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个人简介
My research interests are focused on general signal and image processing, sparsity-inspired modeling, machine learning and their application to biomedical sciences.
For thousands of years explorers were inspired by the sight of uncharted shores, or by the defiant look of new and higher peaks - right after having overtaken the last. Others rejoiced with the discovery of a new star cruising across the sky, and thrived when realizing that they could predict where the bright dot would be with the passing of time.
I'm fascinated by our understanding of the information contained in data, from the image of the mountain peak to immunohistochemistry images in digital pathology. This understanding is often formalized through the construction of models, thus capturing the information contained in these different data sources. If successful, one can deploy these constructions to tackle inverse problems of different kinds, prediction, clustering and other machine learning tasks, and more. I'm particularly interested in the responsible use of machine learning, studying aspects of robustness and interpretability.
For thousands of years explorers were inspired by the sight of uncharted shores, or by the defiant look of new and higher peaks - right after having overtaken the last. Others rejoiced with the discovery of a new star cruising across the sky, and thrived when realizing that they could predict where the bright dot would be with the passing of time.
I'm fascinated by our understanding of the information contained in data, from the image of the mountain peak to immunohistochemistry images in digital pathology. This understanding is often formalized through the construction of models, thus capturing the information contained in these different data sources. If successful, one can deploy these constructions to tackle inverse problems of different kinds, prediction, clustering and other machine learning tasks, and more. I'm particularly interested in the responsible use of machine learning, studying aspects of robustness and interpretability.
研究兴趣
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JOURNAL OF ORTHOPAEDIC RESEARCHno. 2 (2024): 453-459
Magnetic Resonance in Medicine (2024)
bioRxiv (Cold Spring Harbor Laboratory) (2024)
Radiology. Artificial intelligenceno. 1 (2024): e230159-e230159
Trans. Mach. Learn. Res. (2023)
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MACHINE LEARNING IN CLINICAL NEUROIMAGING, MLCN 2023 (2023): 56-66
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