Tuhin Sahai: Dynamical Systems and Continous Approximations for NP-hard Problems In this talk, we will explore the use of dynamical systems for approximating the solutions of NP-hard problems that arise in a wide variety of Industrial settings. In particular, we will investigate the use of dynamical systems for clustering networks and performing inference on machine learning applications. Additionally, we will develop novel approximations to the iconic traveling salesman problem (TSP) and use the results to learn the structure of Bayesian networks. We will also explore the Industrial settings in which these approaches are useful. Sumanth Swaminathan: The Challenges in Multi-Scale Modeling of Liquid Filtration Applications Liquid filtration using porous media is of great interest to W.L. Gore in a wide variety of applications. Given the complexity of Gore’s fluoropolymer based technologies, one could imagine that a purely empirical approach to correlating product performance and material structure would be slow and expensive without the intervention of predictive models. In this talk, we discuss the complexity of correlating porous media material properties to performance in liquid filtration applications. We specifically highlight the challenges faced in characterization of media, modeling length scales, computational needs, and experimental validation.