Learning-based software and systems are deeply embedded in our lives. However, despite the excellent performance of machine learning models on benchmarks, state-of-the-art methods like neural networks often fail once they encounter realistic settings. Since neural networks often learn correlations without reasoning with the right signals and knowledge, they fail when facing shifting distributions, unforeseen corruptions, and worst-case scenarios. In this talk, I will show how to build reliable and robust machine learning by tightly integrating context into the models. The context has two aspects: the intrinsic structure of natural data, and the extrinsic structure of domain knowledge. Both are crucial: By capitalizing on the intrinsic structure in natural images, I show that we can create robust computer vision systems, even in the worst case, an analytical result that also enjoys strong empirical gains. Through the integration of external knowledge, such as causal structure, my framework can instruct models to use the right signals for visual recognition, enabling new opportunities for controllable and interpretable models. I will also talk about future work in making machine learning robust, which I hope to transform us into an intelligent society.   Bio:  Chengzhi Mao is a final-year Ph.D. student from the Department of Computer Science at Columbia University. He is advised by Prof. Junfeng Yang and Prof. Carl Vondrick. He received his B.S in E.E. from Tsinghua University. His research focuses on reliable and robust machine learning. His work has led to over ten publications and Orals at top conferences, which established a new generalization of robust models beyond feedforward inference. His work also connects causality to the vision domain. He serves as reviewers for several top conferences, including CVPR, ICCV, ECCV, ICLR, NeurIPS, IJCAI, and AAAI.