Keynote speakers


Peter Sloot

University of Amsterdam
Making Complex Systems Tractable: The Predictive Power of Computational Science
We live in a complex world and are surrounded by complex systems. These complex systems display endless signatures of order, disorder, self-organisation and self-annihilation. Understanding this complexity is one of the biggest challenges of our time. In complex systems the interactions form exquisite networks, each component providing feedback pathways resulting in non-linear dynamics among the selected interaction partners. It is not just complicated, it is complex and it is beautiful. In this talk I will discuss how this complexity emerges at the edge of chaos, we will peek into the collective behaviour of crowds of people, the intricacies of the immune system, the inner workings of organizations and the (un-)importance of the kingpins of criminal networks, all ‘magically’ emerging from the simple rules of Nature.

Nikolay O Nikitin

Automated machine learning: current state and perspectives
Automatic Machine Learning (AutoML) tools are finding increasing applications in solving applied problems in a wide variety of domains. However, their further improvement is limited by the high computational complexity of AutoML algorithms. How can they be improved? What are the prospective directions of AutoML-related researches? Can the existing export knowledge be used? How can ChatGPT help here?
In this talk I will discuss the current state of AutoML field and most promising direction for the future development. The special attention will be taken to meta-optimisation techniques and application of AutoML for industrial tasks.

Konstantinos Papoutsakis

Mediterranean Hellenic University, Greece

The interaction between humans and objects, known as Human-Objects Interactions (HOI), holds significant importance within human behavior. We naturally manipulate common or unknown objects in many ways and contexts to accomplish tasks of varying significance and impact. Given the recent success and impact of Artificial Intelligence (AI) and Deep/Machine Learning from health care to manufacturing, the challenging and open topic of visual analysis of HOI is attracting a lot of attention from both academia and industry. In this Lecture, we will explore ideas and techniques for a novel framework that will empower smart agents and robotic systems to visually recognize and anticipate high-level semantics of HOI based on Deep Neural Networks, Knowledge Graphs, and Visual Reasoning.


Alexandra Vatjan

The topological approach for explainable AI

Davide La Torre

SKEMA (France)
Artificial Intelligence and Beyond for Finance
No matter the specific industry or application, AI has become a new engine of growth. Both finance and banking have been leveraging AI technologies and algorithms and applied them to automate routine tasks, procedures, forecasting, and improved the overall customer experience. This talk will provide an overview on recent applications of AI to finance, in particular:

How ML algorithms can forecast signals in the modern dynamic financial world, enabling investors to make data-driven decisions in this rapidly evolving market,
How AI can be leveraged to create sophisticated quantitative tools for comprehensive financial analysis, providing informed decision-making and enhance investment strategies,
How to decode the intricacies of market sentiments to uncover valuable insights and gain a deeper understanding of market dynamics,
How to use AI algorithms to optimize risk-return profiles and maximizing investment performance,
How to develop transparent and interpretable models that shed light on risk factors and support effective risk management strategies,
How to deploy reinforcement learning techniques, enabling portfolios to adapt and optimize strategies in response to dynamic market conditions.
The topics covered by this talk provide an understanding on how AI has affected the banking and financial industries and how it will continue to change them in the years to come.
Reference: M. Corazza, R. Garcia, F. Khan, D. La Torre, H. Masri (edited by), Artificial Intelligence and Beyond for Finance, Word Scientific, 2024.

Raja Jayaraman

Khalifa University (Abu Dhabi)
Emerging Frontiers in AI and Digital Twins for Supply Chains
As supply chain management continues to evolve in the era of digital transformation, the integration of Artificial Intelligence (AI) and Digital Twins emerges as a transformative paradigm. This talk will explore the burgeoning frontier where AI and Digital Twins converge to reshape the landscape of supply chain operations, optimizing efficiency, resilience, and sustainability. AI, with its machine learning algorithms and data analytics capabilities, is revolutionizing supply chain processes. It enables predictive and prescriptive analytics, demand forecasting, route optimization, and real-time decision-making. AI-driven insights empower organizations to proactively address disruptions and streamline inventory management. Digital Twins acts as virtual replicas of physical supply chain entities and offer a dynamic, real-time representation of assets, processes, and products. Leveraging Internet of Things (IoT) data, Digital Twins enables visibility, simulation, and scenario analysis. They provide a holistic view of the supply chain, aiding in proactive issue resolution and performance optimization. Together, AI and Digital Twins are instrumental in addressing supply chain challenges. Furthermore, they support sustainability goals by optimizing energy consumption and carbon footprint reduction.

Hatem Masri

Applied Science University (Bahrain)

The presentation concerns the integration of artificial intelligence (AI) into modern teaching practices. Its objective is to equip future educators with a comprehensive understanding of the current and future capabilities of AI in education, with a particular focus on generative AI models such as ChatGPT, Google's Bard, and Microsoft's Bing Chat.

The presentation will commence with an exploration of the opportunities and challenges associated with incorporating AI in classrooms. It will then provide a detailed explanation of AI fundamentals and machine learning. We will discuss the necessity of reframing education in the era of AI, preparing students for an AI-driven workforce, fostering AI literacy, promoting computational thinking, and cultivating a culture of lifelong learning. Furthermore, we will examine strategies for adapting pedagogical practices to integrate AI, offering insights into learning theories, active learning approaches, and problem and project-based learning strategies with AI. We will also highlight the role of Bloom's taxonomy in an AI-enhanced classroom. Finally, we will address the significance of teacher professional development in the AI era. This will encompass the need for continuous adaptation and growth, AI competencies for educators, and the evolving nature of education with the increasing integration of AI.