Machine Learning
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.
Researchers
machine learning, statistical learning theory, adaptive control
phase transitions, networks, graphs, graphons, algorithmic game theory, machine learning, applications in cancer immunotherapy, ethical decision-making, climate change, materials science
high-dimensional statistical learning, statistical computing, computational biology and genomics, precision medicine and health
machine learning, variational inference, Bayesian methods, robustness quantification, and sensitivity analysis
computer science, artificial intelligence, computational biology, statistics, machine learning, electrical engineering, applied statistics, optimization
scientific/engineering machine learning, randomized numerical linear algebra, random matrix theory, stochastic optimization, spectral graph theory, time series forecasting, fluid solid subsurface and chemistry/physics applications, internet and social media analysis
machine learning, statistical prediction, variational inference, statistical computing, optimization, sequential inference, causal inference, physical sciences, biology, control and reinforcement learning
language models and diffusion models, deep learning theory, reinforcement learning theory, high-dimensional statistics, quantum algorithms, and uncertainty quantification
AI and machine learning, applied probability, computational biology, computational genomics, evolutionary biology, human genetics
mechanistic interpretability, large language models, alignment and safety
high-dimensional statistics, nonparametric estimation, distribution-free inference, machine learning, optimization, numerical methods, probabilistic forecasting, computational epidemiology
veridical data science (PCS), trustworthy AI, interpretability, deep learning theory, tree-based methods, interdisciplinary research in medical AI, computational biology and genomics, neuroscience
nonparametric estimation, hypothesis testing, applied probability, statistical learning theory, online learning