Our mission is to push machine learning to the extreme-scale. We design and implement exponentially resource-frugal and scalable machine learning (ML) algorithms by using randomized hashing and sketching algorithms, suited for modern big-data constraints. Apart from being exponentially cheap, the algorithms are embarrassingly parallelizable. The extremely low resource requirements makes our techniques ideal for IoT devices as well. Furthermore, the algorithms are naturally privacy-preserving as they do not work directly with data attributes and instead only operate on secure hashes or sketches. See Publications page for more information regarding results and installations.
