Niki van Stein
Assistant professor
- Name
- Dr. N. van Stein
- Telephone
- +31 71 527 2727
- n.van.stein@liacs.leidenuniv.nl
- ORCID iD
- 0000-0002-0013-7969
Niki van Stein is a researcher in the Natural Computing Group of LIACS and manager of the applied data science lab. She received her PhD in Computer Science from Leiden University in 2018. Niki's research interest are in automated machine learning, global (Bayesian) optimization and neural architecture search.
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Niki van Stein is a researcher in the Natural Computing Group of LIACS and manager of the applied data science lab. She received her PhD in Computer Science from Leiden University in 2018. Niki's research interest are in automated machine learning, global (Bayesian) optimization and neural architecture search.
Next to her research, Niki has founded several software solution-oriented companies such as Van Stein & Groentjes – Custom software development, and Smartnotation – the smart meeting minutes application.
Assistant professor
- Science
- Leiden Inst of Advanced Computer Science
Work address
Schipholweg 55-892316 ZL Leiden
Contact
- Stein. N. van, Winter R. de, Bäck T.H.W. & Kononova A. V. (2023), AI for Expensive Optimization Problems in Industry. 2023 IEEE Conference on Artificial Intelligence 5 June 2023 - 6 June 2023. 2023 IEEE Conference on Artificial Intelligence: IEEE.
- Thomson S. L., Stein N. van, Berg D. van den & Leeuwen C. van (2023), The Opaque Nature of Intelligence and the Pursuit of Explainable AI. 15th International Conference on Neural Computation Theory and Applications 13 November 2023 - 15 November 2023. 15th International Conference on Neural Computation Theory and Applications 555-564.
- Stein B. van & Raponi E. (2022), GSAreport: easy to use global sensitivity reporting, Journal of Open Source Software 7(78): 4721.
- Winter R. de, Bronkhorst P., Stein B. van & Bäck T.H.W. (2022), Constrained multi-objective optimization with a limited budget of function evaluations, Memetic Computing 14: 151-174.
- Vermetten D.L., Caraffini F., Stein B. van & Kononova A.V. (2022), Using structural bias to analyse the behaviour of modular CMA-ES. In: Fieldsend J.E. (Ed.) GECCO '22: Proceedings of the genetic and evolutionary computation conference companion. Boston: ACM. 1674-1682.
- Winter R. de, Stein B. van & Bäck T.H.W. (2022), Multi-point acquisition function for constraint parallel efficient multi-objective optimization. In: Fieldsend E.J. (Ed.) GECCO '22: Proceedings of the genetic and evolutionary computation conference. New York: Association for Computing Machinery . 511-519.
- Kefalas M., Stein B. van, Baratchi M., Apostolidis A. & Bäck T.H.W. (2022), End-to-end pipeline for uncertainty quantification and remaining useful life estimation: an application on aircraft engines. In: Do P. (Ed.) Proceedings of the European conference of the PHM Society 2022 . PHM Society European Conference no. 7: PHM Society. 245-260.
- Kefalas M., Santiago Rojo J. de, Apostolidis A., Herik D. van den, Stein B. van & Bäck T.H.W. (2022), Explainable artificial intelligence for exhaust gas temperature of turbofan engines, Journal of Aerospace Information Systems 19(6): 447-454.
- Hanse G., Winter R. de, Stein B. van & Bäck T.H.W. (2022), Optimally weighted ensembles for efficient multi-objective optimization. In: Nicosia G., Ojha V., Malfa E. La, Malfa G. La, Jansen G., Pardalos P.M., Giuffrida G. & Umeton R. (Eds.) Machine Learning, Optimization, and Data Science. LOD 2021. no. 13163 Cham: Springer. 144-156.
- Saha S., de Jesus de Araujo Rios T., Minku L.L., Stein B. van, Wollstadt P., Yao X., Bäck T.H.W., Sendhoff B. & Menzel S. (2021), Exploiting generative models for performance predictions of 3D car designs: IEEE. 1-9.
- Stein B. van, Raponi E., Sadeghi Z., Bouman N., Ham R.C.H.J. van & Bäck T.H.W. (2022), A comparison of global sensitivity analysis methods for explainable AI with an application in genomic prediction, IEEE Access 10: 103364-103381.
- Vermetten D.L., Stein B. van, Kononova A.V. & Caraffini F. (2022), Analysis of structural bias in differential evolution configurations. In: Kumar B.V., Oliva D. & Suganthan P.N (Eds.) Differential evolution: from theory to practice. Studies in Computational Intelligence Singapore: Springer. 1-22.
- Long F.X., Stein B. van, Frenzel M., Krause P., Gitterle M. & Bäck T.H.W. (2022), Learning the characteristics of engineering optimization problems with applications in automotive crash. In: Fieldsend J.E. (Ed.) GECCO '22: Proceedings of the genetic and evolutionary computation conference.: ACM. 1227-1236.
- De Jesus de Araujo Rios T., Stein B. van, Bäck T.H.W., Sendhoff B. & Menzel S. (2022), Multi-task shape optimization using a 3D point cloud autoencoder as unified representation, IEEE Transactions on Evolutionary Computation 26(2): 206-217.
- Vermetten D.L., Stein B. van, Caraffini F., Minku L. & Kononova A.V. (2021), BIAS: a toolbox for benchmarking structural bias in the continuous domain: Institute of Electrical and Electronics Engineers (IEEE). [Working paper].
- Stein B. van, Caraffini F. & Kononova A.V. (2021), Emergence of structural bias in differential evolution. In: Chicano F. (Ed.) Proceedings of the Genetic and Evolutionary Computation Conference Companion.: ACM. 1234-1242.
- Winter R. de, Stein B. van & Bäck T.H.W. (2021), SAMO-COBRA: a fast surrogate assisted constrained multi-objective optimization algorithm. Ishibuchi H., Zhang Q., Cheng R., Li K., Li H., Wang H. & Zhou A. (Eds.), Evolutionary Multi-Criterion Optimization. 11th International Conference on Evolutionary Multi-Criterion Optimization 28 March 2021 - 31 March 2021 no. 12654. Cham: Springer Nature Switzerland AG 2021. 270-282.
- deJesus de Araujo Rios T., Stein B. van Wollstadt P., Bäck T.H.W., Sendhoff B. & Menzel S. (2021), Exploiting local geometric features in vehicle design optimization with 3D point cloud autoencoders. In: 2021 IEEE Congress on Evolutionary Computation (CEC).: IEEE. 514-521.
- de Jesus de Araujo Rios T., Stein B. van, Bäck T.H.W., Sendhoff B. & Menzel S. (2021), Point2FFD: learning shape representations of simulation-ready 3D models for engineering design optimization. In: 2021 International Conference on 3D Vision (3DV).: IEEE. 1024-1033.
- Zeiser A.Z., Stein B van & Bäck T.H.W. (2021), Requirements towards optimizing analytics in industrial processes, Procedia Computer Science 184: 597-605.
- Winter R. de, Stein B. van & Bäck T.H.W. (2021), Ship design performance and cost optimization with machine learning. In: Bertram V. (Ed.) COMPIT'21. no. 20 Mülheim : Hamburg University of Technology. 185-196.
- Ponse K., Kononova A.V., Loleyt M. & Stein B. van (2021), Using machine learning to detect rotational and local reflectional symmetries in 2D images. In: 2021 IEEE Symposium Series on Computational Intelligence (SSCI) Proceedings. Orlando, Florida: IEEE. 01-08.
- Wang Y., Stein B. van, Bäck T.H.W. & Emmerich M.T.M. (2020), A Tailored NSGA-III for Multi-objective Flexible Job Shop Scheduling, 2020 IEEE Symposium Series on Computational Intelligence (SSCI). IEEE Symposium Series on Computational Intelligence (SSCI) 1 December 2020 - 4 December 2020: IEEE. 2746-2753.
- Rios T., Kong J., Stein B. van, Bäck T.H.W., Wollstadt P., Sendhoff B. & Menzel S. (2020), Back To Meshes: Optimal Simulation-ready Mesh Prototypes For Autoencoder-based 3D Car Point Clouds, 2020 IEEE Symposium Series on Computational Intelligence (SSCI). IEEE Symposium Series on Computational Intelligence (SSCI) 1 December 2020 - 4 December 2020: IEEE. 942-949.
- Stein N. van, Wang H. & Bäck T.H.W. (2020), Neural network design: learning from Neural Architecture Search, 2020 IEEE Symposium series on computational intelligence (SSCI). IEEE Symposium Series on Computational Intelligence (SSCI) 1 December 2020 - 4 December 2020: IEEE. 1341-1349.
- Rios T., Stein B. van, Menzel S., Bäck T.H.W., Sendhoff B. & Wollstadt P. (2020), Feature visualization for 3d point cloud autoencoders, 2020 International Joint Conference on Neural Networks (IJCNN). 2020 International Joint Conference on Neural Networks (IJCNN) 19 July 2020 - 24 July 2020: IEEE. 1-9.
- Wang Y., Stein N. van, Bäck T.H.W. & Emmerich M.T.M. (2020), Improving NSGA-III for flexible job shop scheduling using automatic configuration, smart initialization and local search, GECCO '20: Proceedings of the 2020 genetic and evolutionary computation conference companion. Genetic and Evolutionary Computation Conference (GECCO 2020) 8 July 2020 - 12 July 2020. New York: ACM. 181-182.
- Stein B. van, Wang H., Kowalczyk W.J., Emmerich M.T.M. & Bäck T.H.W. (2019), Cluster-based Kriging approximation algorithms for complexity reduction, Applied Intelligence : .
- Winter R. de, Stein B. van, Dijkman M. & Bäck T.H.W. (2019), Designing Ships Using Constrained Multi-objective Efficient Global Optimization. Nicosia G., Parda-los P., Giuffrida G., Umeton R. & Sciacca V. (Eds.), Machine Learning, Optimization, and Data Science LOD2018. The Fourth International Conference on Machine Learning, Optimization, and Data Science 13 September 2018 - 16 September 2018 no. 11331. Cham: Springer International Publishing. 191–203.
- Guo X., Stein B. van & Bäck T.H.W. (2019), A New Approach Towards the Combined Algorithm Selection and Hyper-parameter Optimization Problem, 2019 IEEE Symposium Series on Computational Intelligence (SSCI). 2019 IEEE Symposium Series on Computational Intelligence (SSCI) 6 December 2019 - 9 December 2019: IEEE. 2042-2049.
- Stein B. van, Wang H. & Bäck T.H.W. (2019), Automatic Configuration of Deep Neural Networks with Parallel Efficient Global Optimization, 2019 International Joint Conference on Neural Networks (IJCNN). 2019 International Joint Conference on Neural Networks (IJCNN) 14 July 2019 - 19 July 2019. Budapest, Hungary, Hungary: IEEE. 1-7.
- Rios T., Sendhoff B., Menzel S., Bäck T.H.W. & Stein B. van (2019), On the Efficiency of a Point Cloud Autoencoder as a Geometric Representation for Shape Optimization, 2019 IEEE Symposium Series on Computational Intelligence (SSCI). 2019 IEEE Symposium Series on Computational Intelligence (SSCI) 6 December 2019 - 9 December 2019. Piscataway, New Jersey: Institute of Electrical and Electronics Engineers (IEEE). 791-798.
- Rios T., Wollstadt P., Stein B. van, Bäck T.H.W., Xu Z., Sendhoff B. & Menzel S. (2019), Scalability of Learning Tasks on 3D CAE Models Using Point Cloud Autoencoders, 2019 IEEE Symposium Series on Computational Intelligence (SSCI). 2019 IEEE Symposium Series on Computational Intelligence (SSCI) 6 December 2019 - 9 December 2019. Piscataway, New Jersey: Institute of Electrical and Electronics Engineers (IEEE). 1367-1374.
- Stein B. van (20 September 2018), Data driven modeling & optimization of industrial processes (Dissertatie. Leiden Institute of Advanced Computer Science (LIACS), Faculty of Science, Leiden University). Supervisor(s) and Co-supervisor(s): Bäck T.H.W., Kowalczyk W.J.
- Stein B. van, Wang H., Kowalczyk W.J. & Bäck T.H.W. (2018), A Novel Uncertainty Quantification Method for Efficient Global Optimization. Medina J., Ojeda-Aciego M., Verdegay J.L., Perfilieva I., Bouchon-Meunier B. & Yager R.R. (Eds.), Information Processing and Management of Uncertainty in Knowledge-Based Systems. Applications - 17th International Conference, IPMU 2018, Cádiz, Spain, June 11-15, 2018, Proceedings, Part III. IPMU 2018 : 17th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems 11 June 2018 - 15 June 2018 no. Communications in Computer and Information Science, volume 855. Cham: Springer. 480-491.
- Stein B. van, Wang H. & Bäck T.H.W. (2018), Automatic Configuration of Deep Neural Networks with EGO.
- Rijn S.J. van, Wang H., Stein B. van & Bäck T.H.W. (2017), Algorithm configuration data mining for CMA evolution strategies, GECCO '17 Proceedings of the Genetic and Evolutionary Computation Conference. Genetic and Evolutionary Computation Conference (GECCO 2017) 15 July 2017 - 19 July 2017. New York: ACM. 737-744.
- Wang H., Stein B. van, Emmerich M.T.M. & Bäck T.H.W. (2017), Time complexity reduction in efficient global optimization using cluster kriging, GECCO '17 Proceedings of the Genetic and Evolutionary Computation Conference. Genetic and Evolutionary Computation Conference (GECCO 2017) 15 July 2017 - 19 July 2017. New York: ACM. 889-896.
- Spek T. van der, Stein B. van, Holst M. van der & Bäck T.H.W. (2017), A Multi-Method Simulation of a High-Frequency Bus Line, 2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITCS). IEEE 20th International Conference on Intelligent Transportation Systems 16 October 2017 - 19 October 2017: IEEE Xplore. 1-6.
- Wang H., Stein B. van, Emmerich M.T.M. & Bäck T.H.W. (2017), A New Acquisition Function for Bayesian optimization based on the moment-generating function. Wang Hao, Stein B. van, Emmerich Michael T.M. & Bäck T.H.W. (Eds.), 2017 IEEE International Conference on Systems, Man, and Cybernetics (SMC). 2017 IEEE International Conference on Systems, Man, and Cybernetics (SMC) 5 October 2017 - 8 October 2017. Banff, AB, Canada: IEEE. 507-512.
- Stein B. van, Leeuwen M. van & Bäck T. (2017), Local Subspace-Based Outlier Detection using Global Neighbourhoods, 2016 IEEE International Conference on Big Data (Big Data). : IEEE. 1136-1142.
- Stein B. van, Leeuwen M. van, Wang H., Purr S., Kreissl S., Meinhardt J. & Bäck T.H.W. (2017), Towards Data Driven Process Control in Manufacturing Car Body Parts, 2016 International Conference on Computational Science and Computational Intelligence CSCI. International Conference on Computational Science and Computational Intelligence (CSCI 2016) 15 December 2016 - 17 December 2016: IEEE CPS.
- Stein B. van, Hao Wang, Kowalczyk W.J., Emmerich Michael T.M. & Bäck T.H.W. (2016), Fuzzy clustering for Optimally Weighted Cluster Kriging, 2016 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). Fuzzy Systems, 2016 IEEE 24 June 2016 - 29 June 2016: IEEE. 978-1-5090-0627-4.
- Stein B. van & Kowalczyk W.J. (2016), An Incremental Algorithm for Repairing Training Sets with Missing Values. Carvalho J.P., Lesot M., Kaymak U., Vieira S., Bouchon-Meunier B. & Yager R.R. (Eds.), Information Processing and Management of Uncertainty in Knowledge-Based Systems. Information Processing and Management of Uncertainty in Knowledge-Based Systems 2016 20 June 2016 - 24 June 2016 no. 611. Switzerland: Springer International Publishing. 175-186.
- Stein B. van, Kowalczyk W.J. & Bäck T.H.W. (2016), Analysis and Visualization of Missing Value Patterns. Carvalho J., Lesot M.J., Kaymak U., Vieira S., Bouchon-Meunier B. & Yager R. (Eds.), Information Processing and Management of Uncertainty in Knowledge-Based Systems. Information Processing and Management of Uncertainty in Knowledge-Based Systems 2016 20 June 2016 - 24 June 2016 no. 611. Switzerland: Springer International Publishing. 187-198.
- Heiningen P. van, Stein B. van & Bäck T.H.W. (2016), A Framework for Evaluating Meta-models for Simulation-based Optimisation, 2016 IEEE Symposium Series on Computational Intelligence (SSCI). IEEE Symposium Series on Computational Intelligence (SSCI 2016) 6 December 2016 - 9 December 2016: IEEE.
- Stein B. van, Leeuwen M. van & Bäck T.H.W. (2016), Local Subspace-Based Outlier Detection using Global Neighbourhoods, 2016 IEEE International Conference on Big Data (Big Data). IEEE International Conference on Big Data 2016 5 December 2016 - 8 December 2016: IEEE Publishing.
- Stein B. van, Wang H., Kowalczyk W., Bäck T. & Emmerich M. (2015), Optimally Weighted Cluster Kriging for Big Data Regression. Fromont E., Bie T. de & Leeuwen M. van (Eds.), Advances in Intelligent Data Analysis XIV. 14th International Symposium, IDA 2015 22 October 2015 - 24 October 2015 no. LNCS 9385. Cham: Springer International Publishing. 310-321.
- Stein B. van, Emmerich Michael T.M. & Yang Z. (2013), 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. : Springer International Publishing. 75-89.
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