Abstract: "The increasing power of computing platforms and the recent advances in data science techniques have fostered the development of data-driven computational models of engineering systems with considerably improved prediction accuracies. An important feature of these modeling approaches is the reliance on data to develop reduced-order models of physical phenomena involved and/or the characterization of the uncertainty associated with the models or their parameters. In the latter case, the quantification of the impact of such uncertainty on the quantities of interest is key to assess the validity of a given model or perform reliability analysis, among other tasks. However, for complex engineering systems, such as those featuring multi-physics and multi-scale phenomena, simulation models are computationally expensive. These, in turn, pose significant challenges on standard UQ and reliability estimation approaches. I will start this talk with a brief discussion on the challenges associated with uncertainty quantification (UQ) of complex systems and a high-level introduction to recent work performed by the UQ and Data-driven Modeling group at CU Boulder to tackle these challenges. I will focus on model reduction approaches (linear and non-linear) for efficient UQ, generative modeling, and failure probability estimation. I will present application examples to highlight the efficiency of these multi-fidelity approaches and their wide applicability to a broad range of problems."