Shadi Albarqouni is a Palestinian-German Computer Scientist. 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 a prestigious 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. Albarqouni received his Ph.D. in Computer Science with summa cum laude in 2017.

Since then, Albarqouni has been working as a Senior Research Scientist & Team Lead at CAMP leading the Medical Image Analysis (MedIA) team with an emphasis on developing deep learning methods for medical applications. In 2019, he received the P.R.I.M.E. fellowship for one-year international mobility. During the period from Nov. 2019 to Jul. 2020, 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. During the period Aug.-Oct. 2020, Albarqouni worked as a Visting Scientist at the Department of Computing at Imperial College London, United Kingdom. He worked with Prof. Daniel Rueckert on Federated Learning.

Since Nov. 2020, Albarqouni is holding an AI Young Investigator Group Leader position at Helmholtz AI. The aim of Albarqouni 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. Albarqouni has around 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 ELLIS, AGYA, MICCAI, BMVA, IEEE EMBS, IEEE CS, and ESR society. Since 2015, he has been serving as a PC member for a couple of MICCAI workshops, e.g., COMPAY, and DART among others. Since 2019, Albarqouni has been serving as an Area Chair in Advance Machine Learning Theory at MICCAI.

Interests

  • Interpretable machine learning
  • Robustness and uncertainty in ML
  • Federated learning

Education

  • AI Young Investigator Group Leader

    Helmholtz AI

  • Visiting Scientist, 2019-2020

    Department of Information Technology and Electrical Engineering (D-ITET) at ETH Zürich

  • Visiting Scientist, 2020

    Department of Computing, Imperial College London

  • PhD in Computer Science (summa cum laude), 2017

    TUM

  • M.Sc. Electrical Engineering, 2010

    IU Gaza, Palestine

  • B.Sc. Electrical Engineering, 2005

    IU Gaza, Palestine