We live in a Big Data world where amount of data available is growing at an exponential rate, and the real problem has now shifted from collecting enough data to handling this data and developing computational tools to extract information from this data. Existing algorithms for several computational tasks are based on models that do not apply in the realm of big data, and we need new and better computational tools - including models, algorithms and systems. This has been a growing field of research over the last decade or so. The spring school aims to give an introduction to basic techniques and current research directions in algorithms for dealing with big data problems. The lectures will be given by internationally leading experts from computer science and mathematics, both from academia and industry addressing in particular young researchers in India and Germany. The spring school is jointly organized by the Indraprastha Institute of Information Technology, Delhi and the priority program "Algorithms for Big Data" of the German Research Foundation (DFG) with support from ACM-India, and Shiv Nadar University.

The Indo-German Spring School on Algorithms for Big Data aims to train young researchers, in particular PhD students and postdocs, in algorithmic solution techniques for big data problems. To this end, we bring leading researchers together that work on big data aspects in different scientific subfields. Besides the training, such a forum would allow the exchange of ideas for solving future challenges in the big data context. We will also invite selected industrial researchers as speakers. Furthermore, interested practitioners are welcome to attend and share their experience in discussion rounds. This opening to industry is meant to improve the exchange between theory and practice in both directions. Academic researchers learn about application requirements in practice and, in turn, top-notch academic results may enter industrial solutions sooner. This way the workshop would also serve as a platform for seeding new collaborations, both between academia and industry but also within academia across the two countries and beyond.