IDLab targets the most daunting challenges industry is facing to deliver digital transformation, connecting everything and extracting high value from data.

The application domains of our research span the entire spectrum from connected systems to artificial intelligence.

Along this spectrum our five research programs are situated:

PIN focuses on AI-driven future communication networks that optimize network resources to deliver extremely reliable and hyper-efficient connectivity for diverse applications and devices. (Lead: prof. dr. Johann Marquez-Barja)Application domains: Connected multimodal mobility, transportation and logistics, Internet of Things, emergency services, smart cities.

PRS researches integrated sensing and communications by exploiting the algorithms, signals and protocols across the increasing density of wireless infrastructure and devices. (Lead: prof. dr. Jeroen Famaey)Application domains: Energy-aware networking, low-power devices, wireless (crowd) sensing and localization, environmental monitoring from edge devices to non-terrestrial networks.

ADAPT-I focuses on end-to-end reinforcement learning AI solutions for agents that continuously need to assess and adapt to changing environments. (Lead: prof. dr. Siegfried Mercelis)Application domains: AI for industrial and (bio-)chemical processes, (autonomous) mobile systems on land, in the air or over water, semiconductor manufacturing, smart energy grids and services.

DATADOR leverages graph-based Artificial Intelligence for relationship modeling, making use of the appropriate shallow or deep learning techniques depending on the use case. (Lead: prof. dr. Kevin Mets)Application domains: AI for mobility and traffic optimization, financial risk detection, sports and physical performance, industrial processes (in particular chemistry/bio-chemistry).

sqIRL focuses on fundamental insights in explainable, intrinsically interpretable, efficient, and reliable AI systems. (Lead: prof. dr. José Oramas)Application domains: All areas where insights in the transparency and reliability of AI systems are fundamental and relevant for scientific discovery and learning, critical understanding of AI black boxes, regulatory compliance, debugging.

For the activities of IDLab at Ghent University, see https://www.idlab.ugent.be