Xiaowu Sun

Xiaowu Sun

Assistant Professor

20162026

Research activity per year

Personal profile

Personal profile

Dr. Xiaowu Sun is an Assistant Professor at the school of AIAC, XJTLU. Xiaowu received his PhD in 2023 from Leiden University Medical Center, the Netherlands. His research focuses on artificial intelligence for medical imaging, with particular emphasis on coronary angiography and cardiac MRI. More recently, he has been especially interested in applying graph neural networks and foundation models to clinical applications, and some of his work has been translated into the commercial software MASS.
   Before joining XJTLU, he was a Postdoctoral Fellow at the EPFL AI Center in Lausanne, Switzerland. He is also the recipient of a SWISS AI grant as Principal Investigator, focusing on foundation model research. Outside of his academic work, he enjoys volleyball. He achieved 7th place with his team at the Beijing Universities Volleyball League and played for the SKC club in Leiden, the Netherlands, for four seasons in a competitive amateur league.

Experience

2025.08 -              Assistant Professor,  XJTLU

2023.03 - 2025.08  Postdoctoral Fellow, École Polytechnique Fédérale de Lausanne (EPFL), Switzerland. 

2017.08 - 2018.08  Algorithm Engineer, Rokid

 

Awards and honours

  • National Scholarship × 2
  • Outstanding undergraduates of LiaoNing Province
  • Undergraduate Mathematical Contest in Modeling (CUMCM):
    • National Second prize × 2;
    • Liaoning Province First Prize × 2;
    • Liaoning Province Second Prize × 1
  • Mathematics Contest (Professional Division) of DaLian Municipality
    • First Prize × 2 (Ranking 3rd, 9th place, resp.)
  • MICCAI challenge
    • 3rd place in the M&Ms-2 challenge
    • 4th place in the CMRxMotion challenge
  • MICCAI Travel award
  • 2025  Gerald Pohost Award finalists

Related documents

Education/Academic qualification

PhD, Leiden University

3 Sept 20181 Mar 2023

Award Date: 5 Jul 2023

Research areas

  • Cardiovascular Imaging
  • Deep Learning
  • Graph Neural Networks
  • Physics-informed Network
  • Foundation Models

Person Types

  • Staff

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