Marco Virgolin

Full Name: M. Virgolin
Function(s): Scientific Staff Member

Email: M.Virgolin@cwi.nl
Telephone: +31 20 592 4017
Room: M277b
Department(s): Life Sciences and Health
Homepage: https://marcovirgolin.github.io/

Biography

Marco Virgolin is a junior researcher (tenure track) at CWI. He works on eXplainable AI (XAI), most notably by means of Symbolic Regression to obtain interpretable models. He is also interested in medical applications of machine learning, conversational AI, and human-machine interaction. Before, Marco was a postdoc with Mattias Wahde at Chalmers University of Technology, a PhD student with Peter A. N. Bosman and Tanja Alderliesten at Centrum Wiskunde & Informatica, and a master student with Alberto Bartoli and Eric Medvet at the Machine Learning Lab of the University of Trieste.

Publications

Wang, Z, Virgolin, M, Balgobind, B.V, Dijk, van, I.W.E.M, Smith, S.A, Howell, R.M, … Alderliesten, T. (2022). Validation and comparison of radiograph-based organ dose reconstruction approaches for Wilms tumor radiation treatment plans. Advances in Radiation Oncology, 7(6), 101015.1–101015.12. doi:10.1016/j.adro.2022.101015
Virgolin, M, & Bosman, P.A.N. (2022). Coefficient mutation in the gene-pool optimal mixing evolutionary algorithm for symbolic regression. In GECCO 2022 Companion - Proceedings of the 2022 Genetic and Evolutionary Computation Conference (pp. 2289–2297). doi:10.1145/3520304.3534036
Uriot, T, Virgolin, M, Alderliesten, T, & Bosman, P.A.N. (2022). On genetic programming representations and fitness functions for interpretable dimensionality reduction. In GECCO 2022 - Proceedings of the 2022 Genetic and Evolutionary Computation Conference (pp. 458–466). doi:10.1145/3512290.3528849
Liu, D, Virgolin, M, Alderliesten, T, & Bosman, P.A.N. (2022). Evolvability degeneration in multi-objective genetic programming for symbolic regression. In GECCO 2022 - Proceedings of the 2022 Genetic and Evolutionary Computation Conference (pp. 973–981). doi:10.1145/3512290.3528787
Virgolin, M, Alderliesten, T, Witteveen, C, & Bosman, P.A.N. (2021). Improving model-based Genetic Programming for Symbolic Regression of small expressions. Evolutionary Computation, 29(2), 211–237. doi:10.1162/evco_a_00278
Den Ottelander, T, Dushatskiy, A, Virgolin, M, & Bosman, P.A.N. (2021). Local Search is a remarkably strong baseline for Neural Architecture Search. In Evolutionary Multi-Criterion Optimization (pp. 465–479). doi:10.1007/978-3-030-72062-9_37
Virgolin, M, Wang, Z, Balgobind, B.V, Dijk, van, I.W.E.M, Wiersma, J, Kroon, P. S., … Alderliesten, T. (2020). Surrogate-free machine learning-based organ dose reconstruction for pediatric abdominal radiotherapy. Physics in Medicine and Biology, 65(24). doi:10.1088/1361-6560/ab9fcc
Virgolin, M, Wang, Z, Alderliesten, T, & Bosman, P.A.N. (2020). Machine learning for the prediction of pseudorealistic pediatric abdominal phantoms for radiation dose reconstruction. Journal of Medical Imaging, 7(4). doi:10.1117/1.JMI.7.4.046501
Virgolin, M. (2020, June 8). Design and application of gene-pool optimal mixing evolutionary algorithms for genetic programming. SIKS Dissertation Series.
Virgolin, M, Wang, Z, Alderliesten, T, & Bosman, P.A.N. (2020). Machine learning for automatic construction of pediatric abdominal phantoms for radiation dose reconstruction. In Medical Imaging 2020: Imaging Informatics for Healthcare, Research, and Applications. doi:10.1117/12.2548969