Applications in the Physical and Environmental Sciences
Berkeley Statistics has a long history of applications in the physical sciences, ranging from high-energy particle physics and statistical mechanics to cosmology (microwave cosmology, SNIa cosmology), astronomy and astronomical data processing (large-scale inference for optical astronomy surveys, crowded-starfield deblending, galaxy diversity, helioseismology), solid-Earth geophysics (seismology, earthquake risk, geomagnetism, geophysical fluid flow), and climate (global circulation models, tropical cyclogenesis, changes in extreme weather events, impact of climate change, climate models in policy contexts).
Current research interests are focused on atmospheric sciences (carbon monitoring, greenhouse gas emissions, atmospheric rivers), glaciology (mass balance, ice sheet modeling), and ecology (soil organic carbon, historical forest carbon, regenerative agriculture, paleoecology, food webs, endangered species).
We collaborate with a broad range of subject matter experts—including physicists, geophysicists, climate scientists, soil scientists, astronomers, and chemists—in other departments, the Lawrence Berkeley National Laboratory, and around the world.
Researchers
selective inference, multiple testing, multivariate analysis, risks of artificial intelligence, ecological statistics
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
machine learning, statistical prediction, variational inference, statistical computing, optimization, sequential inference, causal inference, physical sciences, biology, control and reinforcement learning
environmental statistics, statistical computing, spatial statistics, Bayesian statistics
scientific computing, educational data science, earth sciences, physical sciences
uncertainty quantification and inference, inverse problems, nonparametrics, risk assessment, elections, geophysics, astrophysics, cosmology, litigation, health
Bayesian inference, inverse problems, stochastic processes, biological systems, empirical game theory, nonequilibrium thermodynamics, optimization, and computational topology