Research Vision
Histopathology is being transformed by digital pathology, whole-slide imaging, multi-omics, and modern artificial intelligence. Our research develops computational methods that make tissue data more measurable, interpretable, and clinically actionable.
We study how to analyze and model complex biomedical images across scales, from microscopy to tissue architecture, and how to bridge morphology with molecular information such as spatial transcriptomics and spatial proteomics. A central goal is to build robust, explainable, and human-centered AI systems that can assist clinicians and biomedical researchers while remaining scientifically grounded and ethically responsible.
Core Research Areas
Computational Pathology
AI methods for detection, segmentation, quantification, and interpretation in whole-slide images.
Omics Imaging
Integration of histology with spatial transcriptomics, spatial proteomics, and multimodal tissue data.
Virtual Staining
Computational generation of molecular and immunohistochemical contrasts from standard H&E images.
Trustworthy AI
Human-centered models with interpretability, uncertainty estimation, and responsible decision support.
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