Professor Ionnis Krevikidis spoke about data, manifold learning, and the modeling of complex/multiscale systems on March 10, 2016 as a part of the Distinguished Lecture series at the UTC Institute for Advanced Systems Engineering. He discussed some recent developments on the connection between data mining/machine learning on the one hand, and the modeling of complex/multi-scale problems on the other. The talk addressed the interface between fine scale, atomistic/stochastic codes and coarse-grained, macroscopic descriptions. In particular, Professor Krevikidis discussed (a) the reduction of stochastic simulations through diffusion maps and the use of the Mahalanobis distance, and issues of heterogeneous data fusion; (b) the issue of extending diffusion-map based simulations to new configurations/conditions; and (c) the issue of not only reducing the number of independent variables, but also reducing the number of independent parameters by taking advantage of data-mining tools.