AALBORG UNIVERSITET

Martin Bøgsted is the Head of the Center for Clinical Data Science (CLINDA) at Aalborg University. In the ARISTOTELES project, he leads Work Package 4, which aims to establish the data infrastructure to manage information from two million atrial fibrillation patients across Europe. Throughout his career, Prof. Bøgsted has led several complex infrastructure projects, including the development of Aalborg University's genomic medicine workflow for cancers with limited treatment options. He has also served as project manager for knowledge databases during the implementation of the Danish National Genome Center (DNGC). Additionally, he has worked as a technical project manager on large-scale data infrastructure projects in the telecommunications sector, managing millions of mobile customer calls and data. Prof. Bøgsted has published numerous papers on applied probability, statistics, and machine learning. He possesses deep expertise in machine learning and high-performance computing for clinical applications. His significant research contributions include methods for classifying therapy-resistant patients using cell line models and identifying the cellular origin of cancer. These findings, published in leading medical journals, have led to the identification of potential new treatment strategies, which are currently being validated in pre-clinical models.

Charles Vesteghem is Group Leader at the Center for Clinical Data Science (CLINDA) at Aalborg University. His group focuses on the development and implementation of AI based solutions in medicine and more generally on innovative digitalization project such as data/knowledge sharing in oncology. Charles also works on issues related to health data infrastructure, from data sharing (FAIR Data Principles) to harmonization (OMOP CDM). His work spans across multiple specialties, from gastro-enterology to cardiology with a focus on oncology and general practice, where he is, for example, exploring the use of large language models. Charles has also had an extensive experience in entrepreneurship and digital innovation, having worked more than 10 years in data driven start-ups, including 6 years as founder and CEO of his own company. In the ARISTOTELES project, he will be involved in the Work Packages 4 and 5, focusing on data infrastructure and AI development.

Rasmus Froberg Brøndum is an associate professor in bioinformatics and statistics at of the Center for Clinical Data Science (CLINDA) at Aalborg University. He received his Ph.D. in statistical genetics from Aarhus University in 2013 and has 15 years’ experience working with omics data in high performance computing systems. He has spent the last 10 investigating and implementing personalized medicine solutions and is actively involved in both pre-processing pipelines and down-stream analysis of molecular and clinical registry data. In the Aristoteles project, he is involved in Work Package 4, helping to set of the data infra structure at the Danish National Genome Centre (NGC), and has the technical responsibility for the contract with NGC.

Simon Dahl serves as a data scientist and data manager at the Center for Clinical Data Science at Aalborg University Hospital. His primary contributions have been directed towards the implementation and deliverables of Work Package 4. He has played a key role in designing and developing the ARISTOTELES data infrastructure and platform, hosted on the Danish National Genome Center’s managed cloud environment. The platform is designed to store, harmonize, integrate, and analyze data from two million atrial fibrillation patients across Europe. In addition, he has facilitated the transformation of Danish National Health Registry data from the Danish Health Data Authority (SDS) into the OMOP common data model.

Rune Sejer Jakobsen is a postdoc in the Center for Clinical Data Science (CLINDA) at Aalborg University. In the ARISTOTELES project, he participates in the first deliverables of Work Package 5, which aims to perform feature engineering to bridge the available data to the machine learning models in comprehending the challenges of atrial fibrillation. Rune has a background in Biomedical Engineering and Informatics and did his PhD on using clinical explainable machine learning to fight hospital-acquired infections. He has extensive experience navigating large amounts of Danish healthcare data. Rune has primarily published papers in various clinical journals, focusing on how new machine-provided knowledge can become an integral and valuable feature in future healthcare.

Professor, consultant, head of Danish Center for Clinical Health Services Research, MD, PhD. More than 20 years of full-time research experience within clinical epidemiology and health services research. The primary field of interest is clinical health services research with a focus on the quality of clinical care, including implementation of evidence-based care, comparative effectiveness of clinical interventions and evaluation of quality improvement strategies. Cofounder of multiple national Danish clinical quality registries, including Danish Stroke Registry and Atrial Fibrillation Denmark. Steering group member of multiple ongoing international research-initiated clinical trials on atrial fibrillation and stroke care. Number of citations: >10.000, H-index (Scopus): 53. More than 90% of the publications include original data.

Peter Brønnum Nielsen is an associate professor and senior epidemiologist at Danish Center for Health Services Research and the department of cardiology, Aalborg University and Aalborg University Hospital. PB Nielsen has extensive experience in epidemiological research with profound knowledge regarding use of the Danish healthcare registries as a resource for various types of research. His scientific contributions have impacted guideline recommended treatment for atrial fibrillation patients, with particular focus on risk stratification and optimal stroke prevention treatment.

Katja Hose received her PhD in Computer Science from Ilmenau University of Technology and had prior positions as a postdoc at the Max Planck Institute for Informatics in Saarbrücken, Germany. Her research is deeply rooted in data and knowledge engineering, with a focus on graph databases, knowledge graphs, data integration, and applied machine learning. In the context of several national and international projects and collaborations, she has gained extensive experience in interdisciplinary data science and worked together with colleagues from bioscience, health, and environmental assessment. Katja has co-authored over 150 peer-reviewed publications and frequently reviews for a wide range of conferences and journals. She also serves as an editorial board member and associate editor for the VLDB Journal, the Semantic Web Journal, and Transactions on Graph Data and Knowledge. Additionally, she has played significant organizational roles in major international conferences, including VLDB, SIGMOD, EDBT, TheWebConf/WWW, and ISWC, and has served as program co-chair for ISWC 2024, EDBT 2023, and ESWC 2021.

Tomer Sagi has published extensively in premier venues such as VLDB and TheWebConf, and has received awards for his work in data integration and medical AI. He serves as a reviewer for several prominent journals and conferences, including TKDE, ISWC, and SIGMOD. Additionally, he has played active roles in organizing international workshops and conferences and contributed to open-source projects related to graph databases and data integration.

Mohammad Yaqoob is a Post-Doctoral Researcher at the Computer Science Department of Aalborg University, contributing to a European project focused on advancing personalized medicine through data engineering and artificial intelligence to predict and prognose Atrial Fibrillation. With a Ph.D. in Electrical Engineering and many years of experience, his background includes expertise in power system design, renewable energy integration, and control strategies. In his current role, Dr. Yaqoob is part of a team developing a unified medical dataset and automated interoperation tools for research partners, while also conducting research on synthetic patient cohort generation and AI model robustness through error-pattern analysis. Passionate about innovation, he blends his technical expertise with a commitment to advancing personalized healthcare solutions.

Thomas Ploug, is Director of Centre for AI Ethics, Law, and Policy, and Director of Centre for Ethics Education. Chair of Research Ethics Committee at Aalborg University. Former member of the National Council of Ethics in Denmark. Guest Professor at Halmstad University, Sweden. Primary research interest is in Data- and AI-ethics, and more particularly in citizen rights in relation to data usage and AI decision-making as well as population views on data and AI use across various domains. Member of multiple expert groups, committees and advisory boards in relation to AI development and implementations.