Centre for Statistics

Clinic Team

Our team of experts cover a diverse set of topics in statistics; including but not limited to those listed below.

Nicole Augustin

Methods: Spatio-temporal models, spatial models, health statistics, spatial confounding, model selection uncertainty

Applications: ecology, Accelerometer data, Fisheries stock management, Forest health monitoring, Physical activity, Tobacco control

Tim Cannings

Methods: classification and clustering, high-dimensional data, nonparametric, random projections, data perturbation, noisy and incomplete data

Applications: big data analytics, cancer therapy, genomic data, precision medicine

Victor Elvira

Methods: Bayesian inference, computational statistics, Monte Carlo methods, state-space models, statistical statistics

Applications: communications, localization, remote sensing, tracking, wireless sensor networks

Ruth King

Methods: Bayesian inference, capture-recapture, hidden (semi-)Markov models, integrated models, missing data, state-space models

Applications: epidemiology, medicine, ecology

Amanda Lenzi

Methods: spatiotemporal statistics, Bayesian inference, deep learning, statistics of extremes

Applications:  geoscience, energy, environmental science

Daniel Paulin

Methods: Markov chain Monte Carlo, Hamiltonian dynamics, concentration inequalities, matrix-free optimization methods

Applications: statistical genetics, Weather forecasting, stochastic volatility models in finance

Gordon Ross

Methods: Bayesian inference, time series, nonparametrics, point process

Applications: finance, risk, cybersecurity, earthquakes, seismology

Torben Sell

Methods: Markov chain Monte Carlo, Piecewise deterministic Markov processes, Particle filtering, Function space sampling methods

Applications: Stochastic control, Bayesian neural networks

Simon Wood

Methods: fast stable computation, MGCV, penalized regression, Smoothing

Applications: energy, environment