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
STAR Lab director will speak at the CRM Workshop on "Combinatorial optimization and data science"
STAR Lab director will speak at the 9th AIROYoung Workshop
Journal of Environmental Management, "How Ex Ante Policy Evaluation Supports Circular City Development: Amsterdam's mass timber construction policy"
Dr Lara Scavuzzo graduated cum laude on "Towards Smarter MILP Solvers: A data-driven approach to branch-and-bound"
Journal of Air Transport Management, "An Aircraft and Schedule Integrated Approach to Crew Scheduling for a Point-to-Point Airline"
Wouter Looijenga, MSc, graduated on "Predictive Modelling for Aviation Resource Allocation: Enhancing Reserve Crew Forecasting"
STAR Lab director will speak at AI and Mobility Day, Mondai House of AI
Sian Hallsworth, MSc, graduated on "Mixed-Integer Non-linear Formulation for Optimisation over Trained Transformer Models"
AAAI 2025, "Epistemic Bellman Operators" (EWRL'24 pdf)
Journal of Process Control, "Data-Driven Robust Optimization with Machine Learning Enabled Uncertainty Set" (pdf)
BNAIC 2024, "An Efficient Decremental Algorithm for Simple Temporal Networks" (pdf)
Laurens Krudde, MSc, graduated on "Demand Responsive Transport to Replace a Fixed-Line Bus Service"