Computational imaging constitutes a pivotal pathway for our observation and comprehension of the natural world. It encompasses three key components: image sensing, image reconstruction, and image analysis. These components have historically evolved separately, with a limited degree of integration among them. However, this situation is gradually changing in light of significant advancements in machine learning, particularly deep learning. The primary focus of this talk lies in exploring the opportunities and challenges brought about by deep learning for computational imaging. It presents an overview of the integration of traditional image reconstruction algorithms with deep learning methodologies, thereby designing data-driven and task-driven imaging algorithms that enable organic fusion of the three components of computational imaging. The final section of the report discusses the significance of computational imaging from a broader perspective in cutting-edge research in life sciences and medicine, as well as its future development trajectories.