Arkadiy Dushatskiy

Volledige naam

A. Dushatskiy

E-mail

Arkadiy.Dushatskiy@cwi.nl

Telefoon

+31 20 592 4073

Kamer

M231

Afdeling(en)

Evolutionary Intelligence

Functie(s)

Researcher

Biography

Arkadiy Dushatskiy did a Bachelor in Applied Mathematics and Informatics at the Higher School of Economics, Moscow, Russia (2012-2016). He continued his studies in Applied Mathematics and Informatics with a specialization High-Performance Computing at the Moscow State University (2016-2018). His MSc thesis topic was performance analysis of Convolutional Neural Networks running on GPUs. He started in September 2018 as a PhD student at CWI where his main research topic is a combination of Evolutionary Algorithms and Deep Learning applied to Medical Image Analysis.

Chebykin, A., Dushatskiy, A., Alderliesten, T., & Bosman, P. (2023). Shrink-Perturb improves architecture mixing during Population Based Training for Neural Architecture Search. In Proceedings of the 26th European Conference on Artificial Intelligence, ECAI 2023 (pp. 381–388). doi:10.3233/FAIA230294
Dushatskiy, A. (2023, September 6). Expensive optimization with model-based evolutionary algorithms applied to medical image segmentation using deep learning. SIKS Dissertation Series.
Dushatskiy, A., Chebykin, A., Alderliesten, T., & Bosman, P. (2023). Multi-objective population based training. In Proceedings of the 40th International Conference on Machine Learning, PMLR (pp. 8969–8989).
Dushatskiy, A., Alderliesten, T., & Bosman, P. (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
Dushatskiy, A., Lowe, G., Bosman, P., & Alderliesten, T. (2022). Data variation-aware medical image segmentation. In Medical Imaging 2022: Image Processing. doi:10.1117/12.2608611
Bosma, M., Dushatskiy, A., Grewal, M., Alderliesten, T., & Bosman, P. (2022). Mixed-block neural architecture search for medical image segmentation. In Medical Imaging 2022: Image Processing. doi:10.1117/12.2611428
Den Ottelander, T., Dushatskiy, A., Virgolin, M., & Bosman, P. (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
Dushatskiy, A., Mendrik, A., Bosman, P., & Alderliesten, T. (2020). Observer variation-aware medical image segmentation by combining deep learning and surrogate-assisted genetic algorithms. In Progress in Biomedical Optics and Imaging - Proceedings of SPIE. doi:10.1117/12.2547459
Dushatskiy, A., Alderliesten, T., Mendrik, A., & Bosman, P. (2019). Convolutional neural network surrogate-assisted GOMEA. In Proceedings of the 2019 Genetic and Evolutionary Computation Conference (pp. 753–761). doi:10.1145/3321707.3321760

Best Paper award at the International Conference on EMO (2021)

Optimization for and with Machine Learning (OPTIMAL2)

Arkadiy Dushatskiy

Biography

Publications

Awards

Current projects with external funding

Arkadiy Dushatskiy

Biography

Publications

Awards

Current projects with external funding

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