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.
Data-driven building optimisation
Uncertainty
Optimisation
Tim Huisman, MSc, graduated on "Algorithmic Solutions for Improved Carrier-Shipper Matching in a Competitive Transport Marketplace"
Luca Cras, MSc, graduated on "Evaluating the Impact and Opportunities of Physics-Informed Machine Learning on the Task of Greenhouse Humidity Prediction"
INFORMS Journal on Computing, "Multi-Objective Linear Ensembles for Robust and Sparse Training of Few-Bit Neural Networks"
Isa Rethans, MSc, graduated on "Predictive Analysis and Key Drivers for PostNL's Cost Per Package"
Joost Commandeur, Shell e-Mobility
TRC-30 symposium, "Psychological Factors in Travel Behaviour Interpretation with Social Media Data"
DSO 2024 workshop at IJCAI'24, "Neural Decision Diagrams for Job Shop Scheduling"
STAR Lab director will speak at TILTing Perspectives 2024 panel on "Values for an Energy Sector in Transition"
Yoshi van den Akker, MSc, graduated on "Creating New Train Timetables in Case of Disruptions"
Sven van der Voort, MSc, graduated on "Sketch-Based Optimisation for Distribution Grid Expansion Planning"
hEART 2024, "Exploring the Impact of Deceleration Rates on Traffic Incident Probability: A Case Study of Motorways in the Netherlands"
IJCAI 2024, "Robust Losses for Decision-Focused Learning" (pdf)
Real Estate, "An Agent-Based Market Analysis of Urban Housing Balance in the Netherlands" (pdf)
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"
STAR Lab director elected to IFAAMAS Board of Directors
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 (pdf)
e-Energy 2024, "Incentives for Accurate Energy Predictions: How to Reduce Epistemic Uncertainty" (pdf)
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" (pdf)
