Description
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 monthly seminar.
Watch our group video to get a glimpse of our activities.
Vacancies
No vacancies currently.
News
Two Best Paper Awards for CWI at GECCO
Researchers from CWI and Leiden University were awarded two best paper awards at the ACM Genetic and Evolutionary Computation COnference (GECCO) 2022.
Best Italian PhD thesis prize for CWI's Giulia Bernardini
On 14 September 2021 the Italian chapter of EATCS awarded a prize for the Best Italian PhD Thesis in Theoretical Computer Science ex aequo to Giulia Bernardini (CWI) and Francesco Dagnino (University of Genova).
JoLEA: new lecture series on cutting-edge research on Evolutionary Algorithms
CWI, Leiden University, Utrecht University and VU Amsterdam proudly present the new Joint Lectures on Evolutionary Algorithms (JoLEA) series: top-quality lectures on EAs.
Three GECCO Awards for AI in healthcare
CWI researchers and partners won three awards for AI in healthcare at the ACM Genetic and Evolutionary Computation COnference (GECCO) 2021: the Best Paper Award in the Genetic Algorithms track, the SIGEVO Dissertation Award 2021 and a Silver HUMIES Award.
Members
- Peter Bosman
- Anton Bouter
- Aleksandr Chebykin
- Nick Cleintuar
- Joost Commandeur
- Arkadiy Dushatskiy
- Esteban Gabory
- Monika Grewal
- Arthur Guijt
- Joe Harrison
- Jan Karel Lenstra
- Dazhuang Liu
- Vincent Luo
- Mafalda Malafaia
- Joas Mulder
- Solon Pissis
- Renzo Scholman
- Evi Sijben
- Leen Stougie
- Michelle Sweering
- Marco Virgolin
- Wiktor Zuba
Associated Members
- Sanne Abeln
- Tanja Alderliesten
- Georgios Andreadis
- Jasper van Bemmelen
- Giulia Bernardini
- Timo Deist
- Leah Dickhoff
- Damy Ha
- Ernst Harderwijk
- Xiongbin Kang
- Gerard Kindervater
- Geert Klop
- Evangelos Kostoulas
- Hoang Luong
- Sumanta Ray
- Cedric Rodriguez
- Jelmen Roorda
- Thalea Schlender
- Alexander Schönhuth
- Dustin van Weersel
Publications
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Maree, S.C, Alderliesten, T, & Bosman, P.A.N. (2022). Uncrowded hypervolume-based multiobjective optimization with gene-pool optimal mixing. Evolutionary Computation, 30(3), 329–353. doi:10.1162/evco_a_00303
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Scholman, R.J, Bouter, P.A, Dickhoff, L.R.M, Alderliesten, T, & Bosman, P.A.N. (2022). Obtaining smoothly navigable approximation sets in bi-objective multi-modal optimization. In Proceedings of PPSN 2022 (pp. 247–262). doi:10.1007/978-3-031-14721-0_18
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Harrison, J, Alderliesten, T, & Bosman, P.A.N. (2022). Gene-pool Optimal Mixing in Cartesian Genetic Programming. In Proceedings of PPSN 2022 (pp. 19–32). doi:10.1007/978-3-031-14721-0_2
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Dushatskiy, A, Alderliesten, T, & Bosman, P.A.N. (2022). Heed the noise in performance evaluations in neural architecture search. In GECCO 2022 Companion - Proceedings of the 2022 Genetic and Evolutionary Computation Conference (pp. 2104–2112). doi:10.1145/3520304.3533995
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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
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Chebykin, A, Alderliesten, T, & Bosman, P.A.N. (2022). Evolutionary neural cascade search across supernetworks. In GECCO 2022 - Proceedings of the 2022 Genetic and Evolutionary Computation Conference (pp. 1038–1047). doi:10.1145/3512290.3528749
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Guijt, A, Thierens, D, Alderliesten, T, & Bosman, P.A.N. (2022). Solving multi-structured problems by introducing linkage kernels into GOMEA. In GECCO 2022 - Proceedings of the 2022 Genetic and Evolutionary Computation Conference (pp. 703–711). doi:https://dl.acm.org/doi/pdf/10.1145/3512290.3528828
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Bouter, P.A, & Bosman, P.A.N. (2022). GPU-accelerated parallel gene-pool optimal mixing in a gray-box optimization setting. In GECCO 2022 - Proceedings of the 2022 Genetic and Evolutionary Computation Conference (pp. 675–683). doi:10.1145/3512290.3528797
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Charalampopoulos, P, Pissis, S, & Radoszewski, J. (2022). Longest palindromic substring in sublinear time. In Leibniz International Proceedings in Informatics. doi:10.4230/LIPIcs.CPM.2022.20
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Bernardini, G, Conte, A., Gabory, E. (Esteban), Grossi, R, Loukides, G, Pissis, S, … Sweering, M.J.M. (2022). On Strings Having the Same Length-k Substrings. In 33rd Annual Symposium on Combinatorial Pattern Matching (pp. 16:1–16:17). doi:10.4230/LIPIcs.CPM.2022.16
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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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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Networks COFUND postdocs
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Uitlegbare kunstmatige intelligentie (None)
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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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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