Shadi Albarqouni is a Palestinian-German Professor of Computational Medical Imaging Research. He received his B.Sc. and M.Sc. in Electrical Engineering from the IU Gaza, Palestine, in 2005, and 2010, respectively. In 2012, he received the DAAD research grant to pursue his Ph.D. at the Chair for Computer Aided Medical Procedures (CAMP), Technical University of Munich (TUM), Germany. During his Ph.D., Albarqouni worked with Prof. Nassir Navab on developing machine learning algorithms to handle noisy labels, coming from crowdsourcing, in medical imaging. His AggNet paper, published at the Special Issue on Deep Learning at the IEEE Transaction on Medcial Imaging (IF: 10.048), was among the first ones on Medical Imaging with Deep Learning and has been featured as the top downloaded article for a couple of years at IEEEXplore.

Right before he received his Ph.D. in Computer Science with summa cum laude in 2017, Albarqouni worked as a Senior Research Scientist & Team Lead at CAMP leading the Medical Image Analysis (MedIA). Together with his team addressed the common challenges concern the nature of medical data, namely heterogeneity, severe class-imbalance, few amounts of annotated data, inter-/intra-scanners variability (domain shift), inter-/intra-observer disagreement (noisy annotations). In 2019, Albarqouni received the prestigious P.R.I.M.E. fellowship for a one-year international mobility, where he worked as a Visiting Scientist at the Department of Information Technology and Electrical Engineering (D-ITET) at ETH Zürich, Switzerland. He worked with Prof. Ender Konukoglu on Modeling Uncertainty in Medical Imaging, in particular, the one associated with inter-/intra-raters variability. Afterwards, Albarqouni worked as a Visting Scientist with Prof. Daniel Rueckert at the Department of Computing at Imperial College London, United Kingdom.

Since Nov. 2020, Albarqouni has been appointed as an AI Young Investigator Group Leader at Helmholtz AI. The aim of Albarqouni’s Lab. is to develop innovative deep Federated Learning algorithms that can distill and share the knowledge among AI agents in a robust and privacy-preserved fashion. Since Jan. 2022, Albarqouni has been appointed as a W2 Professor of Computational Medical Imaging Research at the Faculty of Medicine, University of Bonn.

Albarqouni has more than 100 peer-reviewed publications in both Medical Imaging Computing and Computer Vision published in high impacted journals and top-tier conferences. He serves as a reviewer for many journals, e.g., IEEE TPAMI, MedIA, IEEE TMI, IEEE JBHI, IJCARS and Pattern Recognition, and top-tier conferences, e.g., ECCV, MICCAI, MIDL, BMVC, IPCAI, and ISBI among others. He is also an active member of MICCAI, BMVA, IEEE EMBS, IEEE CS, and ESR society. Recently, Albarqouni has been elected as a member for the European Lab for Learning and Intelligent Systems (ELLIS), the Arab German Young Academy (AGYA), and the Higher Council for Innovation and Excellence‎ in Diaspora (HCIE). Since 2015, he has been serving as a PC member for a couple of MICCAI workshops, e.g., COMPAY, DART, DCL, FAIR among others. Since 2019, Albarqouni has been serving as an Area Chair in Advance Machine Learning Theory at MICCAI. Recently, he has been serving as a Program Co-Chair at MIDL'22 in Swizterland, and as an Organizing Committee Member at ISBI'22 in India, MICCAI'24 in Morocco.

His current research interests include Interpretable ML, Robustness, Uncertainty, and Federated Learning. He is also interested in Entrepreneurship and Startups for Innovative Medical Solutions with limited resources.