Doctoral candidate: Chadi Barakat
Dissertation title: Design and Evaluation of Parallel and Scalable Machine Learning Research in Biomedical Modelling Applications
Opponents:
Dr. Róbert Lovas, Deputy Director Laboratory of Parallel and Distributed Systems, Institute for Computer Science and Control (SZTAKI), Hungary
Dr. Maximilian Franz Schulze-Hagen, Senior Radiology Consultant, Städtisches Klinikum Solingen, Germany
Advisor: Dr. Morris Riedel, Professor at Faculty of Industrial Engineering, Mechanical Engineering and Computer Science, University of Iceland and Head of High Productivity Data Processing research group at the Juelich Supercomputing Centre, Germany
Also in the doctoral committee:
Dr. Sigurður Brynjólfsson, Professor at the Faculty of Industrial Engineering, Mechanical Engineering and Computer Science, University of Iceland
Dr. Andreas Schuppert, Professor and founding director of the Joint Research Center for Computational Biomedicine at RWTH Aachen University, Germany
Dr. Sebastian Fritsch, Medical Doctor at the Department of Intensive Care Medicine and Intermediate Care, University Hospital RWTH Aachen and Member of Federated Systems and Data division, Juelich Supercomputing Centre, Germany
Chair of Ceremony: Dr. Helmut Wolfram Neukirchen, Professor and Vice Head of the Faculty of Industrial Engineering, Mechanical Engineering and Computer Science, University of Iceland
Abstract
The use of Machine Learning (ML) techniques in the medical field is not a new occurrence and several papers describing research in that direction have been published. This research has helped in analysing medical images, creating responsive cardiovascular models, and predicting outcomes for medical conditions among many other applications. This Ph.D. aims to apply such ML techniques for the analysis of Acute Respiratory Distress Syndrome (ARDS) which is a severe condition that affects around 1 in 10.000 patients worldwide every year with life-threatening consequences. We employ previously developed mechanistic modelling approaches such as the “Nottingham Physiological Simulator,” through which better understanding of ARDS progression can be gleaned, and take advantage of the growing volume of medical datasets available for research (i.e., “big data”) and the advances in ML to develop, train, and optimise the modelling approaches. Finally, we leverage the available Modular Supercomputing Architecture (MSA) developed as part of the Dynamical Exascale Entry Platform - Extreme Scale Technologies (DEEP-EST) EU Project to scale-up and speed-up the modelling processes. This Ph.D. Project is one element of the Smart Medical Information Technology for Healthcare (SMITH) project wherein the thesis research can be validated by clinical and medical experts (e.g. Uniklinik RWTH Aachen).
