Complex challenging decisions: delivering to customers while balancing timeliness and environmental cost; regulating peer-to-peer markets while fostering infrastructure investment; transitioning to electric mobility while ensuring fairness in the uncertain future. The STAR Lab team researches how technology – information and computation – can help people make better decisions in these kinds of complex situations.

Research

The TU Delft STAR Lab focuses on individuals and groups who face many options or complicated implications. We research how bringing together data and models, peoples' preferences, and AI reasoning can facilitate outcomes better for society. We make impact through partnering with companies, universities, municipalities, and government departments.

News

Master's graduate

Marc Droogh, MSc, graduated on "Optimising Topographical Pillar Placement for Thermal Heat Distribution with Artificial Intelligence"

Master's graduate

Daniƫl van Gelder, MSc, graduated on "Real-Time Passenger Load Estimation using In-Vehicle Data"

Accepted paper

Engineering Structures, "Long-Term Viscoelastic Deformation Monitoring of Concrete Dams: A Multi-Output Surrogate Model Approach for Parameter Identification"

Summer school

STAR Lab director will speak at EurAI ACAI 2022 / TAILOR summer school

Doctoral graduate

Dr Longjian Piao graduated on "Electricity Markets for Direct Current Distribution Systems"

Accepted paper

SPARK'22 @ ICAPS 2022, "Predictive Maintenance Scheduling in Twice Re-entrant Flow Shops with Due Dates"

Master's graduate

Dirk van Bokkem, MSc, graduated on "Economic Greenhouse Decision Support"

New STAR Lab doctoral student

Yanyan Xu, TULIPS project

Conference

STAR Lab director will serve as Publications Co-chair for AAMAS 2023

Accepted abstract

ICAPS 2022, "Optimisation of Annual Planned Rail Maintenance" (journal abstract) (journal pdf)

Master's graduate

Thomas Barendse, MSc, graduated on "Truck Routing for an Online Grocer"

Accepted paper

ABMUS'22 @ AAMAS 2022, "No Hope for First-Time Buyers? Towards Agent-Based Market Analysis of Urban Housing Balance" (pdf)

Accepted paper

ESICUP 2022, "A Machine Learning Approach for 3D Load Feasibility Prediction"

New STAR Lab postdoc

Dr Alexandru Babeanu, DCSE / TAILOR project

Accepted paper

TRISTAN XI 2022, "A Data-driven Time-Dependent Routing and Scheduling for Activity-Based Freight Transport Modelling"

Workshop

STAR Lab director will co-chair Data Science Meets Optimization workshop at IJCAI'22

Accepted demonstration

ICT.OPEN 2022, "Stein Variational Gradient Descent for Deep Ensembles"

Accepted poster

ICT.OPEN 2022, "Predictive Maintenance Scheduling in Twice Re-entrant Flow Shops with Due Dates"

Accepted paper

ICAPS 2022, "Talking Trucks: Decentralized Collaborative Multi-Agent Order Scheduling for Self-Organizing Logistics"

Master's graduate

Stefan de Vringer, MSc, graduated on "Offline-Online Reoptimization using a Hybrid Method"

Accepted paper

Urban Planning, "Market-Led Urbanism and Geographic Crises: A Micro-Simulation Lens on Beirut" (pdf)

New STAR Lab doctoral student

Yun Li, Brains for Buildings project

New STAR Lab postdoc

Dr Matthias Horn, Voestalpine project

Projects

TULIPS logo
TULIPS
EU Green Deal (2022-26)

Sustainable landside transport

Optimisation

Simulation


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

Data-driven building optimisation

Optimisation

Uncertainty


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Voestalpine logo
Voestalpine
Industrial (2021-22)

Job planning and sequencing

Optimisation


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E-pi logo
Epistemic AI
Horizon 2020 (2021-25)

Redefining the basis for AI

Uncertainty


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OPTIMAL
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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SAM-FMS
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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