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.
Researchers
machine learning, statistical learning theory, adaptive control
statistics, applied statistics, data science, statistical computing, computational biology and genomics
machine learning, variational inference, Bayesian methods, and robustness quantification
artificial intelligence, control and intelligent systems and robotics, communications and networking
computer science, artificial intelligence, computational biology, statistics, machine learning, electrical engineering, applied statistics, optimization
machine learning, statistical prediction, variational inference, statistical computing, optimization
data science, statistics, machine learning
computational biology, machine learning, applied statistics, applied probability
artificial intelligence, machine learning
high-dimensional statistics, nonparametric estimation, distribution-free inference, machine learning, convex optimization, numerical methods, tracking and forecasting epidemics
statistical machine learning, statistics, Optimization and algorithms, artificial intelligence
statistical inference for high dimensional data and interdisciplinary research in neuroscience, remote sensing, and text summarization
Mathematical Statistics, Applied Probability, and Statistical Learning Theory