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.
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.
Efficient offshore wind turbines
Optimisation
Uncertainty
Sustainable terminal services
Optimisation
Uncertainty
Simulation
Data-driven building optimisation
Uncertainty
Optimisation
International Journal of Electrical Power and Energy Systems, "Exposing a Locational Energy Market to Uncertainty"
Carlos March Moya, PortCall.Zero project
Ngân Hà Dương, Hermes project
STAR Lab director joins the editorial board of ACM AI Letters
"Priors Matter: Addressing Misspecification in Bayesian Deep Q-Learning" (arxiv)
Daniël Hogendoorn, MSc, graduated on "Predicting Data Quality of Event-Based Container Trackers"
Mozafar Shah, MSc, graduated on "Deep Reinforcement Learning for Multi-Objective Airport Ground Handling"
STAR Lab director will speak at the EURO PhD School Symposium "AI Meets Optimization"
Max Le Blansch, MSc, graduated on "Generating Evenly Distributed Near-Optimal Investment Alternatives for Large-Scale Power Systems using Genetic Algorithms"
"Modelling Program Spaces in Program Synthesis with Constraints" (arxiv)
ICCL 2025, "An Incentive-based Coordination Approach for Decentralized Synchromodal Transport Platforms"
Michall Hu, MSc, graduated on "A Cost-Driven Framework for Optimizing Columnar Database"
Rixt Hellinga, MSc, graduated on "Modelling Sharing Economies"
Postdoc position "Applied Planning and Scheduling under Uncertainty"
Steffano Psathas, MSc, graduated on "A Proactive Approach to the Multi-Skill Multi-Mode Resource-Constrained Project Scheduling Problem with Uncertainty"
PhD position "Contextual Optimisation and Reinforcement Learning for Sustainability"
European Journal of Control, "Model Predictive Building Climate Control for Mitigating Heat Pump Noise Pollution"
Venelina Pocheva, MSc, graduated on "Enhancing Issue Tracking Efficiency with AI-Driven Natural Language Processing"
Roy Katz, MSc, graduated on "Adding Ejection Chain to Nurse Rostering Simulated Annealing Solver"
Matthijs de Goede, MSc graduated cum laude on "Robust Optimization of Heavy Goods Electric Vehicle Fleet Planning"
Andrei Mereuta, MSc graduated on "Multi-Meal, Multi-Constraint Recommender System to Optimize Grocery Budget and Waste"
Bart Lagae, MSc, graduated on "Building the Charging Demand Curve at a Heavy Duty Electric Vehicle Charging Station"
"Epistemic Artificial Intelligence is Essential for Machine Learning Models to Truly 'Know When They Do Not Know'" (arxiv)
AIChE 2025, "Optimizing over Trained Transformer Attention Mechanisms: A Mixed-Integer Nonlinear Programming Formulation"
