The Simulation and Data Lab Health and Medicine (SimDataLab HM) aims to shed light on novel data analysis approaches in the medical field with extra focus on the application of High Performance Computing (HPC) architectures in the processing of patient medical data, as well as diagnosis and treatment assistance.
Prof. Dr. – Ing. Morris Riedel
Morris Riedel received his PhD from the Karlsruhe Institute of Technology (KIT) and worked in data-intensive parallel and distributed systems since 2004. He is currently a Full Professor of High-Performance Computing with an emphasis on Parallel and Scalable Machine Learning at the School of Natural Sciences and Engineering of the University of Iceland. Since 2004, Prof. Dr. – Ing. Morris Riedel held various positions at the Juelich Supercomputing Centre of Forschungszentrum Juelich in Germany. In addition, he is the Head of the joint High Productivity Data Processing research group between the Juelich Supercomputing Centre and the University of Iceland. Since 2020, he is also the EuroHPC Joint Undertaking governing board member for Iceland. His research interests include high-performance computing, remote sensing applications, medicine and health applications, pattern recognition, image processing, and data sciences, and he has authored extensively in those fields. Prof. Dr. – Ing. Morris Riedel online YouTube and university lectures include High-Performance Computing – Advanced Scientific Computing, Cloud Computing and Big Data – Parallel and Scalable Machine and Deep Learning, as well as Statistical Data Mining. In addition, he has performed numerous hands-on training events in parallel and scalable machine and deep learning techniques on cutting-edge HPC systems.
Dr. Chadi Barakat
Chadi Barakat received his PhD in Bioengineering from the School of Engineering and Natural Sciences of the University of Iceland. He is a member of the Research Group AI and ML for Healthcare and the Center for Advanced Simulation and Analytics (CASA) Simulation Data Lab for Healthcare at the Juelich Supercomputing Centre of Forschungszentrum Juelich in Germany. He completed his B.Sc. in Computer and Communications Engineering at the American University of Science and Technology – Beirut, Lebanon and received his M.Sc. in Bioengineering from the University of Iceland.
His PhD work revolved around the application of Machine Learning and Deep Learning in medical applications, specifically in the diagnosis and treatment of Acute Respiratory Distress Syndrome (ARDS), and is currently making HPC-accelerated AI solutions available to industry partners in the Smart Data Innovation Services project.
Dr. Sebastian Fritsch
Josefine holds a degree in Media and Computing, she has an established track record in the fields of research, e-commerce, and telecommunications. They possess strong skills in Java, Python, and Pandas, as well as in various web technologies. As an administrative professional, they demonstrate a strong capability for organization and efficiency. Currently, they are preparing to advance their education with a Master's program in Applied Informatics, aiming to further enhance their expertise in the field
C. S. Barakat et al., ‘Developing an Artificial Intelligence-Based Representation of a Virtual Patient Model for Real-Time Diagnosis of Acute Respiratory Distress Syndrome’, Diagnostics, vol. 13, no. 12, p. 2098, Jun. 2023, doi: 10.3390/diagnostics13122098.
C. Barakat, M. Aach, A. Schuppert, S. Brynjólfsson, S. Fritsch, and M. Riedel, ‘Analysis of Chest X-ray for COVID-19 Diagnosis as a Use Case for an HPC-Enabled Data Analysis and Machine Learning Platform for Medical Diagnosis Support’, Diagnostics, vol. 13, no. 3, 2023, doi: 10.3390/diagnostics13030391.
C. Barakat et al., ‘Lessons learned on using High-Performance Computing and Data Science Methods towards understanding the Acute Respiratory Distress Syndrome (ARDS)’, in 2022 45th Jubilee International Convention on Information, Communication and Electronic Technology (MIPRO), Opatija, Croatia: IEEE, Jun. 2022, pp. 368–373. doi: 10.23919/MIPRO55190.2022.9803320.
C. Barakat, S. Fritsch, M. Riedel, and S. Brynjolfsson, ‘An HPC-Driven Data Science Platform to Speed-up Time Series Data Analysis of Patients with the Acute Respiratory Distress Syndrome’, in 2021 44th International Convention on Information, Communication and Electronic Technology (MIPRO), Opatija, Croatia: IEEE, Sep. 2021, pp. 311–316. doi: 10.23919/MIPRO52101.2021.9596840.
M. Riedel et al., ‘Enabling Hyperparameter-Tuning of AI Models for Healthcare using the CoE RAISE Unique AI Framework for HPC’, in 2023 46th MIPRO ICT and Electronics Convention (MIPRO), Opatija, Croatia: IEEE, May 2023, pp. 435–440. doi: 10.23919/MIPRO57284.2023.10159755.