Statistical machine learning merges statistics with the computational sciences—computer science, systems science, and optimization. Much of the agenda in this area is driven by applied problems in science and technology, where data streams are increasingly large-scale, high-dimensional, dynamical, and heterogeneous. In this regime, statistical, mathematical, and algorithmic creativity are required to build robust models and methodologies, and to bridge the gap between rigorous theory and the unprecedented success of modern models. Fields such as artificial intelligence, deep learning, bioinformatics, signal processing, communications, networking, information management, finance, game theory, and control theory are all being heavily influenced by developments in statistical machine learning.

The field of statistical machine learning also poses some of the most challenging theoretical problems in modern statistics, chief among them being the general problem of understanding the link and trade-offs between inference and computation.

Research in statistical machine learning at Berkeley builds on the campus’s world-class strengths in probability, mathematical statistics, computer science, and systems science. Moreover, by its interdisciplinary nature, statistical machine learning helps to forge new links among these fields, and to advance the role of statistics as the foundational language of scientific discovery in the data-driven era.