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.

Research

The TU Delft STAR Lab confronts a fundamental question of the AI era: how can machine intelligence assist individuals and groups who face complex decision trade-offs? We research how bringing together data and models, human preferences, and AI reasoning can facilitate outcomes better for society. We make impact through partnering with companies, universities, municipalities and government departments.

Projects

AIM-TT
AIM-TT
AiNed (2025-28)

AI & mobility learning community

Optimisation

Simulation


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EFFILOAT
EFFILOAT
RVO (2025-27)

efficient offshore wind turbines

Optimisation

Uncertainty


PortCall.Zero
PortCall.Zero
NWO (2025-30)

Sustainable terminal services

Optimisation

Uncertainty

Simulation


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FMaaS
FMaaS
NWO (2024-29)

Freight Management as a Service

Optimisation

Uncertainty


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HaPSISH
AIFES
AiNed (2023)

AI for the future energy system

Optimisation

Uncertainty


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Plug-In
Plug-In
Dutch Ministry EZK (2022-26)

HGV energy hubs

Optimisation

Uncertainty


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TULIPS logo
TULIPS
EU Green Deal (2022-26)

Sustainable landside transport

Optimisation

Simulation


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E-pi logo
Epistemic AI
Horizon 2020 (2021-26)

Redefining the basis for AI

Uncertainty


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B4B logo
Brains for Buildings
Dutch Ministry EZK (2021-25)

Data-driven building optimisation

Uncertainty

Optimisation


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Voestalpine logo
Voestalpine
Industrial (2021-22)

Job planning and sequencing

Optimisation


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HaPSISH
OPTIMAL
NWO (2020-25)

Machine learning for optimisation

Optimisation


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TAILOR logo
TAILOR
EU Horizon 2020 (2020-24)

Trustworthy AI

Simulation

Optimisation


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HaPSISH
SAM-FMS
NWO (2020-24)

Scheduling cyber-physical systems

Optimisation


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HaPSISH
HaPSISH
NWO (2016-20)

Energy system integration

Optimisation

Uncertainty


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DCSMART
DCSMART
EU Horizon 2020 (2016-19)

Energy system integration

Simulation

Uncertainty


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Recent News

Invited speaker

STAR Lab director will speak at the CRM Workshop on "Combinatorial optimization and data science"

Invited speaker

STAR Lab director will speak at the 9th AIROYoung Workshop

Accepted paper

Journal of Environmental Management, "How Ex Ante Policy Evaluation Supports Circular City Development: Amsterdam's mass timber construction policy"

Doctoral graduate

Dr Lara Scavuzzo graduated cum laude on "Towards Smarter MILP Solvers: A data-driven approach to branch-and-bound"

Accepted paper

Journal of Air Transport Management, "An Aircraft and Schedule Integrated Approach to Crew Scheduling for a Point-to-Point Airline"

Master's graduate

Wouter Looijenga, MSc, graduated on "Predictive Modelling for Aviation Resource Allocation: Enhancing Reserve Crew Forecasting"

Workshop speaker

STAR Lab director will speak at AI and Mobility Day, Mondai House of AI

Master's graduate

Sian Hallsworth, MSc, graduated on "Mixed-Integer Non-linear Formulation for Optimisation over Trained Transformer Models"

Accepted paper

AAAI 2025, "Epistemic Bellman Operators" (EWRL'24 pdf)

Accepted paper

Journal of Process Control, "Data-Driven Robust Optimization with Machine Learning Enabled Uncertainty Set" (pdf)

Accepted paper

BNAIC 2024, "An Efficient Decremental Algorithm for Simple Temporal Networks" (pdf)

Master's graduate

Laurens Krudde, MSc, graduated on "Demand Responsive Transport to Replace a Fixed-Line Bus Service"