dates

Submission of applications

Notification of Acceptance

Registration

Event Dates

Our Speakers

Pr. HADID Abdenour

Dr. MEDDOURI Nida

Pr. TEBBIKH Hicham

Pr. ZIOU Djamel

Pr. TALEB-AHMED Abdelmalik

Pr. James Whidborne

Dr. BRAHIMI Mohammed

Dr. BELKACEM Sami

Dr. ZIGHEM Mohammed En Nadhir

Dr. Abdelmalek BELLOULA

Dr. DEHAK Reda

Pr. LOUDNI Samir

Dr. CELEMENT iphar

Dr. LEBORGNE Aurelie

Dr. SALMON Loïc

Dr. SALMAOUI Nazha

Pr. NIAR Smail

Dr. AVANTHEY Loïca

Dr. BEAUDOIN Laurent

Dr. AGOUN Juba

Registration

Registration Fees

Researchers

PhD Students

International

Others

Organizing Committee

Chair

Dr. MENASRIA Azzeddine

Scientific Committee

Chairs

Honorary Chair

Co-Honorary Chair

General Chair

Program

Sponsors

Accommodations

El Baraka Hotel

Mermoura Hotel ★★★★☆

Lalla Maouna Hotel ★★★☆☆

Contact Us

Email

Phone

Social Media

Location

Mermoura Hotel
★★★★☆

Lalla Maouna Hotel
★★★☆☆