Xingjian Zhang
PhD researcher in machine learning for biomedical imaging and clinical AI
About me
I am a PhD student in the Hydrodynamics Laboratory of École Polytechnique (LADHYX) and the Information Processing and Communications Laboratory of Télécom Paris (LTCI), supervised by Prof. Abdul Barakat and Prof. Elsa Angelini.
My research develops machine learning methods for biomedical imaging, with a focus on self-supervised learning for cellular phenotyping, generative models for bioimage restoration, and applied ML systems across academic and industry collaborations in medtech and clinical settings.
You can download a PDF copy of my CV [HERE].
Publications
Journal Articles
-
B. Asadipour, R. Ronzano, J. Morizet, X. Zhang, A. Chessel, P. Mahou, M. Aigrot, B. Stankoff, A. Desmazieres, E. Beaurepaire, and C. Stringari, "Label-free nonlinear microscopy probes cellular metabolism and myelin dynamics in live tissue," Communications Biology, 2025. [PDF]
-
M.C. Yagüe, X. Zhang, M. Volpatti, Y. Wei, G. Lebedev, J. Gamby, and A.I. Barakat, "Noninvasive real-time monitoring of cellular spatiotemporal dynamics via machine learning-enhanced electrical impedance spectroscopy," Science Advances, 2025. [PDF]
-
C. Leclech, G. Cardillo, B. Roellinger, X. Zhang, J. Frederick, K. Mamchaoui, C. Coirault, and A.I. Barakat, "Micro-scale topography triggers dynamic 3D nuclear deformations," Advanced Science, 2025. [PDF]
-
A. Hauguel, K. Kasani, V. Chevance, X. Zhang, A.I. Barakat, S. Haulon, and A. Azarine, "Changes in ascending aorta and aortic arch secondary flow patterns following endovascular repair of the descending thoracic aorta," European Journal of Vascular and Endovascular Surgery, 2025. [PDF]
-
B. Asadipour, E. Beaurepaire, X. Zhang, A. Chessel, P. Mahou, W. Supatto, M.C. Schanne-Klein, and C. Stringari, "Modeling and predicting second harmonic generation from protein molecular structure," Physical Review X, 2024. [PDF]
Conference Proceedings
-
X. Zhang, C. Leclech, L. Blivet-Bailly, A.I. Barakat, and E.D. Angelini, "Log focal frequency loss for bioimage restoration," IEEE International Symposium on Biomedical Imaging (ISBI) (accepted), 2026.
-
X. Zhang, C. Leclech, B. Roellinger, C. Coirault, E.D. Angelini, and A.I. Barakat, "Myoblast mutation classification via microgroove-induced nuclear deformations," Medical Imaging with Deep Learning (MIDL), 2024. [PDF]
-
G. Pogudin and X. Zhang, "Interpretable exact linear reductions via positivity," Computational Methods in Systems Biology (CMSB), 2021. [PDF]
Projects
BioimageLoader
Load bioimages for machine learning applications
bioimageloader is a python library to make it easy to load bioimage datasets for machine learning and deep learning.
Bioimages come in numerous and inhomogeneous forms. bioimageloader attempts to wrap them in unified interfaces, so that you can easily concatenate, perform image augmentation, and batch-load them.
bioimageloader provides:
- collections of interfaces for popular and public bioimage datasets
- image augmentation using albumentations, which is popular and powerful image augmentation library (for 2D images)
- compatibility with pytorch
- and with others such as scikit-learn and tensorflow
Contact
Email: xingjian.zhang@melix.org
Office: Batiment 104, Bd des Maréchaux, 91120, Palaiseau