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Niki van Stein
Associate Professor in Explainable AI for ML and Optimization
- Institute
- LIACS
- Based in
- Leiden
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A list of all the posts and pages found on the site. For you robots out there is an XML version available for digesting as well.
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Niki van Stein
Associate Professor in Explainable AI for ML and Optimization at LIACS, Leiden University.
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Pink Phoenix
RPG books and adventures.
XAI group
The Explainable AI research group at LIACS, Leiden University: making complex systems understandable enough to trust.
Posts
Our new book: Agentic Large Language Models
Published:
After a year and a half of writing, our textbook on agentic LLMs is out as a preprint, with a Springer edition and a new master course to go with it.
From configurations to code: evolving AutoML pipelines with LLaMEA
Published:
LACE lets a language model evolve complete, readable machine learning pipelines, the topic of my keynote at the AutoML Conference 2026.
Looking inside the model: mechanistic interpretability, and why LLaMEA needs it
Published:
A plain-language tour of our paper “Every Component Is a Lookup”, and why understanding how LLMs make design decisions matters for evolving code.
LLaMEA: Combining the Power of Evolution and Large Language Models
Published:
LLaMEA, a pioneering framework combining Evolution and Large Language Models.
Reflecting on CIMPLO
Published:
A Journey Through Predictive Maintenance
Structural Bias, What is it and why do we care?
Published:
An introduction to Structural Bias in search heuristics.
Gecco 2023 - DoE2Vec
Published:
Gecco 2023
Gecco 2023 - Deep Bias
Published:
Gecco 2023
projects
CIMPLO
Cross-Industry Predictive Maintenance Optimization Platform
Complex Lens Design
Optimization of Complex Lens Designs
XAIPre
eXplainable AI for Predictive Maintenance
AI for Oversight
AI for Oversight ICAI lab
LLaMEA
Large Language Evolutionary Algorithm for the automatic design of algorithms.
AI4optics
AI-based simulation, design and reconstruction for illumination and imaging optics.
FIND
Large AI models for a resilient high-tech industry.
GenAIDE
Exploring disruptive AI technologies and evolving computer-aided engineering.
publications
Fitness landscape analysis of nk landscapes and vehicle routing problems by expanded barrier trees
Published in EVOLVE-A Bridge between Probability, Set Oriented Numerics, and Evolutionary Computation IV: International Conference held at Leiden University, July 10-13, 2013, 2013
Recommended citation: Niki van Stein, Michael Emmerich, Zhiwei Yang, "Fitness landscape analysis of nk landscapes and vehicle routing problems by expanded barrier trees." EVOLVE-A Bridge between Probability, Set Oriented Numerics, and Evolutionary Computation IV: International Conference held at Leiden University, July 10-13, 2013, 2013. https://doi.org/10.1007/978-3-319-01128-8_6
Optimally weighted cluster kriging for big data regression
Published in Advances in Intelligent Data Analysis XIV: 14th International Symposium, IDA 2015, Saint Etienne. France, October 22-24, 2015. Proceedings, 2015
Recommended citation: Niki van Stein, Hao Wang, Wojtek Kowalczyk, Thomas Bäck, Michael Emmerich, "Optimally weighted cluster kriging for big data regression." Advances in Intelligent Data Analysis XIV: 14th International Symposium, IDA 2015, Saint Etienne. France, October 22-24, 2015. Proceedings, 2015. https://doi.org/10.1007/978-3-319-24465-5_27
An incremental algorithm for repairing training sets with missing values
Published in Information Processing and Management of Uncertainty in Knowledge-Based Systems: 16th International Conference, IPMU 2016, Eindhoven, The Netherlands, June 20-24, 2016, Proceedings, Part II 16, 2016
Recommended citation: Niki van Stein, Wojtek Kowalczyk, "An incremental algorithm for repairing training sets with missing values." Information Processing and Management of Uncertainty in Knowledge-Based Systems: 16th International Conference, IPMU 2016, Eindhoven, The Netherlands, June 20-24, 2016, Proceedings, Part II 16, 2016. https://doi.org/10.1007/978-3-319-40581-0_15
Analysis and visualization of missing value patterns
Published in Information Processing and Management of Uncertainty in Knowledge-Based Systems: 16th International Conference, IPMU 2016, Eindhoven, The Netherlands, June 20-24, 2016, Proceedings, Part II 16, 2016
Recommended citation: Niki van Stein, Wojtek Kowalczyk, Thomas Bäck, "Analysis and visualization of missing value patterns." Information Processing and Management of Uncertainty in Knowledge-Based Systems: 16th International Conference, IPMU 2016, Eindhoven, The Netherlands, June 20-24, 2016, Proceedings, Part II 16, 2016. https://doi.org/10.1007/978-3-319-40581-0_16
Fuzzy clustering for optimally weighted cluster kriging
Published in 2016 IEEE international conference on fuzzy systems (FUZZ-IEEE), 2016
Recommended citation: Niki van Stein, Hao Wang, Wojtek Kowalczyk, Michael Emmerich, Thomas Bäck, "Fuzzy clustering for optimally weighted cluster kriging." 2016 IEEE international conference on fuzzy systems (FUZZ-IEEE), 2016. https://doi.org/10.1109/fuzz-ieee.2016.7737789
A framework for evaluating meta-models for simulation-based optimisation
Published in 2016 IEEE Symposium Series on Computational Intelligence (SSCI), 2016
Recommended citation: Pepijn van Heiningen, Niki van Stein, Thomas Bäck, "A framework for evaluating meta-models for simulation-based optimisation." 2016 IEEE Symposium Series on Computational Intelligence (SSCI), 2016. https://doi.org/10.1109/ssci.2016.7850207
Local subspace-based outlier detection using global neighbourhoods
Published in 2016 IEEE International Conference on Big Data (Big Data), 2016
Recommended citation: Niki van Stein, Matthijs van Leeuwen, Thomas Bäck, "Local subspace-based outlier detection using global neighbourhoods." 2016 IEEE International Conference on Big Data (Big Data), 2016. https://doi.org/10.1109/bigdata.2016.7840717
Towards data driven process control in manufacturing car body parts
Published in 2016 International Conference on Computational Science and Computational Intelligence (CSCI), 2016
Recommended citation: Niki van Stein, Matthijs Van Leeuwen, Hao Wang, Stephan Purr, Sebastian Kreissl, Josef Meinhardt, Thomas Bäck, "Towards data driven process control in manufacturing car body parts." 2016 International Conference on Computational Science and Computational Intelligence (CSCI), 2016. https://doi.org/10.1109/csci.2016.0093
Algorithm configuration data mining for cma evolution strategies
Published in Proceedings of the Genetic and Evolutionary Computation Conference, 2017
Recommended citation: Sander van Rijn, Hao Wang, Niki van Stein, Thomas Bäck, "Algorithm configuration data mining for cma evolution strategies." Proceedings of the Genetic and Evolutionary Computation Conference, 2017. https://doi.org/10.1145/3071178.3071205
Time complexity reduction in efficient global optimization using cluster kriging
Published in Proceedings of the Genetic and Evolutionary Computation Conference, 2017
Recommended citation: Hao Wang, Niki van Stein, Michael Emmerich, Thomas Bäck, "Time complexity reduction in efficient global optimization using cluster kriging." Proceedings of the Genetic and Evolutionary Computation Conference, 2017. https://doi.org/10.1145/3071178.3071321
A multi-method simulation of a high-frequency bus line
Published in 2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITSC), 2017
Recommended citation: Thierry van der Spek, Niki van Stein, Marcel van der Holst, Thomas Bäck, "A multi-method simulation of a high-frequency bus line." 2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITSC), 2017. https://doi.org/10.1109/itsc.2017.8317770
A new acquisition function for Bayesian optimization based on the moment-generating function
Published in 2017 IEEE International Conference on Systems, Man, and Cybernetics (SMC), 2017
Recommended citation: Hao Wang, Niki van Stein, Michael Emmerich, Thomas Bäck, "A new acquisition function for Bayesian optimization based on the moment-generating function." 2017 IEEE International Conference on Systems, Man, and Cybernetics (SMC), 2017. https://doi.org/10.1109/smc.2017.8122656
A novel uncertainty quantification method for efficient global optimization
Published in Information Processing and Management of Uncertainty in Knowledge-Based Systems. Applications: 17th International Conference, IPMU 2018, C'adiz, Spain, June 11-15, 2018, Proceedings, Part III 17, 2018
Recommended citation: Niki van Stein, Hao Wang, Wojtek Kowalczyk, Thomas Bäck, "A novel uncertainty quantification method for efficient global optimization." Information Processing and Management of Uncertainty in Knowledge-Based Systems. Applications: 17th International Conference, IPMU 2018, C'adiz, Spain, June 11-15, 2018, Proceedings, Part III 17, 2018. https://doi.org/10.1007/978-3-319-91479-4_40
Designing ships using constrained multi-objective efficient global optimization
Published in Machine Learning, Optimization, and Data Science: 4th International Conference, LOD 2018, Volterra, Italy, September 13-16, 2018, Revised Selected Papers 4, 2019
Recommended citation: Roy de Winter, Niki van Stein, Matthys Dijkman, Thomas Bäck, "Designing ships using constrained multi-objective efficient global optimization." Machine Learning, Optimization, and Data Science: 4th International Conference, LOD 2018, Volterra, Italy, September 13-16, 2018, Revised Selected Papers 4, 2019. https://doi.org/10.1007/978-3-030-13709-0_16
Automatic configuration of deep neural networks with parallel efficient global optimization
Published in 2019 International Joint Conference on Neural Networks (IJCNN), 2019
Recommended citation: Niki van Stein, Hao Wang, Thomas Bäck, "Automatic configuration of deep neural networks with parallel efficient global optimization." 2019 International Joint Conference on Neural Networks (IJCNN), 2019. https://doi.org/10.1109/ijcnn.2019.8851720
Cluster-based Kriging approximation algorithms for complexity reduction
Published in Applied Intelligence, 2019
Recommended citation: Niki van Stein, Hao Wang, Wojtek Kowalczyk, Michael Emmerich, Thomas Bäck, "Cluster-based Kriging approximation algorithms for complexity reduction." Applied Intelligence, 2019. https://doi.org/10.1007/s10489-019-01549-7
A new approach towards the combined algorithm selection and hyper-parameter optimization problem
Published in 2019 IEEE Symposium Series on Computational Intelligence (SSCI), 2019
Recommended citation: Xin Guo, Niki van Stein, Thomas Bäck, "A new approach towards the combined algorithm selection and hyper-parameter optimization problem." 2019 IEEE Symposium Series on Computational Intelligence (SSCI), 2019. https://doi.org/10.1109/ssci44817.2019.9003174
On the efficiency of a point cloud autoencoder as a geometric representation for shape optimization
Published in 2019 IEEE Symposium Series on Computational Intelligence (SSCI), 2019
Recommended citation: Thiago Rios, Bernhard Sendhoff, Stefan Menzel, Thomas Bäck, Niki van Stein, "On the efficiency of a point cloud autoencoder as a geometric representation for shape optimization." 2019 IEEE Symposium Series on Computational Intelligence (SSCI), 2019. https://doi.org/10.1109/ssci44817.2019.9003161
Scalability of learning tasks on 3D CAE models using point cloud autoencoders
Published in 2019 IEEE Symposium Series on Computational Intelligence (SSCI), 2019
Recommended citation: Thiago Rios, Patricia Wollstadt, Niki van Stein, Thomas Bäck, Zhao Xu, Bernhard Sendhoff, Stefan Menzel, "Scalability of learning tasks on 3D CAE models using point cloud autoencoders." 2019 IEEE Symposium Series on Computational Intelligence (SSCI), 2019. https://doi.org/10.1109/ssci44817.2019.9002982
Feature visualization for 3D point cloud autoencoders
Published in 2020 International Joint Conference on Neural Networks (IJCNN), 2020
Recommended citation: Thiago Rios, Niki van Stein, Stefan Menzel, Thomas Bäck, Bernhard Sendhoff, Patricia Wollstadt, "Feature visualization for 3D point cloud autoencoders." 2020 International Joint Conference on Neural Networks (IJCNN), 2020. https://doi.org/10.1109/ijcnn48605.2020.9207326
Improving NSGA-III for flexible job shop scheduling using automatic configuration, smart initialization and local search
Published in Proceedings of the 2020 Genetic and Evolutionary Computation Conference Companion, 2020
Recommended citation: Yali Wang, Niki van Stein, Thomas Bäck, Michael Emmerich, "Improving NSGA-III for flexible job shop scheduling using automatic configuration, smart initialization and local search." Proceedings of the 2020 Genetic and Evolutionary Computation Conference Companion, 2020. https://doi.org/10.1145/3377929.3389924
A tailored NSGA-III for multi-objective flexible job shop scheduling
Published in 2020 IEEE Symposium Series on Computational Intelligence (SSCI), 2020
Recommended citation: Yali Wang, Niki van Stein, Thomas Bäck, Michael Emmerich, "A tailored NSGA-III for multi-objective flexible job shop scheduling." 2020 IEEE Symposium Series on Computational Intelligence (SSCI), 2020. https://doi.org/10.1109/ssci47803.2020.9308373
Back to meshes: Optimal simulation-ready mesh prototypes for autoencoder-based 3D car point clouds
Published in 2020 IEEE Symposium Series on Computational Intelligence (SSCI), 2020
Recommended citation: Thiago Rios, Jiawen Kong, Niki van Stein, Thomas Bäck, Patricia Wollstadt, Bernhard Sendhoff, Stefan Menzel, "Back to meshes: Optimal simulation-ready mesh prototypes for autoencoder-based 3D car point clouds." 2020 IEEE Symposium Series on Computational Intelligence (SSCI), 2020. https://doi.org/10.1109/ssci47803.2020.9308400
Neural network design: learning from neural architecture search
Published in 2020 IEEE Symposium Series on Computational Intelligence (SSCI), 2020
Recommended citation: Niki van Stein, Hao Wang, Thomas Bäck, "Neural network design: learning from neural architecture search." 2020 IEEE Symposium Series on Computational Intelligence (SSCI), 2020. https://doi.org/10.1109/ssci47803.2020.9308394
Ship design performance and cost optimization with machine learning
Published in COMPIT'21, 2021
Recommended citation: R Winter, Niki van Stein, THW Bäck, V Bertram, "Ship design performance and cost optimization with machine learning." COMPIT'21, 2021.
Samo-cobra: A fast surrogate assisted constrained multi-objective optimization algorithm
Published in Evolutionary Multi-Criterion Optimization: 11th International Conference, EMO 2021, Shenzhen, China, March 28--31, 2021, Proceedings 11, 2021
Recommended citation: Roy de Winter, Niki van Stein, Thomas Bäck, "Samo-cobra: A fast surrogate assisted constrained multi-objective optimization algorithm." Evolutionary Multi-Criterion Optimization: 11th International Conference, EMO 2021, Shenzhen, China, March 28--31, 2021, Proceedings 11, 2021. https://doi.org/10.1007/978-3-030-72062-9_22
Requirements towards optimizing analytics in industrial processes
Published in Procedia Computer Science, 2021
Recommended citation: Alexander Zeiser, Niki van Stein, Thomas Bäck, "Requirements towards optimizing analytics in industrial processes." Procedia Computer Science, 2021. https://doi.org/10.1016/j.procs.2021.03.074
Multitask Shape Optimization Using a 3-D Point Cloud Autoencoder as Unified Representation
Published in IEEE Transactions on Evolutionary Computation, 2021
Recommended citation: Thiago Rios, Niki van Stein, Thomas Bäck, Bernhard Sendhoff, Stefan Menzel, "Multitask Shape Optimization Using a 3-D Point Cloud Autoencoder as Unified Representation." IEEE Transactions on Evolutionary Computation, 2021. https://doi.org/10.1109/tevc.2021.3086308
Exploiting local geometric features in vehicle design optimization with 3D point cloud autoencoders
Published in 2021 IEEE Congress on Evolutionary Computation (CEC), 2021
Recommended citation: Thiago Rios, Niki van Stein, Patricia Wollstadt, Thomas Bäck, Bernhard Sendhoff, Stefan Menzel, "Exploiting local geometric features in vehicle design optimization with 3D point cloud autoencoders." 2021 IEEE Congress on Evolutionary Computation (CEC), 2021. https://doi.org/10.1109/cec45853.2021.9504746
Emergence of structural bias in differential evolution
Published in Proceedings of the Genetic and Evolutionary Computation Conference Companion, 2021
Recommended citation: Niki van Stein, Fabio Caraffini, Anna V. Kononova, "Emergence of structural bias in differential evolution." Proceedings of the Genetic and Evolutionary Computation Conference Companion, 2021. https://doi.org/10.1145/3449726.3463223
Point2FFD: learning shape representations of simulation-ready 3D models for engineering design optimization
Published in 2021 International Conference on 3D Vision (3DV), 2021
Recommended citation: Thiago Rios, Niki van Stein, Thomas Bäck, Bernhard Sendhoff, Stefan Menzel, "Point2FFD: learning shape representations of simulation-ready 3D models for engineering design optimization." 2021 International Conference on 3D Vision (3DV), 2021. https://doi.org/10.1109/3dv53792.2021.00110
Exploiting generative models for performance predictions of 3D car designs
Published in 2021 IEEE Symposium Series on Computational Intelligence (SSCI), 2021
Recommended citation: Sneha Saha, Thiago Rios, Leandro L. Minku, Niki van Stein, Patricia Wollstadt, Xin Yao, Thomas Bäck, Bernhard Sendhoff, Stefan Menzel, "Exploiting generative models for performance predictions of 3D car designs." 2021 IEEE Symposium Series on Computational Intelligence (SSCI), 2021. https://doi.org/10.1109/ssci50451.2021.9660034
Using Machine Learning to Detect Rotational Symmetries from Reflectional Symmetries in 2D Images
Published in 2021 IEEE Symposium Series on Computational Intelligence (SSCI), 2021
Recommended citation: Koen Ponse, Anna V. Kononova, Maria Loleyt, Niki van Stein, "Using Machine Learning to Detect Rotational Symmetries from Reflectional Symmetries in 2D Images." 2021 IEEE Symposium Series on Computational Intelligence (SSCI), 2021. https://doi.org/10.1109/ssci50451.2021.9660120
Analysis of structural bias in differential evolution configurations
Published in Differential Evolution: From Theory to Practice, 2022
Recommended citation: Diederick Vermetten, Niki van Stein, Anna V. Kononova, Fabio Caraffini, "Analysis of structural bias in differential evolution configurations." Differential Evolution: From Theory to Practice, 2022. https://doi.org/10.1007/978-981-16-8082-3_1
Optimally weighted ensembles for efficient multi-objective optimization
Published in International Conference on Machine Learning, Optimization, and Data Science. Springer, 2022
Recommended citation: Gideon Hanse, Roy de Winter, Niki van Stein, Thomas Bäck, "Optimally weighted ensembles for efficient multi-objective optimization." International Conference on Machine Learning, Optimization, and Data Science. Springer, 2022. https://doi.org/10.1007/978-3-030-95467-3_12
Constrained multi-objective optimization with a limited budget of function evaluations
Published in Memetic Computing, 2022
Recommended citation: Roy de Winter, Philip Bronkhorst, Niki van Stein, Thomas Bäck, "Constrained multi-objective optimization with a limited budget of function evaluations." Memetic Computing, 2022. https://doi.org/10.1007/s12293-022-00363-y
Explainable artificial intelligence for exhaust gas temperature of turbofan engines
Published in Journal of Aerospace Information Systems, 2022
Recommended citation: Marios Kefalas, Juan de Santiago Rojo, Asteris Apostolidis, Dirk van den Herik, Niki van Stein, Thomas Bäck, "Explainable artificial intelligence for exhaust gas temperature of turbofan engines." Journal of Aerospace Information Systems, 2022. https://doi.org/10.2514/1.i011058
An end-to-end pipeline for uncertainty quantification and remaining useful life estimation: An application on aircraft engines
Published in PHM Society European Conference, 2022
Recommended citation: Marios Kefalas, Niki van Stein, Mitra Baratchi, Asteris Apostolidis, Thomas Bäck, "An end-to-end pipeline for uncertainty quantification and remaining useful life estimation: An application on aircraft engines." PHM Society European Conference, 2022. https://doi.org/10.36001/phme.2022.v7i1.3317
Learning the characteristics of engineering optimization problems with applications in automotive crash
Published in Proceedings of the Genetic and Evolutionary Computation Conference, 2022
Recommended citation: Fu Xing Long, Niki van Stein, Moritz Frenzel, Peter Krause, Markus Gitterle, Thomas Bäck, "Learning the characteristics of engineering optimization problems with applications in automotive crash." Proceedings of the Genetic and Evolutionary Computation Conference, 2022. https://doi.org/10.1145/3512290.3528712
Multi-point acquisition function for constraint parallel efficient multi-objective optimization
Published in Proceedings of the Genetic and Evolutionary Computation Conference, 2022
Recommended citation: Roy de Winter, Niki van Stein, Thomas Bäck, "Multi-point acquisition function for constraint parallel efficient multi-objective optimization." Proceedings of the Genetic and Evolutionary Computation Conference, 2022. https://doi.org/10.1145/3512290.3528696
Using structural bias to analyse the behaviour of modular CMA-ES
Published in Proceedings of the Genetic and Evolutionary Computation Conference Companion, 2022
Recommended citation: Diederick Vermetten, Fabio Caraffini, Niki van Stein, Anna V. Kononova, "Using structural bias to analyse the behaviour of modular CMA-ES." Proceedings of the Genetic and Evolutionary Computation Conference Companion, 2022. https://doi.org/10.1145/3520304.3534035
BIAS: a toolbox for benchmarking structural bias in the continuous domain
Published in IEEE Transactions on Evolutionary Computation, 2022
Recommended citation: Diederick Vermetten, Niki van Stein, Fabio Caraffini, Leandro L. Minku, Anna V. Kononova, "BIAS: a toolbox for benchmarking structural bias in the continuous domain." IEEE Transactions on Evolutionary Computation, 2022. https://doi.org/10.1109/tevc.2022.3189848
A comparison of global sensitivity analysis methods for explainable AI with an application in genomic prediction
Published in IEEE Access, 2022
Recommended citation: Niki van Stein, Elena Raponi, Zahra Sadeghi, Niek Bouman, Roeland C. H. J. Van Ham, Thomas Bäck, "A comparison of global sensitivity analysis methods for explainable AI with an application in genomic prediction." IEEE Access, 2022. https://doi.org/10.1109/access.2022.3210175
GSAreport: Easy to Use Global Sensitivity Reporting
Published in Journal of Open Source Software, 2022
Recommended citation: Niki van Stein, Elena Raponi, "GSAreport: Easy to Use Global Sensitivity Reporting." Journal of Open Source Software, 2022. https://doi.org/10.21105/joss.04721
Multi-surrogate Assisted Efficient Global Optimization for Discrete Problems
Published in Preprint on arXiv preprint arXiv:2212.06438, 2022
Recommended citation: Qi Huang, Roy De Winter, Niki van Stein, Thomas Bäck, Anna V. Kononova, "Multi-surrogate Assisted Efficient Global Optimization for Discrete Problems." Preprint on arXiv preprint arXiv:2212.06438, 2022. https://doi.org/10.1109/ssci51031.2022.10022132
A data-centric approach to anomaly detection in layer-based additive manufacturing
Published in at-Automatisierungstechnik, 2023
Recommended citation: Alexander Zeiser, Bekir Özcan, Christoph Kracke, Niki van Stein, Thomas Bäck, "A data-centric approach to anomaly detection in layer-based additive manufacturing." at-Automatisierungstechnik, 2023. https://doi.org/10.1515/auto-2022-0104
DoE2Vec: Deep-learning Based Features for Exploratory Landscape Analysis
Published in Preprint on arXiv preprint arXiv:2304.01219, 2023
Recommended citation: Niki van Stein, Fu Xing Long, Moritz Frenzel, Peter Krause, Markus Gitterle, Thomas Bäck, "DoE2Vec: Deep-learning Based Features for Exploratory Landscape Analysis." Preprint on arXiv preprint arXiv:2304.01219, 2023. https://doi.org/10.1145/3583133.3590609
Evaluation of deep unsupervised anomaly detection methods with a data-centric approach for on-line inspection
Published in Computers in Industry, 2023
Recommended citation: Alexander Zeiser, Bekir Özcan, Niki van Stein, Thomas Bäck, "Evaluation of deep unsupervised anomaly detection methods with a data-centric approach for on-line inspection." Computers in Industry, 2023. https://doi.org/10.1016/j.compind.2023.103852
Deep-BIAS: Detecting Structural Bias using Explainable AI
Published in Preprint on arXiv preprint arXiv:2304.01869, 2023
Recommended citation: Niki van Stein, Diederick Vermetten, Fabio Caraffini, Anna V. Kononova, "Deep-BIAS: Detecting Structural Bias using Explainable AI." Preprint on arXiv preprint arXiv:2304.01869, 2023. https://doi.org/10.1145/3583133.3590551
BBOB Instance Analysis: Landscape Properties and Algorithm Performance Across Problem Instances
Published in International Conference on the Applications of Evolutionary Computation (Part of EvoStar), 2023
Recommended citation: Fu Xing Long, Diederick Vermetten, Niki van Stein, Anna V. Kononova, "BBOB Instance Analysis: Landscape Properties and Algorithm Performance Across Problem Instances." International Conference on the Applications of Evolutionary Computation (Part of EvoStar), 2023. https://doi.org/10.1007/978-3-031-30229-9_25
Challenges of ELA-guided Function Evolution using Genetic Programming
Published in Preprint on arXiv preprint arXiv:2305.15245, 2023
Recommended citation: Fu Xing Long, Diederick Vermetten, Anna Kononova, Roman Kalkreuth, Kaifeng Yang, Thomas Bäck, Niki van Stein, "Challenges of ELA-guided Function Evolution using Genetic Programming." Preprint on arXiv preprint arXiv:2305.15245, 2023. https://doi.org/10.5220/0012206200003595
AI for Expensive Optimization Problems in Industry
Published in Conference on Algebraic Informatics, 2023
Recommended citation: Niki van Stein, Roy de Winter, Thomas Bäck, Anna V. Kononova, "AI for Expensive Optimization Problems in Industry." Conference on Algebraic Informatics, 2023. https://doi.org/10.1109/cai54212.2023.00113
Application of Functional Kernel Hypothesis Testing for Channel Selection in Time Series Classification
Published in Conference on Algebraic Informatics, 2023
Recommended citation: Qi Huang, Thomas Bäck, Niki van Stein, "Application of Functional Kernel Hypothesis Testing for Channel Selection in Time Series Classification." Conference on Algebraic Informatics, 2023. https://doi.org/10.1109/cai54212.2023.00111
Evolutionary Algorithms for Parameter Optimization—Thirty Years Later
Published in Evolutionary Computation, 2023
Recommended citation: Thomas Bäck, Anna V. Kononova, Niki van Stein, Hao Wang, Kirill A. Antonov, Roman T. Kalkreuth, Jacob de Nobel, Diederick Vermetten, Roy de Winter, Furong Ye, "Evolutionary Algorithms for Parameter Optimization—Thirty Years Later." Evolutionary Computation, 2023. https://doi.org/10.1162/evco_a_00325
Representation-agnostic distance-driven perturbation for optimizing ill-conditioned problems
Published in Preprint on arXiv preprint arXiv:2306.02985, 2023
Recommended citation: Kirill Antonov, Anna V. Kononova, Thomas Bäck, Niki van Stein, "Representation-agnostic distance-driven perturbation for optimizing ill-conditioned problems." Preprint on arXiv preprint arXiv:2306.02985, 2023. https://arxiv.org/abs/2306.02985
New solutions to Cooke triplet problem via analysis of attraction basins
Published in Digital Optical Technologies 2023, 2023
Recommended citation: Kirill Antonov, Tiago Botari, Teus Tukker, Thomas Bäck, Niki van Stein, Anna V. Kononova, "New solutions to Cooke triplet problem via analysis of attraction basins." Digital Optical Technologies 2023, 2023. https://doi.org/10.1117/12.2675836
Curing ill-Conditionality via Representation-Agnostic Distance-Driven Perturbations
Published in Proceedings of the 17th ACM/SIGEVO Conference on Foundations of Genetic Algorithms, 2023
Recommended citation: Kirill Antonov, Anna V. Kononova, Thomas Bäck, Niki van Stein, "Curing ill-Conditionality via Representation-Agnostic Distance-Driven Perturbations." Proceedings of the 17th ACM/SIGEVO Conference on Foundations of Genetic Algorithms, 2023. https://doi.org/10.1145/3594805.3607138
Clustering-based Domain-Incremental Learning
Published in IEEE/CVF International Conference on Computer Vision Workshops (ICCVW), 2023
Recommended citation: Christiaan Lamers, René Vidal, Nabil Belbachir, Niki van Stein, Thomas Bäck, Paris Giampouras, "Clustering-based Domain-Incremental Learning." IEEE/CVF International Conference on Computer Vision Workshops (ICCVW), 2023. https://doi.org/10.1109/iccvw60793.2023.00363
The Opaque Nature of Intelligence and the Pursuit of Explainable AI
Published in ECTA 2023, 2023
Recommended citation: Sarah Thomson, Niki van Stein, Daan van den Berg, Cees van Leeuwen, "The Opaque Nature of Intelligence and the Pursuit of Explainable AI." ECTA 2023, 2023. https://doi.org/10.5220/0012249500003595
Adaptive model pruning in federated learning through loss exploration
Published in 2nd Workshop on Advancing Neural Network Training: Computational Efficiency, Scalability, and Resource Optimization (WANT@ ICML 2024), 2024
Recommended citation: Christian Interno, Elena Raponi, Niki van Stein, Thomas Bäck, Markus Olhofer, Yaochu Jin, Barbara Hammer, "Adaptive model pruning in federated learning through loss exploration." 2nd Workshop on Advancing Neural Network Training: Computational Efficiency, Scalability, and Resource Optimization (WANT@ ICML 2024), 2024.
Shapelet-based Model-agnostic Counterfactual Local Explanations for Time Series Classification
Published in Preprint on arXiv preprint arXiv:2402.01343, 2024
Recommended citation: Qi Huang, Wei Chen, Thomas Bäck, Niki van Stein, "Shapelet-based Model-agnostic Counterfactual Local Explanations for Time Series Classification." Preprint on arXiv preprint arXiv:2402.01343, 2024. https://arxiv.org/abs/2402.01343
Parallel multi-objective optimization for expensive and inexpensive objectives and constraints
Published in Swarm and Evolutionary Computation, 2024
Recommended citation: Roy de Winter, Bas Milatz, Julian Blank, Niki van Stein, Thomas Bäck, Kalyanmoy Deb, "Parallel multi-objective optimization for expensive and inexpensive objectives and constraints." Swarm and Evolutionary Computation, 2024. https://doi.org/10.1016/j.swevo.2024.101508
Generating cheap representative functions for expensive automotive crashworthiness optimization
Published in ACM Transactions on Evolutionary Learning and Optimization, 2024
Recommended citation: Fu Xing Long, Niki van Stein, Moritz Frenzel, Peter Krause, Markus Gitterle, Thomas Bäck, "Generating cheap representative functions for expensive automotive crashworthiness optimization." ACM Transactions on Evolutionary Learning and Optimization, 2024. https://doi.org/10.1145/3646554
A Survey of Meta-features Used for Automated Selection of Algorithms for Black-box Single-objective Continuous Optimization
Published in arXiv preprint arXiv:2406.06629, 2024
Recommended citation: Gjorgjina Cenikj, Ana Nikolikj, Gašper Petelin, Niki van Stein, Carola Doerr, Tome Eftimov, "A Survey of Meta-features Used for Automated Selection of Algorithms for Black-box Single-objective Continuous Optimization." arXiv preprint arXiv:2406.06629, 2024. https://arxiv.org/abs/2406.06629
Quality-diversity driven robust evolutionary optimization of optical designs
Published in Computational Optics 2024, 2024
Recommended citation: Kirill Antonov, Teus Tukker, Tiago Botari, Thomas Bäck, Anna V. Kononova, Niki van Stein, "Quality-diversity driven robust evolutionary optimization of optical designs." Computational Optics 2024, 2024. https://doi.org/10.1117/12.3017498
Landscape analysis based vs. domain-specific optimization for engineering design applications: a clear case
Published in 2024 IEEE Conference on Artificial Intelligence (CAI), 2024
Recommended citation: Roy de Winter, Fu Xing Long, Andre Thomaser, Thomas Bäck, Niki van Stein, Anna V. Kononova, "Landscape analysis based vs. domain-specific optimization for engineering design applications: a clear case." 2024 IEEE Conference on Artificial Intelligence (CAI), 2024. https://doi.org/10.1109/cai59869.2024.00148
Beyond the veil of similarity: Quantifying semantic continuity in explainable AI
Published in World Conference on Explainable Artificial Intelligence, 2024
Recommended citation: Qi Huang, Emanuele Mezzi, Osman Mutlu, Miltiadis Kofinas, Vidya Prasad, Shadnan Azwad Khan, Elena Ranguelova, Niki van Stein, "Beyond the veil of similarity: Quantifying semantic continuity in explainable AI." World Conference on Explainable Artificial Intelligence, 2024. https://doi.org/10.1007/978-3-031-63787-2_16
A Corridor Model Evolutionary Algorithm for Fast Converging Green Vehicle Routing Problem
Published in Proceedings of the Genetic and Evolutionary Computation Conference Companion, 2024
Recommended citation: Ananta Shahane, Niki van Stein, Yingjie Fan, "A Corridor Model Evolutionary Algorithm for Fast Converging Green Vehicle Routing Problem." Proceedings of the Genetic and Evolutionary Computation Conference Companion, 2024. https://doi.org/10.1145/3638530.3654323
A Critical Analysis of Raven Roost Optimization
Published in Proceedings of the Genetic and Evolutionary Computation Conference Companion, 2024
Recommended citation: Martijn Halsema, Diederick Vermetten, Thomas Bäck, Niki van Stein, "A Critical Analysis of Raven Roost Optimization." Proceedings of the Genetic and Evolutionary Computation Conference Companion, 2024. https://doi.org/10.1145/3638530.3664124
A functional analysis approach to symbolic regression
Published in Proceedings of the Genetic and Evolutionary Computation Conference, 2024
Recommended citation: Kirill Antonov, Roman Kalkreuth, Kaifeng Yang, Thomas Bäck, Niki van Stein, Anna Kononova, "A functional analysis approach to symbolic regression." Proceedings of the Genetic and Evolutionary Computation Conference, 2024. https://doi.org/10.1145/3638529.3654079
Modular optimization framework for mixed expensive and inexpensive real-world problems
Published in Proceedings of the Genetic and Evolutionary Computation Conference Companion, 2024
Recommended citation: Roy de Winter, Thomas Bäck, Niki van Stein, "Modular optimization framework for mixed expensive and inexpensive real-world problems." Proceedings of the Genetic and Evolutionary Computation Conference Companion, 2024. https://doi.org/10.1145/3638530.3664133
Reasoning with large language models, a survey
Published in arXiv preprint arXiv:2407.11511, 2024
Recommended citation: Aske Plaat, Annie Wong, Suzan Verberne, Joost Broekens, Niki van Stein, Thomas Bäck, "Reasoning with large language models, a survey." arXiv preprint arXiv:2407.11511, 2024. https://arxiv.org/abs/2407.11511
A deep dive into effects of structural bias on cma-es performance along affine trajectories
Published in International Conference on Parallel Problem Solving from Nature, 2024
Recommended citation: Niki van Stein, Sarah L. Thomson, Anna V. Kononova, "A deep dive into effects of structural bias on cma-es performance along affine trajectories." International Conference on Parallel Problem Solving from Nature, 2024. https://doi.org/10.1007/978-3-031-70068-2_3
Landscape-aware automated algorithm configuration using multi-output mixed regression and classification
Published in International Conference on Parallel Problem Solving from Nature, 2024
Recommended citation: Fu Xing Long, Moritz Frenzel, Peter Krause, Markus Gitterle, Thomas Bäck, Niki van Stein, "Landscape-aware automated algorithm configuration using multi-output mixed regression and classification." International Conference on Parallel Problem Solving from Nature, 2024. https://doi.org/10.1007/978-3-031-70068-2_6
ACO-NSGAII: A Novel Metaheuristics for Bi-Objective Electric Vehicle Routing Problems
Published in 2024 IEEE 27th International Conference on Intelligent Transportation Systems (ITSC), 2024
Recommended citation: Suzan Al-Nassar, Niki van Stein, Yingjie Fan, "ACO-NSGAII: A Novel Metaheuristics for Bi-Objective Electric Vehicle Routing Problems." 2024 IEEE 27th International Conference on Intelligent Transportation Systems (ITSC), 2024. https://doi.org/10.1109/itsc58415.2024.10919987
Selection of filters for photonic crystal spectrometer using domain-aware evolutionary algorithms
Published in arXiv preprint arXiv:2410.13657, 2024
Recommended citation: Kirill Antonov, Marijn Siemons, Niki van Stein, Thomas Bäck, Ralf Kohlhaas, Anna V. Kononova, "Selection of filters for photonic crystal spectrometer using domain-aware evolutionary algorithms." arXiv preprint arXiv:2410.13657, 2024. https://doi.org/10.2139/ssrn.5984923
Evolutionary computation and explainable AI: A roadmap to understandable intelligent systems
Published in IEEE Transactions on Evolutionary Computation, 2024
Recommended citation: Ryan Zhou, Jaume Bacardit, Alexander Edward Ian Brownlee, Stefano Cagnoni, Martin Fyvie, Giovanni Iacca, John McCall, Niki van Stein, David Walker, Ting Hu, "Evolutionary computation and explainable AI: A roadmap to understandable intelligent systems." IEEE Transactions on Evolutionary Computation, 2024. https://doi.org/10.1109/tevc.2024.3476443
EconoJax: A Fast & Scalable Economic Simulation in Jax
Published in arXiv preprint arXiv:2410.22165, 2024
Recommended citation: Koen Ponse, Aske Plaat, Niki van Stein, Thomas M. Moerland, "EconoJax: A Fast & Scalable Economic Simulation in Jax." arXiv preprint arXiv:2410.22165, 2024. https://doi.org/10.65109/lmzy7606
Llamea: A large language model evolutionary algorithm for automatically generating metaheuristics
Published in IEEE Transactions on Evolutionary Computation, 2024
Recommended citation: Niki van Stein, Thomas Bäck, "Llamea: A large language model evolutionary algorithm for automatically generating metaheuristics." IEEE Transactions on Evolutionary Computation, 2024. https://doi.org/10.1109/tevc.2024.3497793
TX-Gen: multi-objective optimization for sparse counterfactual explanations for time-series classification
Published in arxiv, 2024
Recommended citation: Qi Huang, Sofoklis Kitharidis, Thomas Bäck, Niki van Stein, "TX-Gen: multi-objective optimization for sparse counterfactual explanations for time-series classification." arxiv, 2024. https://doi.org/10.5220/0013066400003886
Towards Fairness in Machine Learning: Balancing Racially Imbalanced Datasets Through Data Augmentation and Generative AI
Published in IJCCI, 2024
Recommended citation: Anthonie Schaap, Sofoklis Kitharidis, Niki van Stein, "Towards Fairness in Machine Learning: Balancing Racially Imbalanced Datasets Through Data Augmentation and Generative AI." IJCCI, 2024. https://doi.org/10.5220/0013002600003837
Explainable benchmarking for iterative optimization heuristics
Published in ACM Transactions on Evolutionary Learning, 2025
Recommended citation: Niki van Stein, Diederick Vermetten, Anna Kononova, Thomas Bäck, "Explainable benchmarking for iterative optimization heuristics." ACM Transactions on Evolutionary Learning, 2025. https://doi.org/10.1145/3712255.3734245
Diffusion and flow matching models for tabular data: A survey
Published in arXiv preprint arXiv:2502.17119, 2025
Recommended citation: Zhong Li, Qi Huang, Lincen Yang, Jiayang Shi, Zhao Yang, Niki van Stein, Thomas Bäck, Matthijs van Leeuwen, "Diffusion and flow matching models for tabular data: A survey." arXiv preprint arXiv:2502.17119, 2025. https://arxiv.org/abs/2502.17119
Computational Intelligence: 14th and 15th International Joint Conference on Computational Intelligence (IJCCI 2022 and IJCCI 2023) Revised Selected Papers
Published in Springer Nature, 2025
Recommended citation: Thomas Bäck, Niki van Stein, Christian Wagner, Jonathan M Garibaldi, Francesco Marcelloni, Hak-Keung Lam, Marie Cottrell, Faiyaz Doctor, Joaquim Filipe, Kevin Warwick, Janusz Kacprzyk (Eds.), "Computational Intelligence: 14th and 15th International Joint Conference on Computational Intelligence (IJCCI 2022 and IJCCI 2023) Revised Selected Papers." Springer Nature, 2025.
Controlling the mutation in large language models for the efficient evolution of algorithms
Published in International Conference on the Applications of Evolutionary Computation (Part of EvoStar), 2025
Recommended citation: Haoran Yin, Anna V. Kononova, Thomas Bäck, Niki van Stein, "Controlling the mutation in large language models for the efficient evolution of algorithms." International Conference on the Applications of Evolutionary Computation (Part of EvoStar), 2025. https://doi.org/10.1007/978-3-031-90065-5_25
Stalling in Space: Attractor Analysis for any Algorithm
Published in International Conference on the Applications of Evolutionary Computation (Part of EvoStar), 2025
Recommended citation: Sarah L. Thomson, Quentin Renau, Diederick Vermetten, Emma Hart, Niki van Stein, Anna V. Kononova, "Stalling in Space: Attractor Analysis for any Algorithm." International Conference on the Applications of Evolutionary Computation (Part of EvoStar), 2025. https://doi.org/10.1007/978-3-031-90062-4_32
Surrogate-based automated hyperparameter optimization for expensive automotive crashworthiness optimization
Published in Structural and Multidisciplinary Optimization, 2025
Recommended citation: Fu Xing Long, Niki van Stein, Moritz Frenzel, Peter Krause, Markus Gitterle, Thomas Bäck, "Surrogate-based automated hyperparameter optimization for expensive automotive crashworthiness optimization." Structural and Multidisciplinary Optimization, 2025. https://doi.org/10.1007/s00158-025-03989-x
In-the-loop hyper-parameter optimization for llm-based automated design of heuristics
Published in ACM Transactions on Evolutionary Learning, 2025
Recommended citation: Niki van Stein, Diederick Vermetten, Thomas Bäck, "In-the-loop hyper-parameter optimization for llm-based automated design of heuristics." ACM Transactions on Evolutionary Learning, 2025. https://doi.org/10.1145/3731567
An Introduction to the Crossroads of Explainable Artificial Intelligence and Evolutionary Computation
Published in Explainable AI for Evolutionary Computation, 2025
Recommended citation: Niki van Stein, Anna V. Kononova, "An Introduction to the Crossroads of Explainable Artificial Intelligence and Evolutionary Computation." Explainable AI for Evolutionary Computation, 2025. https://doi.org/10.1007/978-981-96-2540-6_1
Explainable AI for Evolutionary Computation
Published in Springer, 2025
Recommended citation: Niki van Stein, Anna V Kononova (Eds.), "Explainable AI for Evolutionary Computation." Springer, 2025. https://doi.org/10.1007/978-981-96-2540-6
Global Sensitivity Analysis Is Not Always Beneficial for Evolutionary Computation: A Study in Engineering Design
Published in Explainable AI for Evolutionary Computation, 2025
Recommended citation: Elena Raponi, Ivan Olarte Rodriguez, Niki van Stein, "Global Sensitivity Analysis Is Not Always Beneficial for Evolutionary Computation: A Study in Engineering Design." Explainable AI for Evolutionary Computation, 2025. https://doi.org/10.1007/978-981-96-2540-6_2
The Synergy of Explainable AI and Evolutionary Computation: Real-World Applications
Published in Explainable AI for Evolutionary Computation, 2025
Recommended citation: Niki van Stein, Qi Huang, Elena Raponi, "The Synergy of Explainable AI and Evolutionary Computation: Real-World Applications." Explainable AI for Evolutionary Computation, 2025. https://doi.org/10.1007/978-981-96-2540-6_8
XAI for Benchmarking Black-Box Metaheuristics
Published in Explainable AI for Evolutionary Computation, 2025
Recommended citation: Anna V. Kononova, Diederick Vermetten, Niki van Stein, "XAI for Benchmarking Black-Box Metaheuristics." Explainable AI for Evolutionary Computation, 2025. https://doi.org/10.1007/978-981-96-2540-6_5
Why Are You Wrong? Counterfactual Explanations for Language Grounding with 3D Objects
Published in 2025 International Joint Conference on Neural Networks (IJCNN), 2025
Recommended citation: Tobias Preintner, Weixuan Yuan, Qi Huang, Adrian König, Thomas Bäck, Elena Raponi, Niki van Stein, "Why Are You Wrong? Counterfactual Explanations for Language Grounding with 3D Objects." 2025 International Joint Conference on Neural Networks (IJCNN), 2025. https://doi.org/10.1109/ijcnn64981.2025.11227256
Code evolution graphs: Understanding large language model driven design of algorithms
Published in Proceedings of the Genetic and Evolutionary Computation Conference, 2025
Recommended citation: Niki van Stein, Anna V. Kononova, Lars Kotthoff, Thomas Bäck, "Code evolution graphs: Understanding large language model driven design of algorithms." Proceedings of the Genetic and Evolutionary Computation Conference, 2025. https://doi.org/10.1145/3712256.3726328
BLADE: Benchmark suite for LLM-driven Automated Design and Evolution of iterative optimisation heuristics
Published in Proceedings of the Genetic and Evolutionary Computation Conference Companion, 2025
Recommended citation: Niki van Stein, Anna V. Kononova, Haoran Yin, Thomas Bäck, "BLADE: Benchmark suite for LLM-driven Automated Design and Evolution of iterative optimisation heuristics." Proceedings of the Genetic and Evolutionary Computation Conference Companion, 2025. https://doi.org/10.1145/3712255.3734347
Neighborhood Adaptive Differential Evolution
Published in Proceedings of the Genetic and Evolutionary Computation Conference Companion, 2025
Recommended citation: Niki van Stein, "Neighborhood Adaptive Differential Evolution." Proceedings of the Genetic and Evolutionary Computation Conference Companion, 2025. https://doi.org/10.1145/3712255.3735092
Optimizing photonic structures with large language model driven algorithm discovery
Published in Proceedings of the Genetic and Evolutionary Computation Conference Companion, 2025
Recommended citation: Haoran Yin, Anna V. Kononova, Thomas Bäck, Niki van Stein, "Optimizing photonic structures with large language model driven algorithm discovery." Proceedings of the Genetic and Evolutionary Computation Conference Companion, 2025. https://doi.org/10.1145/3712255.3734288
Structural bias in optimization algorithms
Published in Proceedings of the Genetic and Evolutionary Computation Conference Companion, 2025
Recommended citation: Anna V. Kononova, Niki van Stein, "Structural bias in optimization algorithms." Proceedings of the Genetic and Evolutionary Computation Conference Companion, 2025. https://doi.org/10.1145/3712255.3716527
Development of a photonic crystal spectrometer for greenhouse gas measurements
Published in International Conference on Space Optics—ICSO 2024, 2025
Recommended citation: Marijn Siemons, Martijn Veen, Irina Malysheva, Johannes Algera, Stefan Philippi, Kirill Antonov, Niki van Stein, Jérôme Loicq, Nandini Bhattacharya, Rene Berlich, Anna V. Kononova, Ralf Kohlhaas, "Development of a photonic crystal spectrometer for greenhouse gas measurements." International Conference on Space Optics—ICSO 2024, 2025. https://doi.org/10.1117/12.3071597
Quality–diversity-driven robust evolutionary optimization of optical designs
Published in Optical Engineering, 2025
Recommended citation: Kirill Antonov, Teus Tukker, Tiago Botari, Thomas Bäck, Anna V. Kononova, Niki van Stein, "Quality--diversity-driven robust evolutionary optimization of optical designs." Optical Engineering, 2025. https://doi.org/10.1117/1.oe.64.7.075106
CoComposer: LLM Multi-agent Collaborative Music Composition
Published in arXiv preprint arXiv:2509.00132, 2025
Recommended citation: Peiwen Xing, Aske Plaat, Niki van Stein, "CoComposer: LLM Multi-agent Collaborative Music Composition." arXiv preprint arXiv:2509.00132, 2025. https://arxiv.org/abs/2509.00132
Leveraging Lightweight Generators for Memory Efficient Continual Learning
Published in Intelligent Systems Conference, 2025
Recommended citation: Christiaan Lamers, Ahmed Nabil Belbachir, Thomas Bäck, Niki van Stein, "Leveraging Lightweight Generators for Memory Efficient Continual Learning." Intelligent Systems Conference, 2025. https://doi.org/10.1007/978-3-031-99958-1_21
Automated Algorithm Design for Auto-Tuning Optimizers
Published in Proceedings of Machine Learning and Systems, 2025
Recommended citation: Floris-Jan Willemsen, Niki van Stein, Ben van Werkhoven, "Automated Algorithm Design for Auto-Tuning Optimizers." Proceedings of Machine Learning and Systems, 2025. https://arxiv.org/abs/2510.17899
Scalable, Explainable and Provably Robust Anomaly Detection with One-Step Flow Matching
Published in Advances in Neural Information Processing Systems, 2025
Recommended citation: Zhong Li, Qi Huang, Yuxuan Zhu, Lincen Yang, Mohammad Mohammadi Amiri, Niki van Stein, Matthijs van Leeuwen, "Scalable, Explainable and Provably Robust Anomaly Detection with One-Step Flow Matching." Advances in Neural Information Processing Systems, 2025. https://doi.org/10.52202/085713-2936
Visual Model Selection using Feature Importance Clusters in Fairness-Performance Similarity Optimized Space
Published in arXiv preprint arXiv:2510.22209, 2025
Recommended citation: Sofoklis Kitharidis, Cor J. Veenman, Thomas Bäck, Niki van Stein, "Visual Model Selection using Feature Importance Clusters in Fairness-Performance Similarity Optimized Space." arXiv preprint arXiv:2510.22209, 2025. https://arxiv.org/abs/2510.22209
EvoCAD: Evolutionary CAD Code Generation with Vision Language Models
Published in 2025 IEEE 37th International Conference on Tools with Artificial Intelligence (ICTAI), 2025
Recommended citation: Tobias Preintner, Weixuan Yuan, Adrian König, Thomas Bäck, Elena Raponi, Niki van Stein, "EvoCAD: Evolutionary CAD Code Generation with Vision Language Models." 2025 IEEE 37th International Conference on Tools with Artificial Intelligence (ICTAI), 2025. https://doi.org/10.1109/ictai66417.2025.00075
REMAINING USEFUL LIFE IN COMPLEX MULTI-COMPONENT SYSTEMS: TAXONOMY, REVIEW, AND RESEARCH DIRECTIONS
Published in Authorea Preprints, 2025
Recommended citation: Bernd Wagner, Qi Huang, Lucas Correia, Thomas Bäck, Anna Kononova, Niki van Stein, "REMAINING USEFUL LIFE IN COMPLEX MULTI-COMPONENT SYSTEMS: TAXONOMY, REVIEW, AND RESEARCH DIRECTIONS." Authorea Preprints, 2025. https://doi.org/10.36227/techrxiv.176288069.90065228/v1
Reasoning capabilities of large language models on dynamic tasks
Published in 2025 3rd International Conference on Foundation and Large Language Models (FLLM), 2025
Recommended citation: Annie Wong, Thomas Bäck, Aske Plaat, Niki van Stein, Anna V. Kononova, "Reasoning capabilities of large language models on dynamic tasks." 2025 3rd International Conference on Foundation and Large Language Models (FLLM), 2025. https://doi.org/10.1109/fllm67465.2025.11391251
Gradient Free Multi-Objective Counterfactual Explainability for Multivariate Time Series Classification
Published in 2025 IEEE International Conference on Big Data (BigData), 2025
Recommended citation: Qi Huang, Sofoklis Kitharidis, Furong Ye, Marius Ottolini, Thomas Bäck, Niki van Stein, "Gradient Free Multi-Objective Counterfactual Explainability for Multivariate Time Series Classification." 2025 IEEE International Conference on Big Data (BigData), 2025. https://doi.org/10.1109/bigdata66926.2025.11400892
Multi-step reasoning with large language models, a survey
Published in ACM Computing Surveys, 2025
Recommended citation: Aske Plaat, Annie Wong, Suzan Verberne, Joost Broekens, Niki van Stein, Thomas Bäck, "Multi-step reasoning with large language models, a survey." ACM Computing Surveys, 2025. https://doi.org/10.1145/3774896
Agentic large language models, a survey
Published in Journal of Artificial Intelligence Research, 2025
Recommended citation: Aske Plaat, Max van Duijn, Niki van Stein, Mike Preuss, Peter van der Putten, Kees Joost Batenburg, "Agentic large language models, a survey." Journal of Artificial Intelligence Research, 2025. https://arxiv.org/abs/2503.23037
State Design Matters: How Representations Shape Dynamic Reasoning in Large Language Models
Published in arXiv preprint arXiv:2602.15858, 2026
Recommended citation: Annie Wong, Aske Plaat, Thomas Bäck, Niki van Stein, Anna V. Kononova, "State Design Matters: How Representations Shape Dynamic Reasoning in Large Language Models." arXiv preprint arXiv:2602.15858, 2026. https://arxiv.org/abs/2602.15858
LLaMEA-SAGE: Guiding Automated Algorithm Design with Structural Feedback from Explainable AI
Published in arXiv preprint arXiv:2601.21511, 2026
Recommended citation: Niki van Stein, Anna V. Kononova, Lars Kotthoff, Thomas Bäck, "LLaMEA-SAGE: Guiding Automated Algorithm Design with Structural Feedback from Explainable AI." arXiv preprint arXiv:2601.21511, 2026. https://arxiv.org/abs/2601.21511
A survey of features used for representing black-box single-objective continuous optimization
Published in Swarm and Evolutionary Computation, 2026
Recommended citation: Gjorgjina Cenikj, Ana Nikolikj, Gašper Petelin, Niki van Stein, Carola Doerr, Tome Eftimov, "A survey of features used for representing black-box single-objective continuous optimization." Swarm and Evolutionary Computation, 2026. https://doi.org/10.1016/j.swevo.2026.102288
Behaviour space analysis of llm-driven meta-heuristic discovery
Published in International Joint Conference on Computational Intelligence, 2026
Recommended citation: Niki van Stein, Haoran Yin, Anna V. Kononova, Thomas Bäck, Gabriela Ochoa, "Behaviour space analysis of llm-driven meta-heuristic discovery." International Joint Conference on Computational Intelligence, 2026. https://doi.org/10.1007/978-3-032-15635-8_23
Computational Intelligence: 17th International Joint Conference, IJCCI 2025, Marbella, Spain, October 22–24, 2025, Proceedings, Part III
Published in Springer Nature, 2026
Recommended citation: Francesco Marcelloni, Kurosh Madani, Niki van Stein, Joaquim Filipe (Eds.), "Computational Intelligence: 17th International Joint Conference, IJCCI 2025, Marbella, Spain, October 22–24, 2025, Proceedings, Part III." Springer Nature, 2026.
Mechanistic interpretability for transformer-based time series classification
Published in International Joint Conference on Computational Intelligence, 2026
Recommended citation: Matīss Kalnāre, Sofoklis Kitharidis, Thomas Bäck, Niki van Stein, "Mechanistic interpretability for transformer-based time series classification." International Joint Conference on Computational Intelligence, 2026. https://doi.org/10.1007/978-3-032-15638-9_15
Multi-subspace SVD Generators for Continual Learning
Published in International Joint Conference on Computational Intelligence, 2026
Recommended citation: Christiaan Lamers, Ahmed Nabil Belbachir, Thomas Bäck, Niki van Stein, "Multi-subspace SVD Generators for Continual Learning." International Joint Conference on Computational Intelligence, 2026. https://doi.org/10.1007/978-3-032-15638-9_42
From heuristic selection to automated algorithm design: Llms benefit from strong priors
Published in arXiv preprint arXiv:2603.02792, 2026
Recommended citation: Qi Huang, Furong Ye, Ananta Shahane, Thomas Bäck, Niki van Stein, "From heuristic selection to automated algorithm design: Llms benefit from strong priors." arXiv preprint arXiv:2603.02792, 2026. https://arxiv.org/abs/2603.02792
How does chain of thought think? mechanistic interpretability of chain-of-thought reasoning with sparse autoencoding
Published in Proceedings of the AAAI Conference on Artificial Intelligence, 2026
Recommended citation: Xi Chen, Aske Plaat, Niki van Stein, "How does chain of thought think? mechanistic interpretability of chain-of-thought reasoning with sparse autoencoding." Proceedings of the AAAI Conference on Artificial Intelligence, 2026. https://doi.org/10.1609/aaai.v40i36.40281
Bridging Racial and Age Gaps in Face Recognition: A Data-Augmentation Framework for Fair AI
Published in SN Computer Science, 2026
Recommended citation: Sofoklis Kitharidis, Anthonie Schaap, Thomas Bäck, Niki van Stein, "Bridging Racial and Age Gaps in Face Recognition: A Data-Augmentation Framework for Fair AI." SN Computer Science, 2026. https://doi.org/10.1007/s42979-026-04885-x
Benchmarking that Matters: Rethinking Benchmarking in Continuous Optimisation for Practical Impact
Published in International Conference on the Applications of Evolutionary Computation (Part of EvoStar), 2026
Recommended citation: Anna V. Kononova, Niki van Stein, Olaf Mersmann, Thomas Bäck, Thomas Bartz-Beielstein, Tobias Glasmachers, Michael Hellwig, Sebastian Krey, Jakub Kůdela, Boris Naujoks, Leonard Papenmeier, Elena Raponi, Quentin Renau, Jeroen Rook, Lennart Schäpermeier, Diederick Vermetten, Daniela Zaharie, "Benchmarking that Matters: Rethinking Benchmarking in Continuous Optimisation for Practical Impact." International Conference on the Applications of Evolutionary Computation (Part of EvoStar), 2026. https://doi.org/10.1007/978-3-032-23607-4_20
From Performance to Understanding: A Vision for Explainable Automated Algorithm Design
Published in International Conference on the Applications of Evolutionary Computation (Part of EvoStar), 2026
Recommended citation: Niki van Stein, Anna V. Kononova, Thomas Bäck, "From Performance to Understanding: A Vision for Explainable Automated Algorithm Design." International Conference on the Applications of Evolutionary Computation (Part of EvoStar), 2026. https://doi.org/10.1007/978-3-032-23607-4_21
Investigating the Interplay of Parameterization and Optimizer in Gradient-Free Topology Optimization: A Cantilever Beam Case Study
Published in International Conference on the Applications of Evolutionary Computation (Part of EvoStar), 2026
Recommended citation: Jelle Westra, Ivan Olarte Rodriguez, Niki van Stein, Thomas Bäck, Elena Raponi, "Investigating the Interplay of Parameterization and Optimizer in Gradient-Free Topology Optimization: A Cantilever Beam Case Study." International Conference on the Applications of Evolutionary Computation (Part of EvoStar), 2026. https://doi.org/10.1007/978-3-032-23607-4_29
LLM Driven Design of Continuous Optimization Problems with Controllable High-Level Properties
Published in International Conference on the Applications of Evolutionary Computation (Part of EvoStar), 2026
Recommended citation: Urban Skvorc, Niki van Stein, Moritz Seiler, Britta Grimme, Thomas Bäck, Heike Trautmann, "LLM Driven Design of Continuous Optimization Problems with Controllable High-Level Properties." International Conference on the Applications of Evolutionary Computation (Part of EvoStar), 2026. https://doi.org/10.1007/978-3-032-23607-4_12
MM-OptBench: A Solver-Grounded Benchmark for Multimodal Optimization Modeling
Published in arXiv preprint arXiv:2605.12154, 2026
Recommended citation: Zhong Li, Qi Huang, Yuxuan Zhu, Mohammad Mohammadi Amiri, Niki van Stein, Thomas Bäck, Matthijs van Leeuwen, Zaiwen Wen, Lincen Yang, "MM-OptBench: A Solver-Grounded Benchmark for Multimodal Optimization Modeling." arXiv preprint arXiv:2605.12154, 2026. https://arxiv.org/abs/2605.12154
Every Component is a Lookup: Token Attribution and Composition from a Single Decomposition
Published in arXiv preprint arXiv:2605.23393, 2026
Recommended citation: Po-Kai Chen, Aske Plaat, Niki van Stein, "Every Component is a Lookup: Token Attribution and Composition from a Single Decomposition." arXiv preprint arXiv:2605.23393, 2026. https://arxiv.org/abs/2605.23393
3DMorph: Single-Image-Guided Local 3D Shape Editing and Morphing
Published in arXiv preprint arXiv:2606.07115, 2026
Recommended citation: Tobias Preintner, Yunfei Deng, Phillip Müller, Sebastian Illing, Adrian König, Thomas Bäck, Elena Raponi, Niki van Stein, "3DMorph: Single-Image-Guided Local 3D Shape Editing and Morphing." arXiv preprint arXiv:2606.07115, 2026. https://doi.org/10.1109/ijcnn69854.2026.11728815
Maintenance Planning for Prognostic Health Management of Multi-Component Systems: A Case Study of Multi-Objective Optimisation for Railway Vehicles
Published in PHM Society European Conference, 2026
Recommended citation: Bernd Wagner, Sofoklis Kitharidis, Furong Ye, Thomas Bäck, Niki van Stein, "Maintenance Planning for Prognostic Health Management of Multi-Component Systems: A Case Study of Multi-Objective Optimisation for Railway Vehicles." PHM Society European Conference, 2026. https://doi.org/10.36001/phme.2026.v9i1.5006
Computational Intelligence: 16th International Joint Conference, IJCCI 2024 Porto, Portugal, November 20-22, 2024 Revised Selected Papers
Published in Springer Nature, 2026
Recommended citation: Niki van Stein, Francesco Marcelloni, Kurosh Madani, Joaquim Filipe (Eds.), "Computational Intelligence: 16th International Joint Conference, IJCCI 2024 Porto, Portugal, November 20-22, 2024 Revised Selected Papers." Springer Nature, 2026.
LLM-Driven Evolutionary Generation of Multi-Objective Bayesian Optimization Algorithms
Published in arXiv preprint arXiv:2607.08791, 2026
Recommended citation: Georgios Laskaris, Reuben Brasher, Niki van Stein, Elena Raponi, Thomas Bäck, Florian Neukart, "LLM-Driven Evolutionary Generation of Multi-Objective Bayesian Optimization Algorithms." arXiv preprint arXiv:2607.08791, 2026. https://arxiv.org/abs/2607.08791
Assessing Reproducibility in Evolutionary Computation: A Case Study using Human-and LLM-based Assessment
Published in Proceedings of the Genetic and Evolutionary Computation Conference, 2026
Recommended citation: Francesca Da Ros, Tarik Začiragić, Aske Plaat, Thomas Bäck, Niki van Stein, "Assessing Reproducibility in Evolutionary Computation: A Case Study using Human-and LLM-based Assessment." Proceedings of the Genetic and Evolutionary Computation Conference, 2026. https://arxiv.org/abs/2602.07059
Block-Bench: A Framework for Controllable and Transparent Discrete Optimization Benchmarking
Published in Proceedings of the Genetic and Evolutionary Computation Conference, 2026
Recommended citation: Furong Ye, Frank Neumann, Thomas Bäck, Niki van Stein, "Block-Bench: A Framework for Controllable and Transparent Discrete Optimization Benchmarking." Proceedings of the Genetic and Evolutionary Computation Conference, 2026. https://doi.org/10.1145/3795095.3805071
LLaMEA-BO: A Large Language Model Evolutionary Algorithm for Automatically Generating Bayesian Optimization Algorithms
Published in Proceedings of the Genetic and Evolutionary Computation Conference, 2026
Recommended citation: Wenhu Li, Niki van Stein, Thomas Bäck, Elena Raponi, "LLaMEA-BO: A Large Language Model Evolutionary Algorithm for Automatically Generating Bayesian Optimization Algorithms." Proceedings of the Genetic and Evolutionary Computation Conference, 2026. https://doi.org/10.1145/3795095.3805059
Landscape-aware Automated Algorithm Design: An Efficient Framework for Real-world Optimization
Published in Proceedings of the Genetic and Evolutionary Computation Conference, 2026
Recommended citation: Haoran Yin, Shuaiqun Pan, Zhao Wei, Jian Cheng Wong, Yew-Soon Ong, Anna Kononova, Thomas Bäck, Niki van Stein, "Landscape-aware Automated Algorithm Design: An Efficient Framework for Real-world Optimization." Proceedings of the Genetic and Evolutionary Computation Conference, 2026. https://doi.org/10.1145/3795095.3805094
Structural Bias in Multi-objective Optimization
Published in Proceedings of the Genetic and Evolutionary Computation Conference, 2026
Recommended citation: Jakub Kudela, Niki van Stein, Thomas Bäck, Anna Kononova, "Structural Bias in Multi-objective Optimization." Proceedings of the Genetic and Evolutionary Computation Conference, 2026. https://doi.org/10.1145/3795095.3805108
EvoHIIT: Preference-Based Selection in LLM-Assisted Evolutionary Design of Natural Language Solutions
Published in Proceedings of the Genetic and Evolutionary Computation Conference Companion, 2026
Recommended citation: Johana Chen, Niki van Stein, Robert Cabri, Thomas Bäck, Anna V. Kononova, "EvoHIIT: Preference-Based Selection in LLM-Assisted Evolutionary Design of Natural Language Solutions." Proceedings of the Genetic and Evolutionary Computation Conference Companion, 2026. https://doi.org/10.1145/3795101.3814653
Large Language Models for Metaheuristic Design: Exploring Challenges, Limitations, Tools, Benchmarking and Future Opportunities
Published in Proceedings of the Genetic and Evolutionary Computation Conference Companion, 2026
Recommended citation: Roman Senkerik, Niki van Stein, Adam Viktorin, Michal Pluhacek, Thomas Bäck, "Large Language Models for Metaheuristic Design: Exploring Challenges, Limitations, Tools, Benchmarking and Future Opportunities." Proceedings of the Genetic and Evolutionary Computation Conference Companion, 2026. https://doi.org/10.1145/3795101.3807753
Optimizing for Difference: LLM-Driven Benchmark Design for Maximum Solver Discriminability
Published in Proceedings of the Genetic and Evolutionary Computation Conference Companion, 2026
Recommended citation: Ananta A. Shahane, Niki van Stein, "Optimizing for Difference: LLM-Driven Benchmark Design for Maximum Solver Discriminability." Proceedings of the Genetic and Evolutionary Computation Conference Companion, 2026. https://doi.org/10.1145/3795101.3815555
Pruning Federated Models through Loss Landscape Analysis and Client Agreement Scoring
Published in IEEE Internet of Things Journal, 2026
Recommended citation: Christian Internò, Elena Raponi, Markus Olhofer, Ali Raza, Thomas Bäck, Niki van Stein, Yaochu Jin, Barbara Hammer, "Pruning Federated Models through Loss Landscape Analysis and Client Agreement Scoring." IEEE Internet of Things Journal, 2026. https://doi.org/10.1109/jiot.2026.3686028
Evolving Executable Pipeline Programs for AutoML with Language Models
Published in arXiv preprint arXiv:2608.16416, 2026
Recommended citation: Sofoklis Kitharidis, Cor J. Veenman, Jan N. van Rijn, Thomas Bäck, Niki van Stein, "Evolving Executable Pipeline Programs for AutoML with Language Models." arXiv preprint arXiv:2608.16416, 2026. https://arxiv.org/abs/2608.16416
Lens-descriptor guided evolutionary algorithm for optimization of complex optical systems with glass choice
Published in International Conference on Parallel Problem Solving from Nature, 2026
Recommended citation: Kirill Antonov, Teus Tukker, Tiago Botari, Thomas Bäck, Anna V. Kononova, Niki van Stein, "Lens-descriptor guided evolutionary algorithm for optimization of complex optical systems with glass choice." International Conference on Parallel Problem Solving from Nature, 2026. https://doi.org/10.1007/978-3-032-36229-2_25
Detecting and Repairing Hallucinations in Retrieval-Augmented Generation
Published in arXiv preprint arXiv:2608.29307, 2026
Recommended citation: Sai Krishna Reddy Mulakkayala, Niki van Stein, Aske Plaat, "Detecting and Repairing Hallucinations in Retrieval-Augmented Generation." arXiv preprint arXiv:2608.29307, 2026. https://arxiv.org/abs/2608.29307
EvoMO-SR: Multiobjective LLM-based Evolution of Symbolic Expressions with substructure guidance
Published in arXiv preprint arXiv:2609.36187, 2026
Recommended citation: Cristina Rossetti, Anna V. Kononova, Thomas Bäck, Fei Liu, Niki van Stein, "EvoMO-SR: Multiobjective LLM-based Evolution of Symbolic Expressions with substructure guidance." arXiv preprint arXiv:2609.36187, 2026. https://arxiv.org/abs/2609.36187
Breaking Bureaucracy: Evaluating open-source LLMs for legal document review
Published in arXiv preprint arXiv:2610.06345, 2026
Recommended citation: Farrukh Baratov, Niki van Stein, Suzan Verberne, "Breaking Bureaucracy: Evaluating open-source LLMs for legal document review." arXiv preprint arXiv:2610.06345, 2026. https://arxiv.org/abs/2610.06345
Agentic Large Language Models
Published in Springer (book, coming soon), 2026
Recommended citation: Max van Duijn, Michiel van der Meer, Aske Plaat, Niki van Stein, "Agentic Large Language Models." Springer (book, coming soon), 2026. https://arxiv.org/abs/2610.13455
Large language models and evolutionary computation: A critical review of bidirectional interaction, automated algorithm design, and co-adaptive systems
Published in Computer Science Review, 2027
Recommended citation: Dikshit Chauhan, Bapi Dutta, Indu Bala, Niki van Stein, Thomas Bäck, Anupam Yadav, "Large language models and evolutionary computation: A critical review of bidirectional interaction, automated algorithm design, and co-adaptive systems." Computer Science Review, 2027. https://doi.org/10.1016/j.cosrev.2026.101050
talks
Datamining & Apps
Published:
Invited talk at the CWI, Amsterdam
Fuzzy Clustering for Optimally Weighted Cluster Kriging
Published:
Invited talk for the PhD Seminar at LIACS, Leiden University
From Data to Knowledge to Optimization
Published:
Invited talk for thhe PhH Colloquium with Math and Informatics
Deep Steel: Optimizing networks to discover defects on steel surface
Published:
Invited talk for the ECOLE program, learning to optimize.
Deep Steel: Optimizing networks to discover defects on steel surface
Published:
Invited talk at Tata Steel, about using deep learning to discover steel surface defects.
Deep Learning, A broad introduction
Published:
Invited talk for the Summer school for Early Stage Researchers. This summer school takes place within the EU project Experience-based Computation: Learning to Optimise (ECOLE), which investigates novel synergies between nature-inspired optimisation and machine learning to address key challenges faced by the European industry.
Automated Deep Learning
Published:
Invited presentation at SAILS (online)
Neural Architecture Search VS Black Box Optimization
Published:
Invited presentation at BMW headquarters
Computers Don’t Byte: XAI
Published:
🚢⚙️ Exciting News: “Computers Don’t Byte” Podcast by LIACS! 🎙️
XAI in Industry
Published:
Invited talk on applications of explainable AI in industry at the SAILS Lunch Time Seminar.
XAI for Predictive Maintenance
Published:
Invited talk on explainable AI for predictive maintenance at Microsoft Netherlands.
XAI for Healthcare
Published:
Invited talk on explainable AI for healthcare at VZI HealthTech 2024.
Explainable AI
Published:
Invited talk on explainable AI at Alice and Eve 2024.
Evaluating XAI
Published:
Invited talk on evaluating explainable AI at the XAI-NL meetup.
LLaMEA for Automated Algorithm Design
Published:
Invited talk for the AToNIIC lecture series: https://altzanetos.com/atoniic/llamea-llm-ea-for-automatically-generating-metaheuristics/ (February 20, 2025, 15:00 CET).
XAI for Time Series
Published:
Invited talk on explainable AI for time series at TU Delft.
Automated Algorithm Design
Published:
Invited talk for the AutoML summer school in Tübingen, Germany (June 10–13, 2025).
Automated Algorithm Design
Published:
Invited talk for the AutoML Seminar Series.
Explainable Automated Algorithm Design
Published:
Invited keynote for the Auto-Learn SI workshop focusing on explainable approaches to automated algorithm design.
Evolving Knowledge: What Evolutionary Search and Agentic LLMs can teach us
Published:
Invited keynote for the “AI in Education: Impact, Insights, & Innovation” conference at Leiden University, on what evolutionary search and agentic LLMs can teach us.
Automatic Algorithm Design with LLMs and Evolutionary Computation
Published:
Invited lecture on automatic algorithm design with LLMs and evolutionary computation, covering LLaMEA and how to apply it to real-world cases. Given at the Peking University summer school “Swarm Intelligence & Large Language Models: Intersection, Collision, and Academic Frontiers”.
Towards a Universal Algorithm Constructor: Evolutionary Computation in the Age of LLMs
Published:
Abstract
What if evolutionary computation is not just a tool for optimization, but a design engine for algorithms themselves?LLMs for Optimization in Code-Spaces: From Metaheuristic Design to AutoML
Published:
Invited lecture at the AutoML summer school on LLMs for optimization in code-spaces, covering LLaMEA, LLaMEA-BO, and our newest work applying LLaMEA to optimize ML pipelines in code-space.
Efficiently Evolving Algorithms for Real-World Applications
Published:
Abstract
What if evolutionary computation is not just a tool for optimization, but a design engine for algorithms themselves?Automated Algorithm Design
Published:
Keynote speaker at AutoML 2026, held September 28 – October 1, 2026 in Ljubljana, Slovenia.
teaching
Data Science course
Course, Bsc Computer Science, Leiden University, 2022
Data Science places data mining, machine learning and statistics in context, both experimentally and socially. If you want to correctly deploy data mining techniques, you must be able to translate a (broadly formulated) question by a customer or a co-worker into an experimental set-up, to make the right choices for the methods you use, and to be able to process the data in the right form to apply those methods. After performing your experiments, you should not only be able to evaluate the results but also interpret and translate it back to the original question (e.g. by visualization). Socially, data science is of great importance because the media simplify many data-driven results and statistical research, often making mistakes. Thus, a lot of nonsense comes down on us and it is up to you, the data scientists of the future, to recognize, explain and correct that nonsense. This course is a combination of lectures and practical sessions, in which you take a hands-on approach to solving real-world data science problems.
Business Analytics (Minor)
Course, Bsc Computer Science, Leiden University, 2024
Business Analytics places data mining, visualisation, machine learning and statistics in a business context. If you want to correctly deploy data mining / AI techniques, you must be able to translate a (broadly formulated) question by a customer or a co-worker into an experimental set-up, to make the right choices for the methods you use, and to be able to process the data in the right form to apply those methods. After performing your experiments, you should not only be able to evaluate the results but also interpret and translate it back to the original question (e.g. by visualization) and communicate findings to management of colleagues. Socially, data science is of great importance because the media simplify many data-driven results and statistical research, often making mistakes. Thus, a lot of nonsense comes down on us and it is up to you, the data scientists of the future, to recognize, explain and correct that nonsense. This course is a combination of lectures and practical sessions, in which you take a hands-on approach to solving real-world data science problems in a business context.
Applied Data Science and Explainable AI
Course, Bsc Computer Science, Leiden University, 2025
Data Science places data mining, machine learning and statistics in context, both experimentally and socially. If you want to correctly deploy data mining techniques, you must be able to translate a (broadly formulated) question by a customer or a co-worker into an experimental set-up, to make the right choices for the methods you use, and to be able to process the data in the right form to apply those methods. After performing your experiments, you should not only be able to evaluate the results but also interpret and translate it back to the original question (e.g. by visualization). Socially, data science is of great importance because the media simplify many data-driven results and statistical research, often making mistakes. Thus, a lot of nonsense comes down on us and it is up to you, the data scientists of the future, to recognize, explain and correct that nonsense. This course is a combination of lectures and practical sessions, in which you take a hands-on approach to solving real-world data science problems.
Agentic Large Language Models
Master course, Master course, LIACS, Leiden University, 2026
A master course (6 EC) at LIACS on large language models that reason, act and interact. We follow the textbook Agentic Large Language Models (Van Duijn, Van der Meer, Plaat & Van Stein) and teach the course together as the same four authors.