Computational imaging technologies are increasingly required to operate in real-world applications, offering high-fidelity visual output under complex lighting environments. Among these, snapshot compressive imaging (SCI) is a promising technique that retrieves high-dimensional signals from 2D optically compressed measurements. Incorporating modern AI techniques, SCI has significantly advanced the capabilities of traditional optical sensing in various fields, including hyperspectral imaging, video compression, microscopy, and security. However, despite its promise, SCI remains sensitive and under-explored to uncertainties rooted in its hybrid structure – optical encoders and algorithmic decoders – stemming from imperfect optical hardware, algorithmic overfitting, and unpredictable environmental noise, which limits its deployment across diverse platforms and safety-critical systems. This research project aims to enhance the reliability and robustness of SCI in practical use by developing novel methods to study uncertainties at various system levels. The success of this project benefits a broad range of areas, including computational imaging, signal processing, remote sensing, AI photonics, and machine learning. Along with the proposed research, the project supports a comprehensive education and outreach agenda, encompassing cutting-edge undergraduate and graduate research activities, as well as engaging K-12 students through hands-on experiences with AI and imaging technologie