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.
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