Current research projects

These are the current research projects. You can also view the list of previous research projects.
  • Learning optimal preventive strategies to mitigate epidemics of latent infectious diseases (FWO)

    Viruses that cause latent infectious diseases (e.g. HIV, hepatitis C, papilloma) pose an important threat to public health. The most efficient way to mitigate epidemics of latent infectious diseases is by prevention. However, the complex dynamics of such epidemics render the development of effective and efficient prevention strategies challenging. To support the decision making with respect to prevention strategies, epidemiological models are frequently used. In this research project we aim to improve the state-of-the art of epidemiological models and to use reinforcement learning to learn optimal preventive strategies in epidemiological models.

  • Multi-Objective Decision Making with Guarantees (FWO)

    We investigate the possibilities of Multi-objective Decision Making (MODeM), and particulary Reinforcement Learning (MORL) for decision support. Specifically, we are interested in providing guarantees with respect to user utility.

  • Life-Long Hierarchical Reinforcement Learning (FWO)

    Reinforcement Learning is currently applied to single tasks, for instance parking a car, playing a video game, navigating to a goal, etc. Hierarchical Reinforcement Learning allows to decompose a complex task into simpler sub-tasks. For instance, an agent may progressively learn to grab objects, turn them, open doors, navigate through hallways (with doors), then do something interesting in a complete building. This project applies Hierarchical Reinforcement Learning to very complex tasks, for which the agent has to learn many skills.

  • Coordinating Human and Agent Behaviour in Collective-Risk Scenarios (FWO)

    Different situations, wherein humans interact among themselves or through technologies in hybrid socio-technical systems, resemble social dilemmas, i.e. situations wherein participants have to select between short-term personal profits and long-term social benefits. The behavioural outcome in those dilemmas is very much dependent on how successful the participants are in calculating the risk associated to the uncertainty of future rewards and on anticipating the opponents’ choices.

  • Fleet Reinforcement Learning for Wind Farm Control (FWO)

    The world is a connected place, in which the cloud plays an increasingly vital role. One example is the Internet of Things, which has a topic of conversation in virtually all industries. The control of physical devices is no exception: modern wireless sensors allow these devices to move forward from local controllers towards smarter cloud-based architectures.

  • C-CURE: Cost-Sensitive Dynamic User Authentication with Reinforcement Learning (INNOVIRIS)

    C-CURE addresses a pressing problem with regards to user authentication: lack of flexibility. Current authentication methods are uniform and static. Their power, and thus the involved overhead for the user, is the same for small (in terms of the required security) and large processes or transactions. C-CURE aims to develop a flexible multi-modal approach which adapts the security level and procedure depending on the expected risk and the context of the user.

  • SeCloud: Security-driven Engineering of Cloud-based applications (INNOVIRIS)

    The conception of a holistic & coherent set of tools, technologies and techniques that will allow the software industry to proactively think about security in their Cloud-based applications whether SaaS or Mobile. The four considered perspectives are architecture, infrastructure, programming and process.

  • ArTificial Language uNdersTanding In robotS (ATLANTIS) (CHIST-ERA)

    ATLANTIS attempts to understand and model the very first stages in grounded language learning, as we see in children until the age of three: how pointing or other symbolic gestures emerge from the ontogenetic ritualization of instrumental actions, how words are learned very fast in contextualized language games, and how the first grammatical constructions emerge from concrete sentences.

  • A SCAlable and modular system for eneRGY trading between prosumers (SCANERGY)

    SCANERGY will be built up by an intelligent multi-agent system that can manage the electricity produced and consumed both on a lower level (dwellings and neighbourhoods) as well as on a higher level (cities). The SCANERGY system enables the smart energy trade between prosumers, while coping with the inherent real-time dynamism in electricity demand and supply.

  • Stable MultI-agent LEarnIng for neTworks (SMILE-IT) (IWT-SBO)

    SMILE-IT aims to extend and improve upon the state of the art in multi-agent reinforcement learning and network management in order to implement and validate a generic, stable, and robust multi-agent reinforcement learning framework, capable of (semi-)automatically managing modern networked systems (e.g., telecommunications networks, smart grids, air traffic routing, traffic control) through software.

  • How do we really respond in iterated games – inferring strategies from behavioral experiments in the Prisoners Dilemma game (FWO)

    Cooperators should be evolutionary extinct because cheaters always benefit more than cooperators. However cooperation is all around us and without it human society, could not exist. One of the mechanisms which could explain how cooperation emerged and persisted is direct reciprocity, under which the individuals cooperate with each other to ensure future cooperation. A number of strategies emerge in theory which would be the most beneficial to the players in this setting and also lead to high cooperation. However it is still uncertain if these are the strategies people actually use. 

    Here, I will conduct a series of Iterated Prisoner's Dilemma experiments where large numbers of players play long games through a computer interface. Typically experiments which search for strategies have very small number of rounds and the resulting level of cooperation is not very high. However, in longer experiments after the initial drop, the level of cooperation is rising to a very high level. The question is what are the strategies that people use when cooperation is established and what kind of behaviour drives this change. Afterwards, I will perform experiments which will test how outside contexts (like the information the players receive, etc.) influence the strategies. Finally, I will pay special attention to the possibility of negotiation during the game. We will test if the inferred strategies explain the observed behaviours using agent based simulations and evolutionary models. 



  • Identifying the mechanisms involved in transducing binding information through SH3-SH2 supradomains and their role in regulating the activity of members of the family of Src kinases. (FWO)

    Communication is essential at all biological scales. Our cells have been rigged with communication systems that allow them to respond properly to environmental cues. These systems, or signaling pathways, are composed of different proteins, whose activity is regulated by other proteins. This intricate regulatory system ensures that cells provide the right response, both in kind and magnitude. Errors in regulation often result in diseases.

  • BRiDGEIRIS - Brussels big data platform for sharing and discovery in clinical Genomics (INNOVIRIS)

    Genome or Exome screening has started to become a norm in clinical settings, with an aim to provide an improved and effective diagnosis process for patients. To cater to this need, hospitals need an effective, certified and reliable (Bio)informatics solution that can merge, store and analyze the multidimensional data forms. Also, challenge is to handle the huge avalanche of data generated from sequencing. This project has been planned to design and develop solution to afore mentioned needs of Centre for Medical Genetics of VUB UZ Brussel, ULB Erasme and UCL, De Duve.

  • An integrated Methodology to bring Intelligent Robotic Assistive Devices to the user (MIRAD) (IWT-SBO)

    The overall objective of the project is to develop and validate an integrated methodology to design intelligent robotic assistive devices and to test these devices in clinical conditions.

    The integration relates to all aspects of such devices and their interaction with humans: engineering aspects (mechatronic design, intelligent control, and simulation), physiological and clinical aspects, and psychological aspects.

  • ESSENCE - Evolution of Shared SEmaNtics in Computational Environments

    ESSENCE is a European Marie Curie Actions Project (FP7, ITN) that aims to improve robustness and resilience of natural language processing systems. In everyday life, humans exhibit strong skills in resolving communication problems by re-negotiating what they mean. Modern-day computational systems, however, are lacking in resilience and robustness in this respect. Whenever different components with different vocabularies and models of meaning interact within distributed, open environments, they have to rely on their human designers’ abilities to resolve problems of miscommunication.

  • Adaptive Preferences for a Changing World: an AI study in the co-evolution of preferences and group formation. (FWO)

    Many strategic and economic situations are characterized by participant preferences that take into account the well being of others.  These preferences guide humans in their choices with whom to participate in economical or social activities.  Little attention has been given to how this group formation shapes these preferences and, vice versa, how these preferences shape formation of groups.

  • Towards an understanding of the interplay between other regarding preferences and group formation in strategic environments (FWO)

    Many strategic situations are characterized by player’s preferences that take into account the well being of others.  These preferences guide humans in their choices with whom to participate in economical or social activities.  Little attention has been given to how group formation shapes players’ beliefs concerning preferences and how preferences guide the formation of groups.

  • Multi-agent reinforcement learning of coordination and problem structure (FWO)

    In this project, we will investigate the automatic detection of interactions between agents and its use for local coordination, without requiring agents to have a full view of other agent’s state and actions. Furthermore, we will validate the techniques developed from gained insights on settings that are fully cooperative, fully competitive and mixed.

  • Personalized Products Emerging from Tailored User Adapting Logic (Perpetual) (IWT-SBO)

    The ambition of the PERPETUAL project is to develop methods and algorithms that achieve automatic, user-oriented functional demand profiling in support of enhancing product service performance, while requiring less resources through an increased efficiency as a result of well-optimized control policies for a wide range of applications.

  • Advancing behavioral and cognitive understanding of speech (ABACUS) (ERC)

    This is a European Research Council starting grant project that investigates which cognitive mechanisms allow us to use combinatorial speech. Human speech is unique because it uses a small set of basic speech sounds to make an unlimited set of possible utterances. This combinatorial structure allows us to make new words (such as “blog” or “app”) easily using speech sounds that we already know. Humans are the only apes that can do this, yet we do not know how our brains do it, nor do we know how exactly our abilities are different from those of other apes. Using novel experimental techniques to investigate human behavior and novel computational techniques to model human cognition, it is the goal of this project to find out how we deal with combinatorial speech.

  • A Cognitive and Computational Investigation of Combinatorial Speech (Innoviris-BB2B)

    This is a project sponsored by the BrainsBack to Brussels initiative of the Brussels region. It intends to investigate the cognitive mechanisms that give us combinatorial speech in order to establish how these mechanisms could have evolved. Combinatorial speech is the ability to make new words by recombining pre-existing speech sounds. Humans are the only apes that can do this, yet we do not know how our brains do it, nor how exactly we differ from other apes, and hence we cannot propose a plausible scenario of how it evolved. Using new experimental techniques to study human behavior and new computational techniques to model human cognition, the project will find out how we deal with combinatorial speech and how this ability may have evolved.