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
No vacancies currently.
News
Three GECCO Awards for AI in healthcare
Researchers from CWI and other organizations 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 for a team of CWI, TU Delft and LUMC, the SIGEVO Dissertation Award 2021 for CWI’s Margo Virgolin and a Silver HUMIES award for CWI and Amsterdam UMC.
Funding for Explainable Artificial Intelligence for medical professionals
LUMC, Amsterdam UMC, and CWI have received funding from the Gieskes-Strijbis Fund to enhance the understanding of AI by doctors.
NETWORKS consortium awarded €1M from EU COFUND for postdoc programme
After receiving the COFUND grant from the Horizon 2020 programme for 14 PhD positions, NETWORKS has been granted a COFUND grant of €1.0 million to appoint 14 postdoctoral researchers for 2 years.
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”.
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
- Solon Pissis
- Renzo Scholman
- Evi Sijben
- Leen Stougie
- Michelle Sweering
Associated Members
Publications
-
Ayad, L.A.K, Badkobeh, G, Fici, G, Heliou, A, & Pissis, S. (2021). Constructing antidictionaries of long texts in output-sensitive space. Theory of Computing Systems, 65, 777–797. doi:10.1007/s00224-020-10018-5
-
Chen, L, Megow, N, Rischke, R, Stougie, L, & Verschae, J. (2021). Optimal algorithms for scheduling under time-of-use tariffs. Annals of Operations Research. doi:10.1007/s10479-021-04059-3
-
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
-
Maree, S.C. (2021, March 17). Model-based evolutionary algorithms for finding diverse high-quality solutions : with an application in brachytherapy for prostate cancer.
-
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
-
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
-
Bernardini, G, Chen, H, Conte, A., Grossi, R, Loukides, G, Pisanti, N, … Sweering, M.J.M. (2020). Combinatorial algorithms for string sanitization. ACM Transactions on Knowledge Discovery from Data, 15(1). doi:10.1145/3418683
-
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
-
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
-
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.
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
-
Statistical Models for Structural Genetic Variants in the Genome of the Netherlands
-
Algorithms for PAngenome Computational Analysis (ALPACA)
-
Fast, accurate, and insightful brachytherapy treatment planning for cervical cancer through artificial intelligence (Brachytherapy treatment)
-
Distributed and Automated Evolutionary Deep Architecture Learning with Unprecedented Scalability (DAEDALUS)
-
Evolutionary eXplainable Artificial Medical INtelligence Engine (EXAMINE)
-
Fusible Evolutionary Deep Neural Network Mixture Learning from Distributed Data for Robust Medical Image Analysis (FEDMix)
-
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)
-
Networks
-
Uitlegbare kunstmatige intelligentie (None)
-
Optimization for and with Machine Learning (OPTIMAL)
-
Pan-genome Graph Algorithms and Data Integration (PANGAIA)
-
Transparent, Reliable and Unbiased Smart Tool for AI (TRUST-AI)
Related partners
-
AMC Medical Research
-
CNRS
-
Elekta Limited
-
European Molecular Biology Laboratory
-
INRIA
-
Universita di Pisa
-
Xomnia
-
Academisch Medisch Centrum
-
Biomedical Imaging Group Rotterdam
-
Erasmus Universiteit Rotterdam
-
Geneton S.R.O.
-
Heinrich-Heine-Universitaet Dusseldorf
-
Illumina Cambridge
-
Institut Pasteur
-
Leids Universitair Mediach Centrum
-
Univerzita Komenskeho V Bratislave
-
Universiteit Leiden
-
Universitaet Bielefeld
-
Universita' Degli Studi di Milano-Bicocca
-
Universiteit van Tilburg