• Lael Wakamatsu (PhD), Models of harmful algal blooms in coastal Tasmania, 2020-ongoing, IMAS, University of Tasmania,

  • Christine Padalino (MSc), pCO2 dynamics in the North Pacific at meso- and submeso-scales, 2022-ongoing, MIT

  • Simona Meiler (MSc), Evaluating marine diazotroph biomass distributions using nifH gene abundance observations, 2019-2020, MIT visiting student from ETH Zurich


  • Christine Padalino, Covariation of mixed layer depth and phytoplankton concentrations in the North Pacific, 2020-2022, MIT

  • Michelle Yin, Machine learning models of chlorophyll variability in the North Pacific, 2020, MIT

  • Morgan Mayborne, Stephanie Howe, Omozusi Guobadia, Edward Guthru, Stochastic models of COVID-19, 2020, MIT

  • Kathryn Tso, An updated decomposition analysis of the ocean’s biological pumps, 2020, MIT

  • Syed Faizanul Haque, Stochastic models of marine microbial populations, 2017-2018, UCI

  • Henry Sue, Empirical models of the ocean’s vertical organic matter flux profile, 2017–2018, UCI

  • Megha Rudresh, Models of plankton competition in a mixing environment, 2017, UCI

  • Lael Wakamatsu, Testing the temperature-ballast hypothesis in the Southern Ocean, 2016, UCI

Workshop and Course Materials

With colleagues I have developed a number of workshops and course materials to teach statistical methods to environmental science students and researchers. Code and workshop materials are linked below. They will be updated and improved over time.

Bayesian modeling of ecological systems using the 'Stan' software package
Developed with Paul Mattern as part of the Independent Activities Period at MIT. The course develops a Bayesian analysis of a marine ecosystem model via simulation, model fitting, prior specification, predictive analyses, and uncertainty quantification. The model analysis is done in Python. Fitting the model is done in Stan.

Materials: https://github.com/gregbritten/BayesianEcosystems_IAP

Bayesian Modelling of Dynamic Marine Systems (01/08-10/2020)
Developed with Paul Mattern as part of the Collaboration on Computational Biogeochemical Modelling of Marine Ecosystems (CBIOMES). The workshop materials apply the MCMC package Stan to a series of case studies. All codes to call Stan are written in Julia, R, and Python, inclusively. Lecture materials include a brief introduction to Bayesian inference and the science behind the case studies.

Materials: https://github.com/jpmattern/bayesian_cbiomes

Introduction to Spatial-Temporal Statistics (04/27/2017)
Developed with Yara Mohajerani as part of the Data Science Initiative at UCI. Topics cover introductory concepts in applied time series and spatial analysis using a few key R packages. The notes also include instructions linking Python to R via the package rPy2, allowing Python users to access the many statistics packages in R.

Materials: https://github.com/gregbritten/Introduction-Spatial-Temporal-Stats