The Embedded Information Processing group is a young team of researchers working on embedded and mobile machine learning, efficient deep learning, low-power sensing systems, information processing in IoT devices. State-of-the-art computational models that, for example, recognize a face, or detect events of interest are increasingly based on deep learning principles and algorithms. Unfortunately, deep models exert severe demands on local device resources and this limits their adoption within mobile and embedded platforms. Our group works on solving the challenges when running these models on highly resource-constrained devices. We care about energy consumption, environmental sustainability and data privacy, which is reflected in applications of our work and deployed prototypes of the developed systems.