Ann Nowé is full professor at the Vrije Universiteit Brussel (VUB) where she heads the AI-Lab. Her main research interest is Reinforcement Learning (RL) including Multi-Agent RL and Multi-Criteria RL. Her team developed novel algorithms which were tested in domains such as smart grids, communication networks, mechatronics and scheduling problems. She also investigates how RL policies can be made more transparent and be formally verified. Currently , she coordinates the EU Horizon project PEER, Redefining Human-AI Collaboration for Complex Decisions. Within the Flemish AI program, she coordinates the embodied AI challenge.
Ann Nowé is a former board member of EurAI (European AI association), IFAAMAS (International Foundation for Autonomous Agents and Multiagent Systems) and chairman of the BNVKI (BeNeLux AI association). She was and is also chair of several international AI conferences, incl. ECAI’23, EWRL’23, AAMAS’25 and HHAI’26. She is furthermore an elected EurAi fellow, initiator of the AI Experience center, and academic director at FARI, the AI institute for the common good at Brussels.
BRiDGEIRIS – Brussels big data platform for sharing and discovery in clinical Genomics
C-CURE: Cost-Sensitive Dynamic User Authentication with Reinforcement Learning
SeCloud: Security-driven Engineering of Cloud-based applications
An integrated Methodology to bring Intelligent Robotic Assistive Devices to the user (MIRAD)
Coordinating Human and Agent Behaviour in Collective-Risk Scenarios
Fleet Reinforcement Learning for Wind Farm Control
Stable MultI-agent LEarnIng for neTworks (SMILE-IT)
Learning optimal preventive strategies to mitigate epidemics of latent infectious diseases
Multi-agent reinforcement learning of coordination and problem structure
A SCAlable and modular system for eneRGY trading between prosumers (SCANERGY)
Learning Control for Production Machines (Lecopro)
Multi agent reinforcement learning in large state spaces
Personalized Products Emerging from Tailored User Adapting Logic (Perpetual)
Online multi-criteria reinforcement learning
Operator Info
Adaptive multi-objective metaheuristics for energy-saving problems
INSILICO – The in silico Wet Lab
Design of new multi-domain network algorithms
DiCoMas
Multi-type ant colony optimization
Distributed reinforcement learning for multi-agent sequential decision problems
Study of Traffic Engineering in IP-over-Optical NetworksPublications on VUB CRIS