High-Dimensional Data Analysis
High-dimensional statistics focuses on problem settings in which the number of features is of comparable size, or larger than the number of observations. Problems of this type present a variety of new challenges, since classical theory and methodology can break down in surprising and unexpected ways.
Berkeley researchers study both the statistical and computational challenges that arise in the high-dimensional setting. On the theoretical side, they bring to bear a range of techniques from statistics, probability, and information theory, including empirical process theory, concentration inequalities, as well as random matrix theory and free probability. Methodological innovations include new estimators in high-dimensional regression, classification, and multivariate analysis, as well as randomized algorithms for optimization, and techniques for prediction, inference, and decision-making in sequential settings. The work is motivated and applied to various scientific and engineering disciplines, including computational biology, astronomy, financial time series, epidemic forecasting, and climate forecasting.
Researchers
statistics, machine learning, semiparametric models, asymptotic theory, hidden Markov models, applications to molecular biology
statistics, applied statistics, data science, statistical computing, computational biology and genomics
theoretical and applied statistics
Integrable probability, random matrices, asymptotic representation theory
nonparametric and high-dimensional statistics, shape constrained statistical estimation, empirical processes, statistical information theory
high dimensional and integrative genomic data analysis, network modeling, hierarchical multi-label classification, translational bioinformatics
artificial intelligence, control and intelligent systems and robotics, communications and networking
data science, statistics, machine learning
AI/machine learning, computational biology, applied statistics, applied probability
uncertainty quantification and inference, inverse problems, nonparametrics, risk assessment, earthquake prediction, election auditing, geomagnetism, cosmology, litigation, food/nutrition
Stochastic Processes, Hierarchical Bayesian Inference, Random Graph Theory, Multi-Agent Training, Computational Biology, Solution Continuation, Optimization
high-dimensional statistics, nonparametric estimation, distribution-free inference, probabilistic forecasting, machine learning, convex optimization, numerical methods, tracking and forecasting epidemics
Mathematical Statistics, Applied Probability, and Statistical Learning Theory