Søren Taverniers joined PARC as a member of research staff in the Intelligent Systems Lab. He has expertise in uncertainty quantification (UQ) and predictive modeling of multiscale/multiphysics problems with applications in materials science and high-speed particle-laden flows, among others. Recently, he also started applying deep learning (neural networks) to build domain-aware surrogate models for aiding design of complex systems. At PARC, he wants to use a combination of multiphysics modeling, physics-informed machine learning and UQ to help improve additive manufacturing techniques and develop next-generation technology with a direct impact on society. Prior to joining PARC, Søren was a postdoctoral scholar at Stanford University where he developed accelerated Monte Carlo approaches for estimating the probability distribution of quantities of interest in multiscale applications, and information-theoretic, deep learning-based techniques for accelerating computer-aided design. Previously, he was a member of the San Diego State University Research Foundation where he built novel cloud-in-cell approaches for shocked particle-laden flows. Søren graduated from the Katholieke Universiteit Leuven in Belgium with an MS degree in Physics. He also holds an MS and PhD degree in Engineering Physics from the University of California, San Diego, where he was nominated for the Chancellor’s Dissertation Medal recognizing outstanding doctoral research. He published his academic findings in leading peer-reviewed venues such as Journal of Computational Physics and Water Resources Research. In his spare time, Søren enjoys exploring ancient cultures in South America and Asia, and the main cultural hubs of Europe. He also likes to learn new languages.