Optical (light) microscopy enables acquisition of visual information about samples across spatial, spectral, and temporal dimensions in a noninvasive manner. Throughout its long history, advancements in light microscopy have continuously pushed the limits of the information that can be captured along these dimensions. 

Computational microscopy approaches merge the design of hardware (light sources, optics, sensors) with computational algorithms for image reconstruction, enhancement, and analysis. This integration expands the information content beyond that of traditional microscopy and enables new functionalities such as snapshot 3D imaging. Progress in machine learning and artificial intelligence (AI) has further accelerated developments by enabling data-driven optimization. Nevertheless, these methods introduce challenges, especially in life sciences, where collecting and annotating high-quality data is often tedious and difficult to standardize. 

It is widely recognized that future advancements in the field will be driven by multidisciplinary research integrating accurate models of image formation involving complex light–matter interactions, precise optics/photonics fabrication, and advanced AI algorithms. Crucially, these components must be jointly developed and tightly coupled to the specific problem or use case at hand. This workshop, thus, aims to bring together researchers and practitioners from Optical & Physical Sciences, Computing Sciences, and Biomedical & Life Sciences to promote collaboration and inspire future innovations in computational microscopy. 

Example topics of interest: 

• Snapshot 3D (volumetric) fluorescence microscopy
• Quantitative phase imaging
• Computational microscopy with learned optics/illumination 
• Computational models of tissue optics and scattering  
• Machine learning for biomedical image reconstruction and analysis 
• Datasets for microscopy: acquisition, annotation, curation