In this talk I’ll describe a novel approach that yields algorithms whose parallel running time is exponentially faster than any algorithm previously known for a broad range of machine learning applications. The algorithms are designed for submodular function maximization which is the algorithmic engine behind applications such as clustering, network analysis, feature selection, Bayesian inference, ranking, speech and document summarization, recommendation systems, hyperparameter tuning, and many others. Since applications of submodular functions are ubiquitous across machine learning and data sets become larger, there is consistent demand for accelerating submodular optimization. The approach we describe yields simple algorithms whose parallel runtime is logarithmic in the size of the data rather than linear. I’ll introduce the frameworks we recently developed and present experimental results from various application domains.