…expands research from the Virtual Physiological Human initiative. Unlike fully data-driven machine learning data science approaches a rich assortment of complementary modelling approaches will be utilized, including 3D (e.g. Navier-Stokes solvers, Lattice Boltzmann approaches), 1D and 0D models of fluid mechanics, Finite Element Analysis, Growth and Remodelling frameworks and Agent-Based Models of structural mechanics and others. This research area will be concerned with the development of fundamental modelling tools and workflows, by simulating physiology and predicting multi-morbidity through integration with data science technologies.
This team will also investigate applications of in-silico clinical trials which concerns modelling and simulation of devices, drugs and interventions to be evaluated over trial populations covering complete target treatment populations, hugely reducing the time and cost of traditional clinical trials through the use of computing. Significant research questions remain around these approaches, particularly over missing and inaccurate data, which could be addressed by combining modelling and simulation approaches with Data Science and Healthcare Informatics for input data distribution and accuracy. Furthermore, biology, biochemistry, physics models can be developed in the attempts to explain AI decisions, to improve clinical acceptance largely black-box deep learning approaches of data science.
Zbigniew R. Struzik is a physicist within the area of applied, interdisciplinary and statistical physics and computer science, with interests including Biophysics and Medical Physics, Complex Systems Physics and the Physics of Living Systems. He holds a Doctor degree from the Faculty of Mathematics, Computer Science, Physics and Astronomy of the University of Amsterdam (1996). His engineering background stems from his Master of Science degree in Fundamental Problems of Technology from the Faculty of Technical Physics and Applied Mathematics of Warsaw University of Technology (1986). He has worked at a number of notable universities and scientific institutes in the Netherlands, Japan and recently Poland (incl. University of Amsterdam, Centrum Wiskunde en Informatica, the University of Tokyo, Riken Brain Science Institute, University of Warsaw).
Maciej Szymkowski received his B.Sc. in 2017 and M.Sc. in 2018 from Białystok University of Technology, Faculty of Computer Science. Right now, he is PhD candidate at the same Faculty. His main research interests are related to biometrics, signal processing and analysis, processes simulations, artificial intelligence and machine learning. He gained experience not only at universities but also in diversified companies (where he was also responsible for software engineering tasks alongside those related to research). In his free time he likes reading books (especially biographies of famous scientists) and taking a long walk.
Her scientific journey started in Scotland, at the University of Edinburgh where she graduated from in 2021 with a Master of Physics degree. The subject of her thesis was modelling a response of a single cell to the use of fractionated radiotherapy in order to induce the abscopal effect in cancer treatment. She joined Sano Modelling and Simulation Team as a PhD student and is currently working on modelling venous flow in patients presenting with deep vein thrombosis of the lower limb.
Aleksandra is pursuing MSc degree in Data Science at AGH. Her bachelor thesis was related to predicting epileptic seizures using neural networks. In Sano she is a member of Computer Vision Data Science team. Currently doing research on the application of statistical and machine learning models in neuroscience as her master's thesis project. Privately, she is all about getting away from the city, trekking in the mountains, learning rock climbing and nature travelling (especially delighted with Scandinavia).
Aleksandra is pursuing MSc degree in Data Science at AGH. Her bachelor thesis was related to predicting epileptic seizures using neural networks. In Sano she is a member of Computer Vision Data Science team. Currently doing research on the application of statistical and machine learning models in neuroscience as his master's thesis project. Privately, she is all about getting away from the city, trekking in the mountains, learning rock climbing and nature travelling (especially delighted with Scandinavia).
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Sensitivity analysis of a model of lower limb haemodynamics Workshop
2022.
Sensitivity analysis of a model of lower limb haemodynamics Conference
International Conference on Computational Science 2022 0000.
Modularity promotes excitation-inhibition balance in synaptically coupled neuronal networks Workshop
0000.
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