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 machine learning on 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
statistics, applied statistics, data science, statistical computing, computational biology and genomics
machine learning, variational inference, Bayesian methods, and robustness quantification
statistics, empirical process, high-dimensional modeling, technology in education
environmental statistics, statistical computing, spatial statistics, Bayesian statistics
data science, Educational Data Science, scientific computing
algorithms, applied probability, statistics, random walks, Markov chains, computational applications of randomness, Markov chain Monte Carlo, statistical physics, combinatorial optimization
uncertainty quantification and inference, inverse problems, nonparametrics, risk assessment, earthquake prediction, election auditing, geomagnetism, cosmology, litigation, food/nutrition
mathematics, combinatorics, computational algebraic geometry
high-dimensional statistics, nonparametric estimation, distribution-free inference, machine learning, convex optimization, numerical methods, tracking and forecasting epidemics