Tanja Alderliesten
- Full Name
- Dr. T. Alderliesten
- Function(s)
- Associate Professor - Leids Universitair Medisch Centrum, Researcher - Leids Universitair Medisch Centrum
- Tanja.Alderliesten@cwi.nl
- Telephone
- +31 20 592 4265
- Room
- M276
- Department(s)
- Life Sciences and Health
Biography
Dr. Tanja Alderliesten is currently employed as an associate professor at the department of Radiation Oncology of the Leiden University Medical Center (LUMC) located in Leiden, the Netherlands. The focus of her research is on translational research and associated development of state-of-the-art methods and techniques from the field of mathematics and computer science, and in particular AI techniques, to radiation oncology. After Tanja obtained her PhD degree in (Medical) Computer Science at the Image Sciences Institute (ISI), Utrecht University, the Netherlands, she was affiliated with The Netherlands Cancer Institute as a postdoctoral researcher; at first with the department of Radiology and later with the department of Radiation Oncology. After eight years she joined the Amsterdam UMC, University of Amsterdam, department of Radiation Oncology as a senior researcher. Her research often includes image-processing and computer-science methods and techniques such as segmentation, biomedical modeling, simulation, and deformable image registration, but also includes algorithmic design, optimization, machine learning, and (explainable) AI techniques in general. She is experienced in research concerning image-guidance systems (i.e., optical infrared image navigation and surface imaging systems), the improvement of radiotherapy for esophageal cancer patients, 3D radiation dose reconstruction for children with cancer treated in the pre CT-based radiotherapy planning era as well as advanced optical imaging of the esophagus during cancer therapy, deformable breast image registration, and automated brachytherapy planning.
Publications
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Maree, S.C, Thierens, D, Alderliesten, T, & Bosman, P.A.N. (2021). Two-Phase Real-Valued Multimodal Optimization with the Hill-Valley Evolutionary Algorithm. In Metaheuristics for Finding Multiple Solutions (pp. 165–189). doi:10.1007/978-3-030-79553-5_8
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Bouter, P.A, Alderliesten, T, & Bosman, P.A.N. (2021). GPU-accelerated parallel Gene-pool Optimal Mixing applied to multi-objective Deformable Image Registration. In Proceedings of the 2021 IEEE Congress on Evolutionary Computation (pp. 2539–2548). doi:10.1109/CEC45853.2021.9504840
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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
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van der Meer, M.C, Bosman, P.A.N, Niatsetski, Y, Alderliesten, T, Pieters, B.R, & Bel, A. (2021). Robust optimization for HDR prostate brachytherapy applied to organ reconstruction uncertainty. Physics in Medicine and Biology, 66(5). doi:10.1088/1361-6560/abe04e
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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
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Maree, S.C, Alderliesten, T, & Bosman, P.A.N. (2020). Ensuring smoothly navigable approximation sets by Bézier curve parameterizations in evolutionary bi-objective optimization. In Lecture Notes in Computer Science/Lecture Notes in Artificial Intelligence. doi:10.1007/978-3-030-58115-2_15
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Deist, T.M, Maree, S.C, Alderliesten, T, & Bosman, P.A.N. (2020). Multi-objective optimization by uncrowded hypervolume gradient ascent. In Lecture Notes in Computer Science/Lecture Notes in Artificial Intelligence. doi:10.1007/978-3-030-58115-2_13
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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
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Bouter, P.A, Maree, S.C, Alderliesten, T, & Bosman, P.A.N. (2020). Leveraging conditional linkage models in gray-box optimization with the real-valued gene-pool optimal mixing evolutionary algorithm. In GECCO 2020 - Proceedings of the 2020 Genetic and Evolutionary Computation Conference (pp. 603–611). doi:10.1145/3377930.3390225
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Bouter, P.A, Alderliesten, T, & Bosman, P.A.N. (2020). Achieving highly scalable evolutionary real-valued optimization by exploiting partial evaluations. Evolutionary Computation, 1–27. doi:10.1162/evco_a_00275