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Professor Martin Ingvar, MD, PhD
(Karolinska Institute, ICHOM)
Keynote speaker 1
Martin Ingvar is a professor in Neurophysiology and former Dean of Research at Karolinska Institutet, Stockholm, Sweden. In addition, he is a professor of Integrative Medicine at Osher Center for Integrative Medicine. Recently he has devised a structured formalism for decision making in healthcare in distributed organizations with the aim of sharing knowledge along the patient trajectory. The aim is to standardize decision making in trans-professional teams based on shared semantic context that serves the patient health. Martin Ingvar is a co-founder of International Consortium for Health Outcomes Measurements (ICHOM) and serves the board.
Noëlla Pierlet, MSc.
Keynote speaker 2
Noëlla Pierlet is Head of Data Science in hospital Ziekenhuis Oost-Limburg. She studied mathematics at KU Leuven, followed by additional university courses in Informatics, Applied Informatics and Business Administration at KU Leuven and UHasselt. She has 25 years of experience in Hospital Informatics, in several roles including system administration, security, software development and project management. She joined ZOL 6 years ago where she took an active role in the implementation of a new electronic medical record (EMR). Currently, as head of the Data Science Unit at ZOL, she is leading a team of data scientists who add improvements to the EMR, transform data to insights for hospital management and physicians and collect and wrangle different kinds of data for research. She and her team participate in several innovative and international projects where data is used to improve patient outcomes.
Peter De Jaeger, Prof, PhD, MSc.
(AZ Delta, RADar)
Keynote speaker 3
Prof. PhD. MSc. Peter De Jaeger is head of the RADar learn- and innovation department of AZ Delta. He received the MSc degree in mechanical engineering and in electronics engineering from the Ghent University, where he also obtained his PhD in fluid mechanics and heat transfer. He stayed as a researcher at the Queens University in Brisbane Australia to investigate highly porous media and at the University College Dublin in Dublin, Ireland to develop novel large-strain plasticity models. He authored or co-authored 70 papers in various international journals and conferences. He has practical experience with complex numerical algorithms, surrogate modelling, data engineering, data analysis, machine learning, IoT and advanced computing. He brings in experience in managing complex international scientific projects and successfully managed different successful VLAIO fudanded projects.
Attia Zachi, Co-Director of AI in cardiology, Assistant professor of medicine
(Mayo Clinic, Rochester MN)
Keynote speaker 4
Attia Zachi is an AI researcher in Mayo Clinic Minnesota, developing AI based medical screening tests for early detection of cardiac diseases. Three of these tests are currently in FDA approval process under breakthrough designation. As the co-director of AI in cardiology, Zachi direct a team of AI researchers, working side by side with cardiologist to find effective algorithm to help patients get the care they need, as quickly as possible. Zachi Received his BSc and MSc in Electrical engineering from Ben Gurion University in Israel and his PhD in Bioinformatics and Computational Biology from the university of Minnesota.
Vincent Keereman, MD, MScE, PhD
(AZ Maria Middelares, Ghent)
Keynote speaker 5
Vincent Keereman holds a Master in Electronics Engineering and a Master in Medicine (MD) from Ghent University. He obtained a PhD in Biomedical Engineering on the topic of PET-MR imaging in 2011 and board certification in Neurology in 2018. He is co-founder of Molecubes, Epilog and XEOS, all medtech companies based in Ghent. He is a practicing neurologist at AZ Maria Middelares hospital in Ghent, and has an active role in XEOS and Epilog as CMO. He also holds a part-time position as Assistant Professor in Neuro-engineering at Ghent University.
Raul San Jose Estepar, Associate Professor, PhD
(Harvard Medical School)
Keynote speaker 6
Raúl San José Estépar is co-director of the Applied Chest Imaging Laboratory at Brigham and Women’s Hospital and Associate Professor of Radiology at Harvard Medical School. His laboratory focuses on novel computational imaging applications for image-based biomarker discovery to empower epidemiological and genetic studies and provide novel surrogate targets for drug discovery and clinical trial development. His group supports the image analytics of multiple Federal and Industry-sponsored investigations serving as imaging core for COPDGene, the Framingham Heart Study Pulmonary Research Center, the CARDIA Lung Study, and, more recently, the American Lung Association (ALA) Lung Health Cohort. He is the original developer and chief architect of the Chest Imaging Platform, an open-source software platform for CT-based lung phenotyping. His research has made significant contributions to the quantitative study of pulmonary vascular remodeling and the subtyping and modeling of parenchymal lung injury. His current research interests are focused on artificial intelligence approaches to enable multiscale integration of imaging and molecular information using deep learning, synthetic lung functional imaging from single energy CT, and image-based outcomes prediction models in chronic lung diseases.
Raúl received his Ph.D. in Telecommunications Engineering from the University of Valladolid, Spain, where he specialized in signal processing applied to medical image analysis and conducted his post-doc in the Surgical Planning Laboratory, Brigham and Women’s Hospital. He has been faculty at Harvard Medical School since 2006. He has co-authored over 200 peer-reviewed manuscripts, and he is currently the Principal Investigator of several NIH awards and industry sponsored studies. He is a member of the Fleischner Society, and he is actively involved in disseminating quantitative approaches for better healthcare delivery. He is also the founder and scientific advisor of Quantitative Imaging Solutions, a healthcare technology company that translates image-based AI solutions and modeling to detect and predict lung diseases.