Statistical Computing
Berkeley Statistics faculty work across a range of topics related to the use of computing in Statistics and Data Science, from the development of software languages and tools to innovations in computationally-intensive statistical methods. Current faculty have been leaders in the Jupyter and IPython projects, the Bioconductor Project, and the NIMBLE platform for hierarchical modeling. Our work on computationally-intensive methods includes research on randomized algorithms for big data, assessment of random number generators, and optimization.
In addition, Berkeley faculty have a long history of innovation in emphasizing computing in undergraduate statistical education.
Researchers
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
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
statistics, empirical process, high-dimensional modeling, technology in education
environmental statistics, statistical computing, spatial statistics, Bayesian statistics
scientific computing, educational data science, earth sciences, physical sciences
algorithms, applied probability, random walks, Markov chains, computational applications of randomness, Markov chain Monte Carlo, statistical physics, combinatorial optimization, nonlinear dynamical systems
uncertainty quantification and inference, inverse problems, nonparametrics, risk assessment, elections, geophysics, astrophysics, cosmology, litigation, health
mathematics, combinatorics, computational algebraic geometry
high-dimensional statistics, nonparametric estimation, distribution-free inference, machine learning, optimization, numerical methods, probabilistic forecasting, computational epidemiology