Complex challenging decisions: the STAR Lab team explores trustworthy fundamental AI to help people make better decisions in situations from sustainable logistics, to robust investment planning, to fair energy markets.
Data-driven building optimisation
Uncertainty
Optimisation
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"
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)
AAMAS 2024, "Bayesian Ensembles for Exploration in Deep Q-Learning"
Marvin Kleijweg, MSc, graduated on "Encouraging Circular Wood-Based Building Practices in Amsterdam"
PhD position "Reinforcement learning for sustainable logistics"
Lucas Veeger, MSc, graduated on "CCS Reservoir Simulation using Graph Neural Networks"
Transportation Research Board Part C, "A Data-driven Time-Dependent Routing and Scheduling for Activity-Based Freight Transport Modelling" (pdf)
Katja Schmahl, MSc, graduated on "Railway Maintenance Scheduling"
NEXT'23, "RSimGNN for long-term CO2 saturation predictions for CCS Reservoir Simulation"
Mahsa Movaghar joins the STAR Lab