Description
Leader of the group Life Sciences and Health: Leen Stougie.
The CWI Life Sciences and Health (LSH) group is a group of computer scientists and mathematicians whose research focus is on the analysis and design of models and algorithms as well as their direct application to important challenges in the LSH domain.
On the application side, our present team of researchers has expertise in, e.g., computational genomics, medical informatics, computational phylogenetics, and biological network analysis. On the methodological side, we come from different backgrounds, e.g., computational intelligence, computational data science, and operations research. Methodologically, we develop new theories, models, algorithms and decision support tools, for problems that arise mostly in collaboration with experimental biologists and medical experts. We actively collaborate in projects with academic hospitals, biological and biochemical research institutes, and industry. Click here for more information about our group structure.
The LSH group participates in the INRIA International team ERABLE.
Seminars: The LSH group organizes a biweekly seminar.
Watch our group video to get a glimpse of our activities.
Vacancies
PhD students on evolutionary algorithms, deep learning, and medical applications
Centrum Wiskunde & Informatica (CWI, the Dutch national research institute for mathematics and computer science) located in Amsterdam, and the Leiden University Medical Centre (LUMC) together have 3 vacancies for fully funded PhD students, on evolutionary algorithms and deep learning with applications in medicine.
Doctoral Student Position on Combinatorial Algorithms on Strings and Graphs
PhD student, on the subject of Combinatorial Algorithms on Strings and Graphs with Applications in Bioinformatics.
News
Best Paper Award for LSH researchers at EMO 2021
Researchers from CWI's Life Sciences & Health group were awarded a Best Paper Award at EMO 2021 for their paper “Local search is a remarkably strong baseline for neural architecture search”.
Evolutionary Algorithms for high-quality solutions
It is not uncommon that the solution obtained by an algorithm is not as desired. To overcome this, Stef Maree's PhD research focuses on optimization algorithms for finding not just one solution, but a set of diverse high-quality solutions.
Marie Curie ITN grant awarded to ALPACA network
Recently, CWI and others were awarded an EU Marie Skłodowska-Curie Innovative Training Networks (ITN) consortium grant for ALPACA –'Algorithms for PAngenome Computational Analysis'. The research project involves a total funding of 3.67 million euros.
CWI designs algorithms for the improvement of Genetic Programming
Marco Virgolin of CWI’s Life Sciences & Health group has researched ways to improve the efficiency and effectiveness of Genetic Programming (GP). He defends his thesis ‘Design and Application of Gene-Pool Optimal Mixing Evolutionary Algorithms for Genetic Programming’ on Monday 6 June.
Members
- Matthias Bakker
- Giulia Bernardini
- Martijn Bosma
- Peter Bosman
- Anton Bouter
- Aleksandr Chebykin
- Nick Cleintuar
- Joost Commandeur
- Timo Deist
- Arkadiy Dushatskiy
- Monika Grewal
- Arthur Guijt
- Damy Ha
- Joe Harrison
- Nando Kartoredjo
- Jan Karel Lenstra
- Vincent Luo
- Tom den Ottelander
- Solon Pissis
- Evi Sijben
- Leen Stougie
- Michelle Sweering
Associated Members
Publications
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Maree, S.C. (2021, March 17). Model-based evolutionary algorithms for finding diverse high-quality solutions : with an application in brachytherapy for prostate cancer.
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de Bontridder, K.M.J, Halldórsson, B.V, Halldórsson, M.M, Hurkens, C.A.J, Lenstra, J.K, Ravi, R, & Stougie, L. (2021). Local improvement algorithms for a path packing problem: A performance analysis based on linear programming. Operations Research Letters, 49(1), 62–68. doi:10.1016/j.orl.2020.11.005
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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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Bernardini, G, Chen, H, Conte, A., Grossi, R, Loukides, G, Pisanti, N, … Sweering, M. (Michelle). (2020). Combinatorial algorithms for string sanitization. ACM Transactions on Knowledge Discovery from Data, 15(1). doi:10.1145/3418683
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Bernardini, G, Conte, A., Gourdel, G., Grossi, R, Loukides, G, Pisanti, N, … Sweering, M.J.M. (2020). Hide and mine in strings: Hardness and algorithms. In 20th IEEE International Conference on Data Mining (pp. 924–929). doi:10.1109/ICDM50108.2020.00103
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Abedin, P, Ganguly, A, Pissis, S, & Thankachan, S.V. (2020). Efficient data structures for range shortest unique substring queries†. Algorithms, 13(11), 1–9. doi:10.3390/a13110276
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Pyne, S, Ray, S, Gurewitsch, R., & Aruru, M. (2020). Transition from Social Vulnerability to Resiliency vis-à-vis COVID-19. Statistics and Applications, 18(1), 197–208.
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Bernardini, G, Loukides, G, Pissis, S, Sweering, M.J.M, Chen, H, Pisanti, N, & Stougie, L. (2020). String Sanitization Under Edit Distance. In Leibniz International Proceedings in Informatics (pp. 7:1–7:14). doi:10.4230/LIPIcs.CPM.2020.
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Virgolin, M. (2020, June 8). Design and application of gene-pool optimal mixing evolutionary algorithms for genetic programming. SIKS Dissertation Series.
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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
Software
VirtualLeaf: a modeling framework for plant tissue morphogenesis
VirtualLeaf is a computer modeling framework for the simulation of plant tissue morphogenesis, i.e., the biological development of an organism's shape
Current projects with external funding
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Improving Childhood Cancer Care when Parents Cannot be There - Reducing Medical Traumatic Stress in Childhood Cancer Patients by Bonding with a Robot Companion ()
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Enhancing protein-drug binding prediction
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Statistical Models for Structural Genetic Variants in the Genome of the Netherlands
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Algorithms for PAngenome Computational Analysis (ALPACA)
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Fast, accurate, and insightful brachytherapy treatment planning for cervical cancer through artificial intelligence (Brachytherapy treatment)
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Distributed and Automated Evolutionary Deep Architecture Learning with Unprecedented Scalability (DAEDALUS)
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Evolutionary eXplainable Artificial Medical INtelligence Engine (EXAMINE)
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Fusible Evolutionary Deep Neural Network Mixture Learning from Distributed Data for Robust Medical Image Analysis (FEDMix)
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Multi-Objective Deformable Image Registration (MODIR) – An Innovative Synergy of Multi-Objective Optimization, Machine Learning, and Biomechanical Modeling for the Registration of Medical Images with (MODIR)
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Networks
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Optimization for and with Machine Learning (OPTIMAL)
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Pan-genome Graph Algorithms and Data Integration (PANGAIA)
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Transparent, Reliable and Unbiased Smart Tool for AI (TRUST-AI)
Related partners
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AMC Medical Research
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CNRS
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Elekta Limited
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European Molecular Biology Laboratory
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INRIA
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Universita di Pisa
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Xomnia
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Academisch Medisch Centrum
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Biomedical Imaging Group Rotterdam
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Erasmus Universiteit Rotterdam
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Geneton S.R.O.
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Heinrich-Heine-Universitaet Dusseldorf
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Illumina Cambridge
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Institut Pasteur
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Leids Universitair Mediach Centrum
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Technische Universiteit Delft
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Univerzita Komenskeho V Bratislave
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Universiteit Leiden
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Universitaet Bielefeld
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Universita' Degli Studi di Milano-Bicocca
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Universiteit van Tilburg