Connectivity Fund

Róbert Móro Researcher at Kempelen Institute of Intelligent Technologies To mitigate disinformation with AI in a trustworthy way, it should prioritize human agency and control, transparency, and accountability including the means for redress. This can be achieved by using AI to support rather than to replace media professionals, such as fact-checkers, in their efforts to […]

Gonçalo Gomes Araújo PhD at Nova FCT We value a mathematical theory for its generality and its tractability. For example, a magma (that is, a set with a binary operation) is very general because any theorem on it will have an impact on almost all algebras and possibly beyond, but the structure is so weak

Hakob Grigoryan PhD at NVISION With the evolution of intelligent sensing devices and the Internet of Things (IoT), a vast amount of data is generated from various sources, including sensors, cameras, and network infrastructures, and is transmitted to servers for analysis. Data streaming from sensors in IoT systems might face quality issues like incompleteness due

Patrik Goldschmidt PhD candidate at Kempelen Institute of Intelligent Technologies With the rise of information technology and the Internet, the number of cybersecurity incidents has grown immensely. As a response, the research area of Intrusion Detection Systems (IDSs), aiming to detect and mitigate cyber threats, has gained significant attention. Our research focuses on Network IDSs

Muhammad Waseem Postdoctoral Researcher at Faculty of Information Technology, University of Jyväskylä, Jyväskylä, Finland Large Language Models (LLMs) are advanced AI tools capable of understanding and generating human-like text, advancing various sectors, including healthcare. This project aims to enhance healthcare services using LLMs, focusing on improving their trustworthiness for clinical applications. Trustworthiness encompasses reliability, fairness,

Giacomo Cignoni Research Fellow at the University of Pisa Learning continually from non-stationary data streams is a challenging research topic of growing popularity in the last few years. Being able to learn, adapt and generalize continually, in an efficient way appears to be fundamental for a more sustainable development of Artificial Intelligent systems. However, research

Lennert De Smet PhD at KU Leuven Transparency and technical robustness are two fundamental requirements for AI systems following the European Union AI Act, especially in higher-risk domains. Transparency is intricately related to the notion of explainability, allowing an AI system to accurately describe the reasoning behind its predictions. Through such explanations does the system

Georgios Chalkiadakis Professor at Technical University of Crete Current mobility-as-a-service platforms have departed from the original objectives of the sharing economy-inspired social ridesharing paradigm: regrettably, they view drivers as taxi workers; focus on profit maximization rather than fair travel costs’ allocation; and disregard essential private preferences of users (relating for instance to their feeling of

Marko Tesic Post-doc at LCFI, University of Cambridge, UK Recent advancements in AI, particularly in language modeling, have rekindled concerns about the potential automation of certain roles within the human workforce. To better understand which roles are susceptible to automation and to ensure the trustworthy and reliable deployment of AI, I aim to establish a

Lorenzo Pacchiardi Post-doc at University of Cambridge Artificial Intelligence (AI) systems (such as reinforcement-learning agents and Large Language Models, or LLMs) are typically evaluated by testing them on a benchmark and reporting an aggregated score. As benchmarks are constituted of instances demanding various capability levels to be completed, the aggregated score is uninformative of the