Senior Principal Research Scientist, Neuroscience Research Australia and Professor, University of New South Wales (UNSW), Sydney, Australia
From a background in biomechanical engineering, the focus of my research is on how the nervous system responds to mechanical loading – both those loads which cause injury and those which are part of normal function. Our approach spans the range from the whole body (e.g. crash testing) through individual tissues, and down to a molecular scale – integrating the data to gain an understanding of the detailed mechanisms of nervous system injury and mechanically influenced physiological functions.
I use novel imaging techniques such as tagged MRI and MR elastography to study a diverse range of in vivo mechanical functions, including obstructive sleep apnoea, muscle injury, biomechanical properties of tissues during growth and development, neural tissue mechanics, syringomyelia and hydrocephalus.
Broad Research Areas:
Injury, Biomedical Engineering, Neurotrauma, Imaging / Radiology, Population Health, Biomechanics
Natalia A. Trayanova
Murray B. Sachs Professor of Biomedical Engineering and Medicine, Professor of Medicine, Professor of Applied Mathematics and Statistics
Department of Biomedical Engineering, Johns Hopkins University, Baltimore, USA
Cardiac electrophysiology; Computational cardiology; Machine learning in cardiology; Artificial Intelligence; Arrhythmia therapies; Sudden cardiac death
Our lab conducts research in computational cardiology, developing and translating to the clinic personalized computational digital-twin technologies and machine learning approaches for the prognosis and treatment of cardiovascular disease. Our models of patients’ hearts realistically represent the functioning of the diseased organ. They are used, in combination with machine learning, for the discovery of mechanistic relationships and features that are indicative of the trajectory of the patient’s heart condition. Our work focuses on the prediction of risk of adverse cardiovascular events so that patient lives can be better protected. We also design treatment interventions that are optimal for the patient’s unique heart condition, maximizing procedure success and minimizing the potential for disease recurrence and rehospitalization.
Director of Research INRIA (French National Institut(ion) for Research in Computer Science and Control,), Rocquencourt, France
Development of mathematical models and software to simulate tissue organization processes such as regeneration of tissues and tumour growth on the computer to eventually archieve therapy optimizations.
Dirk Drasdo’s main research topic is modeling of multi-cellular tissue organization. He established center-based models of growing tissues in various applications. In these models cells are mimicked as individual agents, parameterized by measurable biophysical and biokinetic parameters. More recently he and his group established a process chain parameterizing single-cell-based tissue models out of image data in collaboration with the group of J.G. Hengstler (IfaDo). With a modeling guided experimental strategy they were able to predict a previously unrecognized order mechanism during liver regeneration and very recently to deduce the necessity for an ammonia sink mechanism after drug induced liver damage which subsequently could be identified.
Professor of Computational Science and Engineering, Informatics Institute, University of Amsterdam
I am affiliated to the Computational Science Lab. Our motto is:
“Nature is a Complex System that processes information. Computational Science aims to make the complexity of those systems tractable.“
We pursue five research themes, Complex Systems Theory, Urban Complex Systems, Computational Finance, Computational Biology, and Computational BioMedicine. The latter has my main interest, though I try to contribute where possible to other themes as well.