I received my Bachelor in Computer Science in 2011 and my Masters degree in 2013, both at the Leiden Institute of Advanced Computer Science (LIACS), Leiden University. In 2012, I founded a software company, Van Stein & Groentjes VOF, together with Tom Groentjes, which turned into a B.V. in 2014, with the hiring of our first employee. At the same time, I continued in academia by gaining my Ph.D. degree in 2018, doing a PostDoc in the CIMPLO project and currently I am an assistant professor in the Natural Computing group at LIACS. My research interests are focused on eXplainable Artificial Intelligence (XAI) in the context of optimization and time-series data with industrial applications such as predictive maintenance and schedule optimization. Next to research, I am active at the University as manager of the Applied Data Science Lab, member of the Institute Council, teacher of the bi-weekly Master Class and the Data Science course, su- pervisor of 5 Ph.D. students, several Bsc and Msc students per semester and as technical support for a number of computing clusters. My experience from my entrepreneurial and software development activities, to- gether with my research, collaborations, and management activities at LIACS, define who I am as a professional.
| 2022 – now Assistant professor | Manager ADSL |
| 2018 – 2021 PostDoc | Manager ADSL |
Bas Van, 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.
Bas Stein, Hao Wang, Wojtek Kowalczyk, Thomas B{\"a}ck, 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.
Pepijn Van, Bas Stein, Thomas B{\"a}ck, "A framework for evaluating meta-models for simulation-based optimisation." 2016 IEEE Symposium Series on Computational Intelligence (SSCI), 2016.
Bas Van, 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.
Bas Stein, Wojtek Kowalczyk, Thomas B{\"a}ck, "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.
Bas Stein, Hao Wang, Wojtek Kowalczyk, Michael Emmerich, Thomas B{\"a}ck, "Fuzzy clustering for optimally weighted cluster kriging." 2016 IEEE international conference on fuzzy systems (FUZZ-IEEE), 2016.
Bas Van, Matthijs Van, Thomas B{\"a}ck, "Local subspace-based outlier detection using global neighbourhoods." 2016 IEEE International Conference on Big Data (Big Data), 2016.
Bas Van, Matthijs Van, Hao Wang, Stephan Purr, Sebastian Kreissl, Josef Meinhardt, Thomas B{\"a}ck, "Towards data driven process control in manufacturing car body parts." 2016 International Conference on Computational Science and Computational Intelligence (CSCI), 2016.
Thierry Spek, Bas Stein, Marcel Holst, Thomas B{\"a}ck, "A multi-method simulation of a high-frequency bus line." 2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITSC), 2017.
Hao Wang, Bas Stein, Michael Emmerich, Thomas Back, "A new acquisition function for Bayesian optimization based on the moment-generating function." 2017 IEEE International Conference on Systems, Man, and Cybernetics (SMC), 2017.
Sander Rijn, Hao Wang, Bas Stein, Thomas B{\"a}ck, "Algorithm configuration data mining for cma evolution strategies." Proceedings of the Genetic and Evolutionary Computation Conference, 2017.
Hao Wang, Bas Stein, Michael Emmerich, Thomas B{\"a}ck, "Time complexity reduction in efficient global optimization using cluster kriging." Proceedings of the Genetic and Evolutionary Computation Conference, 2017.
Bas Stein, Hao Wang, Wojtek Kowalczyk, Thomas B{\"a}ck, "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.
Xin Guo, Bas Stein, Thomas B{\"a}ck, "A new approach towards the combined algorithm selection and hyper-parameter optimization problem." 2019 IEEE Symposium Series on Computational Intelligence (SSCI), 2019.
Bas Stein, Hao Wang, Thomas B{\"a}ck, "Automatic configuration of deep neural networks with parallel efficient global optimization." 2019 International Joint Conference on Neural Networks (IJCNN), 2019.
Roy Winter, Bas Stein, Matthys Dijkman, Thomas B{\"a}ck, "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.
Thiago Rios, Bernhard Sendhoff, Stefan Menzel, Thomas B{\"a}ck, Bas 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.
Thiago Rios, Patricia Wollstadt, Bas Stein, Thomas B{\"a}ck, 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.
Yali Wang, Bas Stein, Thomas B{\"a}ck, Michael Emmerich, "A tailored NSGA-III for multi-objective flexible job shop scheduling." 2020 IEEE Symposium Series on Computational Intelligence (SSCI), 2020.
Thiago Rios, Jiawen Kong, Bas Stein, Thomas B{\"a}ck, 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.
Bas Van, Hao Wang, Wojtek Kowalczyk, Michael Emmerich, Thomas B{\"a}ck, "Cluster-based Kriging approximation algorithms for complexity reduction." Applied Intelligence, 2020.
Thiago Rios, Bas Stein, Stefan Menzel, Thomas Back, Bernhard Sendhoff, Patricia Wollstadt, "Feature visualization for 3D point cloud autoencoders." 2020 International Joint Conference on Neural Networks (IJCNN), 2020.
Yali Wang, Bas Stein, Thomas B{\"a}ck, 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.
Bas Stein, Hao Wang, Thomas B{\"a}ck, "Neural network design: learning from neural architecture search." 2020 IEEE Symposium Series on Computational Intelligence (SSCI), 2020.
Bas Stein, Fabio Caraffini, Anna Kononova, "Emergence of structural bias in differential evolution." Proceedings of the Genetic and Evolutionary Computation Conference Companion, 2021.
Sneha Saha, Thiago Rios, Leandro Minku, Bas Stein, Patricia Wollstadt, Xin Yao, Thomas Back, Bernhard Sendhoff, Stefan Menzel, "Exploiting generative models for performance predictions of 3D car designs." 2021 IEEE Symposium Series on Computational Intelligence (SSCI), 2021.
Thiago Rios, Bas Stein, Patricia Wollstadt, Thomas B{\"a}ck, 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.
Thiago Rios, Bas Stein, Thomas B{\"a}ck, Bernhard Sendhoff, Stefan Menzel, "Multitask Shape Optimization Using a 3-D Point Cloud Autoencoder as Unified Representation." IEEE Transactions on Evolutionary Computation, 2021.
Gideon Hanse, Roy Winter, Bas Stein, Thomas B{\"a}ck, "Optimally weighted ensembles for efficient multi-objective optimization." International Conference on Machine Learning, Optimization, and Data Science. Springer, 2021.
Thiago Rios, Bas Van, Thomas B{\"a}ck, 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.
Alexander Zeiser, Bas Stein, Thomas B{\"a}ck, "Requirements towards optimizing analytics in industrial processes." Procedia Computer Science, 2021.
Roy Winter, Bas Stein, Thomas B{\"a}ck, "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.
R Winter, B Stein, THW B{\"a}ck, V Bertram, "Ship design performance and cost optimization with machine learning." COMPIT'21, 2021.
Koen Ponse, Anna Kononova, Maria Loleyt, Bas Van, "Using Machine Learning to Detect Rotational Symmetries from Reflectional Symmetries in 2D Images." 2021 IEEE Symposium Series on Computational Intelligence (SSCI), 2021.
Bas Van, Elena Raponi, Zahra Sadeghi, Niek Bouman, Roeland Van, Thomas B{\"a}ck, "A comparison of global sensitivity analysis methods for explainable AI with an application in genomic prediction." IEEE Access, 2022.
Marios Kefalas, Bas Stein, Mitra Baratchi, Asteris Apostolidis, Thomas B{\"a}ck, "An end-to-end pipeline for uncertainty quantification and remaining useful life estimation: An application on aircraft engines." PHM Society European Conference, 2022.
Diederick Vermetten, Bas Stein, Anna Kononova, Fabio Caraffini, "Analysis of structural bias in differential evolution configurations." Differential Evolution: From Theory to Practice, 2022.
Diederick Vermetten, Bas Stein, Fabio Caraffini, Leandro Minku, Anna Kononova, "BIAS: a toolbox for benchmarking structural bias in the continuous domain." IEEE Transactions on Evolutionary Computation, 2022.
Roy Winter, Philip Bronkhorst, Bas Stein, Thomas B{\"a}ck, "Constrained multi-objective optimization with a limited budget of function evaluations." Memetic Computing, 2022.
Marios Kefalas, Juan Santiago, Asteris Apostolidis, Dirk Van, Bas Stein, Thomas B{\"a}ck, "Explainable artificial intelligence for exhaust gas temperature of turbofan engines." Journal of Aerospace Information Systems, 2022.
Bas Van, Elena Raponi, "GSAreport: Easy to Use Global Sensitivity Reporting." Journal of Open Source Software, 2022.
Fu Long, Bas Stein, Moritz Frenzel, Peter Krause, Markus Gitterle, Thomas B{\"a}ck, "Learning the characteristics of engineering optimization problems with applications in automotive crash." Proceedings of the Genetic and Evolutionary Computation Conference, 2022.
Roy Winter, Bas Stein, Thomas B{\"a}ck, "Multi-point acquisition function for constraint parallel efficient multi-objective optimization." Proceedings of the Genetic and Evolutionary Computation Conference, 2022.
Qi Huang, Roy Winter, Bas Stein, Thomas B{\"a}ck, Anna Kononova, "Multi-surrogate Assisted Efficient Global Optimization for Discrete Problems." Preprint on arXiv preprint arXiv:2212.06438, 2022.
Diederick Vermetten, Fabio Caraffini, Bas Stein, Anna Kononova, "Using structural bias to analyse the behaviour of modular CMA-ES." Proceedings of the Genetic and Evolutionary Computation Conference Companion, 2022.
Alexander Zeiser, Bekir {\"O}zcan, Christoph Kracke, Bas Stein, Thomas B{\"a}ck, "A data-centric approach to anomaly detection in layer-based additive manufacturing." at-Automatisierungstechnik, 2023.
Fu Long, Diederick Vermetten, Bas Stein, Anna Kononova, "BBOB Instance Analysis: Landscape Properties and Algorithm Performance Across Problem Instances." International Conference on the Applications of Evolutionary Computation (Part of EvoStar), 2023.
Fu Long, Diederick Vermetten, Anna Kononova, Roman Kalkreuth, Kaifeng Yang, Thomas B{\"a}ck, Niki Stein, "Challenges of ELA-guided Function Evolution using Genetic Programming." Preprint on arXiv preprint arXiv:2305.15245, 2023.
Bas Stein, Diederick Vermetten, Fabio Caraffini, Anna Kononova, "Deep-BIAS: Detecting Structural Bias using Explainable AI." Preprint on arXiv preprint arXiv:2304.01869, 2023.
Bas Stein, Fu Long, Moritz Frenzel, Peter Krause, Markus Gitterle, Thomas B{\"a}ck, "DoE2Vec: Deep-learning Based Features for Exploratory Landscape Analysis." Preprint on arXiv preprint arXiv:2304.01219, 2023.
Alexander Zeiser, Bekir {\"O}zcan, Bas Stein, Thomas B{\"a}ck, "Evaluation of deep unsupervised anomaly detection methods with a data-centric approach for on-line inspection." Computers in Industry, 2023.
Thomas B{\"a}ck, Anna Kononova, Bas Stein, Hao Wang, Kirill Antonov, Roman Kalkreuth, Jacob Nobel, Diederick Vermetten, Roy Winter, Furong Ye, "Evolutionary Algorithms for Parameter Optimization—Thirty Years Later." Evolutionary Computation, 2023.
Kirill Antonov, Anna Kononova, Thomas B{\"a}ck, Niki Stein, "Representation-agnostic distance-driven perturbation for optimizing ill-conditioned problems." Preprint on arXiv preprint arXiv:2306.02985, 2023.
Kirill Antonov, Anna Kononova, Thomas B{\"a}ck, Niki Stein, "Curing ill-Conditionality via Representation-Agnostic Distance-Driven Perturbations." Proceedings of the 17th ACM/SIGEVO Conference on Foundations of Genetic Algorithms, 2023.
SL Thomson, N van Stein, D van den Berg, C van Leeuwen, "The Opaque Nature of Intelligence and the Pursuit of Explainable AI." ECTA 2023 proceedings, 2023.
Kirill Antonov, Roman Kalkreuth, Kaifeng Yang, Thomas B{\"a}ck, Niki Stein, Anna Kononova, "A Functional Analysis Approach to Symbolic Regression." Preprint on arXiv preprint arXiv:2402.06299, 2024.
Niki Stein, Diederick Vermetten, Anna Kononova, Thomas B{\"a}ck, "Explainable Benchmarking for Iterative Optimization Heuristics." Preprint on arXiv preprint arXiv:2401.17842, 2024.
Roy Winter, Bas Milatz, Julian Blank, Niki Stein, Thomas B{\"a}ck, Kalyanmoy Deb, "Parallel multi-objective optimization for expensive and inexpensive objectives and constraints." Swarm and Evolutionary Computation, 2024.
Qi Huang, Wei Chen, Thomas B{\"a}ck, Niki Stein, "Shapelet-based Model-agnostic Counterfactual Local Explanations for Time Series Classification." Preprint on arXiv preprint arXiv:2402.01343, 2024.
talk at IDA 2015, Saint-Etienne, France
Invited talk at PhD Seminar, Liacs, Leiden, The Netherlands
Talk at WCCI 2016, Vancouver, Canada
Invited guest lecture at Data Science course, Liacs, Leiden, The Netherlands
Talk at IPMU 2016, Eindhoven, The Netherlands
Talk at IPMU 2016, Eindhoven, The Netherlands
Talk at IEEE Big Data, Washington DC, USA
Invited talk at PhD Colloquium, Leiden, The Nethehrlands
Talk at IPMU 2018, Cadiz, Spain
Invited talk at ECOLE workshop, Leiden, The Netherlands
Invited talk at Tata Steel, IJmuiden, The Netherlands
Talk at IJCNN 2019, Budapest, Hungary
Invited talk at Ecole Summer school, Leiden, The Netherlands
Talk at SSCI 2019, Xiamen, China
Invited talk at SAILS 2020,
Talk at SSCI 2020, Canberra, Australia
Invited talk at BMW invited presentation, Munich, Germany
poster at CAI 2023, Santa Clara, California, United States
poster at CAI 2023, Santa Clara, California, United States
talk at GECCO 2023, Lisbon, Portugal
poster at GECCO 2023, Lisbon, Portugal
poster at GECCO 2023, Lisbon, Portugal
podcast at LIACS podcast, Online