Artificial Intelligence/Machine Learning

Artificial Intelligence/Machine Learning

Statistical machine learning merges statistics with the computational sciences---computer science, systems science and optimization. Much of the agenda in statistical machine learning is driven by applied problems in science and technology, where data streams are increasingly large-scale, dynamical and heterogeneous, and where mathematical and algorithmic creativity are required to bring statistical methodology to bear. Fields such as bioinformatics, artificial intelligence, 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 between inference and computation.

Research in statistical machine learning at Berkeley builds on Berkeley'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.


Peter Bartlett

machine learning, statistical learning theory, adaptive control

Sandrine Dudoit photo

statistics, applied statistics, data science, statistical computing, computational biology and genomics


machine learning, variational inference, Bayesian methods, and robustness quantification

Jiantao Jiao

artificial intelligence, control and intelligent systems and robotics, communications and networking

Michael Jordan

computer science, artificial intelligence, computational biology, statistics, machine learning, electrical engineering, applied statistics, optimization

Jon McAuliffe

machine learning, statistical prediction, variational inference, statistical computing, optimization

Song Mei

data science, statistics, machine learning


computational biology, statistical genetics, applied probability

Jacob Steinhardt

artificial intelligence, machine learning

Photo of Ryan Tibshirani.

high-dimensional statistics, nonparametric estimation, distribution-free inference, machine learning, convex optimization, numerical methods, tracking and forecasting epidemics

Martin Wainwright

statistical machine learning, statistics, Optimization and algorithms, artificial intelligence

Bin Yu

statistical inference for high dimensional data and interdisciplinary research in neuroscience, remote sensing, and text summarization

Nikita Photo

Mathematical Statistics, Applied Probability, and Statistical Learning Theory