Statistical Computing

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.


Sandrine Dudoit photo

statistics, applied statistics, data science, statistical computing, computational biology and genomics


machine learning, variational inference, Bayesian methods, and robustness quantification

Deb Nolan

statistics, empirical process, high-dimensional modeling, technology in education

photo of Christopher Paciorek

environmental statistics, statistical computing, spatial statistics, Bayesian statistics

Photo of Fernando Pérez

data science, Educational Data Science, scientific computing

Alistair Sinclair

algorithms, applied probability, statistics, random walks, Markov chains, computational applications of randomness, Markov chain Monte Carlo, statistical physics, combinatorial optimization

photo of P.B. Stark

uncertainty quantification and inference, inverse problems, nonparametrics, risk assessment, earthquake prediction, election auditing, geomagnetism, cosmology, litigation, food/nutrition

Bern Sturmfels

mathematics, combinatorics, computational algebraic geometry

Photo of Ryan Tibshirani.

high-dimensional statistics, nonparametric estimation, distribution-free inference, machine learning, convex optimization, numerical methods, tracking and forecasting epidemics