Projects
Towards a Learning and Adaptive European pandemic Preparedness and emergency response System KU Leuven
Smart crowd security management through adaptive routing Ghent University
Public safety is one of the most pressing concerns in the cities of today. Whenever multiple citizens gather in public space, crowd dynamics can cause hazardous situations. To avoid evacuations and disasters, it is pertinent that the movement and evolution of a crowd can be tracked, predicted and adapted, while assuring privacy. Therefore, this project will integrate crowd tracking and modeling, which will give input to a routing algorithm. ...
Machine Learning Acceleration on Heterogeneous Platforms KU Leuven
Advances in the fields of biomedical sensors, wearables and medical implants, in combination with state-of-the-art algorithms from signal processing, machine learning and artificial intelligence, are transforming the healthcare landscape. Systems built around these technologies enable remote health monitoring, improve patient care, detect life-threatening conditions or even predict health events. Yet the full integration of such technologies ...
Machine Learning @ the Extreme Edge Karel De Grote Hogeschool
Smart chips: embedding machine learning at circuit level. KU Leuven
EdHub & AI: Implementation of AI in Primary and Secondary Education. Both for Classroom Practices and Learning Analytics HOWEST
Automatic food intake monitoring for the ageing population KU Leuven
Food intake monitoring can play an important role in the prevention of malnutrition among older adults. Traditional monitoring methods typically involve the use of pen-and-paper food diaries or questionnaires. While digital alternatives exist, these tools rely on manual data entry, often multiple times a day. Furthermore, the recorded data may be incomplete and contain mistakes due to human error or a deliberate misreporting of the food ...
Sustainable AI Adaption on Energy Aware IoT Systems (Saints). University of Antwerp
Infinite−lifetime Sensing through Self−Specialization KU Leuven
My goal is to develop a disruptive class of monitoring platforms towards sustainable, infinite life−time sensing for environmental monitoring, health assessment, and personalized services. The key novelty is the introduction of self−specialization. I will create integrated systems that start out very generic, though are capable of collecting knowledge about their operating conditions, as well as their expected behavior. Through the ...