Elena Raponi
Assistant professor
- Name
- Dr. E. Raponi
- Telephone
- 071 5272727
- e.raponi@liacs.leidenuniv.nl
Elena Raponi is assistant professor in Bayesian Optimization at the Leiden Institute of Advanced Computer Science (LIACS). She previously held a position at LIACS as a postdoctoral researcher in the Natural Computing Research Group. She received her PhD in Sciences and Technology, Mathematics, from the University of Camerino, Italy, in May 2021.
Elena's research focuses on the development of analytical and numerical modeling techniques for the optimization of geometries and materials in structural mechanics. She has particular expertise in surrogate-based and high-dimensional (Bayesian) optimization in continuous domains.
Assistant professor
- LIACS
- Natural Computing
- Olarte Rodriguez I., Jankovic A., Bäck T.H.W & Raponi E. (2026), Does dimensionality reduction via random projections preserve landscape features?. Trujillo L. & Hu T. (Eds.), GECCO '26: Proceedings of the Genetic and Evolutionary Computation Conference. GECCO '26: Genetic and Evolutionary Computation Conference Centro Internacional de Convenciones CIC-ANDE San Jose Costa Rica 13 July 2026 - 17 July 2026. New York, U.S.A.: Association for Computing Machinery. 919-927.
- Westra J., Olarte Rodriguez I., Stein N. van, Bäck T.H.W. & Raponi E. (2026), Investigating the interplay of parameterization and optimizer in gradient-free topology optimization: a cantilever beam case study. García-Sánchez P., Díaz Álvarez J. & Murphy A. (Eds.), Applications of Evolutionary Computation (EvoApplications 2026). 29th European Conference, EvoApplications 2026 8 April 2026 - 10 July 2026. Lecture Notes in Computer Science no. 16525. Cham: Springer. 473–488.
- Preintner T.P., Yuan W., Huang Q., König A., Bäck T.H.W., Raponi E. & Stein N. van (2025), Why are you wrong?: Counterfactual explanations for language grounding with 3D objects, 2025 International Joint Conference on Neural Networks (IJCNN). 2025 International Joint Conference on Neural Networks (IJCNN) 30 June 2025 - 5 July 2025: IEEE. 1-9.
- Preintner T.P., Yuan W., König A., Bäck T.H.W., Raponi E. & Stein N. van (2025), EvoCAD: evolutionary CAD code generation with vision language models, 2025 IEEE 37th International Conference on Tools with Artificial Intelligence (ICTAI). 2025 37th International Conference on Tools with Artificial Intelligence (ICTAI) 3 November 2025 - 5 November 2025: IEEE. 504-511.
- Raponi E., Olarte Rodriguez I. & Stein N. van (2025), Global sensitivity analysis is not always beneficial for evolutionary computation: a study in engineering design. In: Stein N. van & Kononova A.V. (Eds.), Explainable AI for evolutionary computation. Natural Computing Series. Singapore: Springer. 13-40.
- Olarte Rodriguez I., Serhat G., Bujny M., Duddeck F., Bäck T.H.W. & Raponi E. (2025), Optimization Is not enough: why problem formulation deserves equal attention. 17th International Conference on Evolutionary Computation Theory and Applications (ECTA 2025) 22 October 2025 - 24 October 2025.
- Olarte Rodríguez I., Santoni M.L., Duddeck F., Doerr C., Bäck T.H.W. & Raponi E. (2025), MECHBench: a set of black-box optimization benchmarks originated from structural mechanics. ArXiv e-prints. [working paper].
- Huang H.-M., Raponi E., Duddeck F., Menzel S. & Bujny M. (2024), Topology optimization of periodic structures for crash and static load cases using the evolutionary level set method, Optimization and Engineering 25: 1597-1630.
- Stein N. van & Raponi E. (2022), GSAreport: easy to use global sensitivity reporting, The Journal of Open Source Software 7(78): 4721.
- Yarkoni S., Raponi E., Bäck T.H.W. & Schmitt S. (2022), Quantum annealing for industry applications: introduction and review, Reports on Progress in Physics 85(10): 104001.
- 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.
- Stein B. van & Raponi E. (2022), GSAreport: easy to use global sensitivity reporting, Journal of Open Source Software 7(78): 4721.