The First International Autumn School on Robotics and Artificial Intelligence

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.

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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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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.

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.

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.

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

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.