Abstract: Machine Learning (ML) is fundamentally changing several industries and businesses in many ways, for example in the Oil and Gas (O&G) industry, in Health Care, Social Media, IoT, etc. Computational Science and Engineering (CSE) is part of this ongoing digital transformation. As more and more data becomes available the blending of Data Science with CSE is inevitable, also because of their common grounds in Mathematics, Statistics, and Computer Science. Since it has its own challenges, this new scientific endeavor gained its own denomination: Scientific Machine Learning (SciML). Data-driven models are still an option, but sometimes they fail since the requirements of physical laws are needed to constraint the predictive model. Methodologies for building Surrogate Models, like Model Order Reduction, for example, gained new flavors. Another area with considerable momentum in the past few years and with great synergy with SciML is Uncertainty Quantification (UQ). This mini-symposium intends to gather researchers and professionals involved in the application and advancement of SciML and UQ. We welcome works ranging from numerical analysis, industrial applications, engineering, biology, and IoT. We expect a wide range of presentations from fundamentals to advanced applications.