Shadi Albarqouni

Shadi Albarqouni

AI Young Investigator Group Leader at Helmholtz AI | TUM Junior Fellow at TU Munich | Former Visiting Scientist at Imperial College London and ETH Zürich

Helmholtz AI

Technical University Munich

Biography

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. Albarqouni serves as an expert and evaluator at the German Research Foundation (DFG), the Federal Ministry of Education and Research (BMBF), and the European Commission. He has been also elected as a member of the European Lab for Learning & Intelligent Systems (ELLIS), and Arab German Young Academy (AGYA), in addition to his membership at 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.

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

Interests

  • Deep Learning with Medical Imaging
  • Representation Learning
  • Uncertainty
  • Federated Learning

Education

  • Ph.D. in Computer Science, 2017

    Technical University of Munich, Germany

  • M.Sc. in Electrical Engineering, 2010

    Islamic University of Gaza, Palestine

  • B.Sc. in Electrical Engineering, 2005

    Islamic University of Gaza, Palestine

Experience

Academic and Professional Experience

 
 
 
 
 

AI Young Investigator Group Leader

Helmholtz AI, Helmholtz Center Munich

Nov 2020 – Present Munich, Germany

I will be leading Albarqouni Lab focusing our research on developing innovative deep Federated Learning algorithms that can distill and share the knowledge among AI agents in a robust and privacy-preserved fashion. The lab will be hosted at Helmholtz AI and The Department of Computational Health at Helmholtz Center Munich allowing us to have access to huge databases of Genetics, Microscopic data, and Medical Imaging, such as Cooperative Health Research in the Augsburg Region (KORA), and German National Cohort (NAKO).

Roles:

Research:

  • We will continue our research directions to develop fully-automated, high accurate solutions that save export labor and efforts, and mitigate the challenges in medical imaging, i.e. i) the availability of a few annotated data, ii) low inter-/intra-observers agreement, iii) high-class imbalance, iv) inter-/intra-scanners variability and v) domain shift. Our research portfolio can be categorized into Learn to Recognize, Adapt, Learn, Reason and Explain, incorporate prior knowledge,
  • We will focus our research on developing innovative deep Federated Learning algorithms that can distill and share the knowledge among AI agents in a robust and privacy-preserved fashion. Research topics include, but not limited to, i) handling distributed DL models with data heterogeneity including non i.i.d, and domain shifts, ii) developing explainability and quality control tools for distributed models, and iii) robustness to data and model poisoning attacks.

Funded Projects:

Community Contribution:

 
 
 
 
 

TUM Junior Fellow

Chair for Artificial Intelligence in Healthcare and Medicine, Technical University Munich

Nov 2020 – Present Munich, Germany

I am affiliated with the Faculty of Informatics and TUM School of Medicine with the Chair for Artificial Intelligence in Healthcare and Medicine (Prof. Rueckert), and Chair for Computer Aided Medical Procedures (Prof. Navab).

Teaching:

 
 
 
 
 

Visiting Scientist

Department of Computing, Imperial College London

Aug 2020 – Oct 2020 London, United Kingdom
I worked with Prof. Daniel Rueckert on Federated Learning in Healthcare, in particular, defining interesting relevant projects between ICL and TU Munich in this direction
 
 
 
 
 

Visiting Scientist

Computer Vision Lab (CVL), ETH Zürich

Nov 2019 – Jul 2020 Zürich, Switzerland

I worked with Prof. Ender Konukoglu on Modeling Uncertainty in Medical Imaging, in particular, the one associated with inter-/intra-raters variability

Supervision:

 
 
 
 
 

Senior Research Scientist and Team Lead

Computer Aided Medical Procedures (CAMP), TU Munich

Jan 2017 – Oct 2020 Munich, Germany

I led the Medical Image Analysis team and worked toegther with a couple of PhD students on Deep Learning for Medical Applications.

Research: We have focused our research directions to develop fully-automated, high accurate solutions that save export labor and efforts, and mitigate the challenges in medical imaging, i.e. i) the availability of a few annotated data, ii) low inter-/intra-observers agreement, iii) high-class imbalance, iv) inter-/intra-scanners variability and v) domain shift. Our research portfolio can be categorized into Learn to Recognize, Adapt, Learn, Reason and Explain, incorporate prior knowledge, and collaborate with other AI agents

Teaching:

Fundraising:

  • Projects with Industry Partners (~1 MEUR)
  • Research Visits (~20 TEUR)
  • Individual Fellowships (~250 TEUR)

Supervision:

  • Mentoring of 10 on-going PhD candidates
  • Supervision of 30 successful Master thesis
  • Supervision of 2 successful Bachelor thesis projects
 
 
 
 
 

Consultant and Expert in Telemedicine

German Corporation for International Cooperation GmbH (GIZ)

Nov 2014 – Nov 2014 Ramallah, Palestine

My tasks:

  • Discussing the importance of using telemedicine to provide comprehensive healthcare.
  • Giving examples of my experience in telemedicine in Palestine.
  • Participate in a panel discussion along with local speakers to discuss the different functions of telemedicine.
  • Give recommendations on how to continue with the telemedicine project
 
 
 
 
 

Research Scientist

Computer Aided Medical Procedures (CAMP), TU Munich

Oct 2013 – Dec 2016 Munich, Germany

I worked as a Research Scientist with Prof. Nassir Navab, Dr. Stefanie Demirci, and Dr. Tobias Lasser, on developing machine learning methods for biomedical imaging. My duties were:

Research:

Teaching:

Supervision:

  • Supervision of 6 successful Master thesis
  • Supervision of 2 successful Bachelor thesis projects
 
 
 
 
 

Visiting Research Scientist

German Center for Neurodegenerative Diseases (DZNE)

Apr 2013 – Dec 2016 Bonn, Germany
I worked with Dr. Ashraf Al-Amoudi and Dr. Ing. Weaam Al-Khaldi on developing image processing, i.e. 3D tomographic reconstruction and noise reduction techniques, for 3D cryo-electron tomographic data.
 
 
 
 
 

Lecturer

Electrical and Computer Engineering Department (ECE), IU Gaza

Feb 2011 – Jan 2012 Gaza, Palestine
I worked as Senior Project Advisor for the Computer Engineering Department, and as a Lecturer of the Robotics Course for the Electrical Engineering Department.
 
 
 
 
 

Instructor

University College of Applied Science (UCAS)

Sep 2007 – Jan 2012 Gaza, Palestine
I worked as an Instructor at the Information technology Department teaching a few courses like ICDL Course and Essentials of Information Technology; i.e. Search Engine Optimization (SEO), E-Commerce, E-Health, and Internet Security
 
 
 
 
 

Head of Information Technology Department

Nasser Pediatric Hospital

Nov 2006 – Oct 2012 Gaza, Palestine

I worked as a Networks Engineer for two years before being promoted to the head of the IT department at the hospital. My tasks were:

  • Build and maintain the network backbone infrastructure
  • Provide technical support for different departments at the hospital
  • Manage and integrate both Linux and Windows OS solutions
  • Implement and maintain the Health Information Management System (HIMS)
  • Contribute to the Bethlehem Aliance Project
  • Build and maintain the videoconference system for Educational/Teleconsultation purposes
  • An initiative of Establishing a Telemedicine Center in Palestine
 
 
 
 
 

Teaching Assistant

Electrical and Computer Engineering Department (ECE), IU Gaza

Feb 2006 – Jun 2007 Gaza, Palestine

Fellowships and Awards

Helmholtz AI Young Investigator Group

It is one of the prestigious fundings to support the independence of young scientists to establish their own research groups. The selection process is highly competitive as the total funding could reach 1.8 million euros.

Postdoctoral Researchers International Mobility Experience (PRIME) Fellowship

It is one of the prestigious fellowships for one-year international mobility followed by half a year at the German host institute. It is very competitive as its acceptance rate around 12%.

Best Reviewer

I have been listed as one of the best reviewers among 600 reviewers at MICCAI 2018, Spain.

Highlighted on Leaderboard

Research Grants for Doctoral Candidates and Young Academics and Scientists

It is among the prestigious research grants offered for Palestinians. It is very competitive as its acceptance rate of less than 5%.

Latest News

Research Projects

Funded and Active Projects. Thanks to our great collaborators!

BigPicture Project

The 6-year, €70 million project called BIGPICTURE will herald a new era in pathology

Deep Federated Learning in Healthcare

One of our recent and promising projects.

Learn from Crowds

Crowdsourcing, Gamification

Learn from Prior Knowledge

Manifold Learning, Graph Convolutional Networks

Learn to Adapt

Domain Adaptation, Style Transfer

Learn to Learn

Meta-Learning, Few-Shot Learning

Learn to Reason and Explain

Interpretable ML, Disentangled Representation, Fairness

Learn to Recognize

Detection, Classification, Segmentation, Anomaly Detection, Semi-/Weakly-Supervised Learning

Modelling Uncertainty in Deep Learning for Medical Applications

DAAD PRIME Fellowship at ETH Zürich and Imperial College London

Telemedicine in Palestine

Telemedicine in Palestine from Shadi Nabil Albarqouni Collaboration: Funding:

Community Engagement

Professional Services and Invited Talks in the last two years

Medium Blog: Journey through COVID-19 RSNA Papers

Disclaimer: I am neither a radiologist nor a clinician. I am a computer scientist who have been working on medical image computing for a while. I tried to summairze the key findings reported in almost 15 papers published in the Radiology Society in North America (RSNA) in the last two months.

Modelling Labels Uncertainty in Medical Imaging

Meet the Team

Albarqouni Lab. @Helmholtz AI

Researchers

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Tariq Bdair

PhD Student

Machine Learning in Medical Imaging, Semi-Supervised Learning, Federated Learning

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Cosmin Bercea

PhD Student

Deep Learning for Medical Image Analysis, Anomaly Detection, Federated Learning, Image Understanding

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Zehra Hayirci

PhD Student

Federated Learning, Machine Learning for healthcare, Computer Vision

Contact

  • Ingolstädter Landstr. 1, Neuherberg, DE- 85764
  • Helmholtz AI Central Unit, Helmholtz Center Munich