I will join the Department of Applied Mathematics at Xi’an Jiaotong-Liverpool University as an Assistant Professor in Scientific Machine Learning in June 2025. My research interest includes but not limited to, reduced-order modelling, Gaussian process and kernel methods, uncertainty quantification, and physics-aware machine learning.
If you are interested in working on the related topic for master thesis or internship, feel free to contact me.
News
- Our work on 'A parametric framework for kernel-based dynamic mode decomposition using deep learning', conducted by UvA computational science master student Konstantinos Kevopoulos, has been available on Arxiv. Congratulation, Kostas!
- I am excited to be granted one of the National Growth Fund programme AiNed XS project on 'Geometric deep learing of shape variatgions in hemodynamic simulation'. Cheers!
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It's my great pleasure to present and share our latest work on 'Gaussian process learning for nonlinear dynamics' at the Scientific Machine Learning Workshop at CWI!
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I am excited to share our latest publication: Data-driven reduced-order modelling for blood flow simulations with geometry-informed snapshots!
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I am excited to present our work 'registration-based reduced-order modelling for blood flow simulations' at CompBioMed Conference, Munich, Garching.
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