The age of "big data" is here: data of unprecedented sizes is becoming ubiquitous, which brings new challenges and new opportunities. With this comes the need to solve optimization problems of unprecedented sizes. Machine learning, compressed sensing, social network science and computational biology are some of many prominent application domains where it is increasingly common to formulate and solve optimization problems with billions of variables. Classical algorithms are not designed to scale to instances of this size and hence new approaches are needed. These approaches utilize novel algorithmic design involving tools such as distributed and parallel computing, randomization, asynchronicity, decomposition, sketching and streaming. This workshop aims to bring together researchers working on novel optimization algorithms and distributed systems capable of working in the Big Data setting.