Prof. Shadi Albarqouni

Professor, University of Bonn, Germany

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.

Dr. Chen Qin

Associate Professor, Imperial College London

Dr. Chen Qin is an Associate Professor in Computer Vision and Machine Learning at Department of Electrical and Electronic Engineering and I-X, Imperial College London. Previously, she was a Lecturer at School of Engineering, University of Edinburgh from July 2020 to September 2022. She obtained her Ph.D. in Computing Research from Imperial College London in January 2020, and M.Sc. in Control Science and Engineering from Department of Automation, Tsinghua University in July 2015. Before moving to University of Edinburgh, she has worked as a post-doctoral research associate at Department of Computing, Imperial College London, where she has worked on deep learning-based MRI reconstruction, image registration and segmentation for enhancing the entire medical imaging pipeline. Her pioneering work on convolutional recurrent neural networks for MRI reconstruction has been highly and widely recognised by international leading groups from both academia and industry. Overall, she has published more than 70 papers in top-tier peer-reviewed engineering and medical imaging journals and conference proceedings. She has served as an area chair for MICCAI 2022-24 and a member of organising and programme committee at several international workshops, e.g., CMRxRecon 2023/24 and UNSURE 2022-24. She also serves as an Associate Editor for SPIE Journal of Medical Imaging and Journal of Pattern Recognition. Her current research mainly focuses on generative models for inverse problems and multi-modal representation learning, with applications in medical image computing.

Dr. Muzammil Behzad

Assistant Professor, KFUPM, Saudi Arabia

I am an AI Scientist at Silo AI (Finland), and a researcher at the Center for Machine Vision and Signal Analysis in the University of Oulu (Finland), where I also completed my PhD (with distinction) in 2022 with Academy Professor Guoying Zhao. Prior to that, I received my partially-funded B.S. degree (with distinctions - double medalist and valedictorian) and fully-funded M.S. degree, both in Electrical Engineering, from COMSATS University Islamabad (Pakistan) and King Fahd University of Petroleum & Minerals (Saudi Arabia), in 2013 and 2017, respectively. I worked as a visiting research scholar in the University College London (UK), Brown University (USA), and King Abdullah University of Science & Technology (Saudi Arabia). I have also held full-time academic positions in Pukyong National University (South Korea), and COMSATS University Islamabad (Pakistan). My research interests lie in signal and image processing, embedded systems, machine/deep learning and computer vision.

Dr. Dwarikanath Mahapatra

Assistant Professor, Khalifa University, UAE

Dwarikanath Mahapatra is an Assistant Professor in the Department of Computer Science at Khalifa University. He has previously worked as a Principal AI Scientist at Locai, and Inception Institute of Artificial Intelligence, Abu Dhabi, UAE. He obtained his Ph.D. from the National University of Singapore, worked as a post-doctoral research fellow at the ETH Zurich, Switzerland, and as a Research Staff Member at IBM Research Australia. Dwarikanath’s research interests are mainly in medical image analysis, machine learning, deep learning, decision support systems and computer aided diagnosis. He also explores other aspects of computer vision such as object detection and tracking, surveillance and image classification using deep neural networks. Dwarikanath has published more than 100 papers in prestigious conferences and journals, and holds 15 patents.