Complex and challenging decisions: the STAR Lab team explores trustworthy fundamental AI to help people make better decisions in situations from sustainable logistics, to robust planning under uncertainty, to fair energy markets.
Data-driven building optimisation
Uncertainty
Optimisation
IJCAI 2024, "Robust Losses for Decision-Focused Learning"
Real Estate, "An Agent-Based Market Analysis of Urban Housing Balance in the Netherlands"
Wantong Zhang, MSc, graduated on "Crowd Risk Assessment in Scheveningen: Exploring the role of crowd and contextual factors"
CPAIOR 2024, "On Learning CP-SAT Resolution Outcomes Before Reaching Time-Limit"
Kees t' Hooft, MSc, graduated on "Advancing RL Fleet Planning Through Robust Reward Design and Graph Neural Networks"
Simon Mariën, MSc, graduated on "Multi-Objective Differential Evolution Optimization of Ion Beam Analysis Spectra"
Kylian Kropf, MSc, graduated on "Supporting Non-Expert Users in Modelling and Understanding AI: An interactive CP approach"
ALA 2024 workshop at AAMAS'24, "Bayesian Ensembles for Exploration in Deep Q-Learning
e-Energy 2024, "Incentives for Accurate Energy Predictions: How to Reduce Epistemic Uncertainty"
Computers & Chemical Engineering, "Mixed-integer Optimisation of Graph Neural Networks for Computer-Aided Molecular Design" (pdf)
ECC 2024, "Robust Optimal Control With Binary Adjustable Uncertainties"
CPAIOR 2024, "Improving Metaheuristic Efficiency for Stochastic Optimization Problems by Sequential Predictive Sampling"
Wytze Elhorst, MSc, graduated on "Exploring the Multi-Objective Dial-A-Ride Problem: An Analysis of Genetic Algorithms and MIP"
International Shipbuilding Progress, "Multi-Fidelity Kriging Extrapolation Together with CFD for the Design of the Cross-Section of a Falling Lifeboat" (pdf)