Bayesian Modeling for Environmental Health

Master Bayesian modeling in Environmental Health through interactive seminars and hands-on sessions in this two-day workshop. Gain practical insights into concepts, techniques, and data analysis methods.



Weekly Effort

16 hours




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Course Description

A two-day intensive course of seminars and hands-on analytical sessions to provide an approachable and practical overview of concepts, techniques, and data analysis methods used in Bayesian modeling with applications in Environmental Health.

  • Learn the principles of Bayesian modeling in Environmental Health through interactive seminar lectures, gaining a solid understanding of the underlying concepts.
  • Engage in hands-on computer sessions to apply theoretical knowledge practically. Explore existing data examples and initiate discussions on launching investigations based on participants' research questions.
  • Acquire practical know-how on dealing with different data structures and explore various software options available for Bayesian modeling, empowering learners to navigate diverse analytical tools.
  • Benefit from the extensive experience of the workshop's scientist-led team. Receive a comprehensive overview of Bayesian inference principles, different analysis types, and insights into current and future research in Environmental Health.

Course Prerequisites

  • Basic familiarity with R and R studio (how to download R/R studio, and how to install a package) is recommended to get the most out of the workshop.
  • Familiarity with spatial and temporal data structures, as well as exponential family distribution types (normal, Poisson etc.) would also be useful, though not essential.
  • Each participant is required to bring a personal laptop as all lab sessions will be done on your personal laptop. Each participant will be using RStudio Cloud to carry out tasks while attending the Workshop. Instructions for the basics of RStudio Cloud(link is external and opens in a new window).

What You Will Learn

This training is needed for those who are interested or who have heard about Bayesian modeling and work in Environmental Health, but who have little theoretical or practical experience of it and would like some tools and know-how to get started in an approachable and friendly setting.

This two-day intensive workshop introduces the ideas of Bayesian inference and modeling in the context of Environmental Health, designed to be as approachable and friendly as possible while still providing technical and practical know-how. Led by a team of scientists with many years of diverse combined experience, the workshop will integrate seminar lectures with hands-on computer sessions to put concepts into practice. Several examples will be given using existing data, and conversations on starting new investigations with attendees' research questions will also be encouraged. The lectures and lab sessions will give an overview of the principles of Bayesian inference, as well as how to deal with different data structures, the various software options available, different types of analyses, and current and future research.

By the end of the workshop, participants will be familiar with the following topics:

  • Principles of Bayesian inference
  • Practicalities of Bayesian inference
  • Priors and hyperpriors
  • Different data structures (spatial, point, continuous, categorical)
  • Advantages and drawbacks of Bayesian approaches
  • Temporal modeling
  • Spatial modeling
  • Spatiotemporal modeling
  • Hierarchical modeling
  • Forecasting
  • Software options
  • Examples of use
  • Examples of current and future research


Robbie Parks
Robbie Parks
Training Director, Mailman School of Public Health

Robbie Parks is an environmental epidemiologist with extensive experience in large-scale, multi-disciplinary quantitative research, focusing on climate-related exposures, public health, and equity. Holding a tenure-track Assistant Professor position at Columbia University's Mailman School of Public Health, Robbie is also an NIH NIEHS K99/R00 Fellow. Serving as the Lead Instructor for the Columbia University SHARP Course on Bayesian Modeling for Environmental Health, Robbie brings expertise to the forefront. Previously a Columbia University Earth Institute/Climate School Post-doctoral Fellow from 2019 to 2022, collaborating with Prof. Marianthi-Anna Kioumourtzoglou, Robbie earned their PhD at the School of Public Health at Imperial College London in 2019 under the guidance of Profs. Majid Ezzati and Ralf Toumi. They graduated with a BA/MA (Oxon) in Physics from the Keble College, University of Oxford. Proudly embodying the roles of a first-gen academic and an Agents of Change in Environmental Justice Senior Fellow, Robbie is committed to making meaningful contributions to their field.

Summer 2024 instructing team is being finalized, but will be comparable to the 2023 lineup listed here.

Jaime Benavides
Jaime Benavides
Postdoctoral Research Scientist

Jaime completed his PhD in environmental engineering at Polytechnic University of Catalonia in the department of Earth Sciences at the Barcelona Supercomputing Center. His doctoral research focused on the development and application of a street-level air quality model for Barcelona, Spain. His primary research interests are in understanding the link between environmental exposures and human health in urban settings. At Columbia, he is applying novel methods to find patterns in urban environmental exposure mixtures aiming to investigate the impact of these patterns on adverse health outcomes. He is also involved in improving air pollution exposure assessment for large-scale population-wide epidemiologic studies.

Marianthi-Anna Kioumourtzoglou
Marianthi-Anna Kioumourtzoglou
Associate Professor of Environmental Health Sciences

Marianthi-Anna Kioumourtzoglou is an environmental engineer and epidemiologist. She holds a Master of Science in Public Health (MSPH) from the Environmental Sciences and Engineering Department at the University of North Carolina at Chapel Hill and a Doctor of Science (ScD) in Environmental Health from the Harvard TH Chan School of Public Health, where she also conducted her post-doctoral fellowship. She is currently an Associate Professor at the Department of Environmental Health Sciences at Columbia University's Mailman School of Public Health. Her research focuses on applied statistical issues related to environmental epidemiology, including quantifying and correcting for exposure measurement error, exposure prediction uncertainty propagation, and assessment of high-dimensional and complex exposures in health analyses. Her studies mainly (albeit not exclusively) focus on air pollution exposures and, additionally, on identifying vulnerable sub-populations and characterizing how risks may vary across neighborhood-level and other urban characteristics, as well as in a changing climate.

Garyfallos Konstantinoudis
Garyfallos Konstantinoudis
Faculty of Medicine

Garyfallos Konstantinoudis holds an Imperial Research College Fellowship at the MRC Centre for Environment and Health. Prior to this he was an MRC Skills Development Research Fellow at the MRC Centre for Environment and Health. He did his PhD in Biostatistics and Epidemiology at the Institute of Social and Preventive Medicine (ISPM) at the University of Bern in Switzerland. Garyfallos gained an MSc in Biostatistics at the University of Glasgow and BSc in Mathematics at the Aristotle University of Thessaloniki.

Garyfallos' current domain of research is on estimating the health related burden of climate change focusing on temperatures. Some of his work also include developing methods for calculating excess mortality due to extreme events such as the COVID-19 pandemic or the recent extremely warm summers.

Theo Rashid
Theo Rashid
Central Faculty

Theo is a PhD student in the Global Environmental Health group at the School of Public Health, supervised by Majid Ezzati, James Bennett, Seth Flaxman, and Mireille Toledano. Supported by the Imperial College President's scholarship, Theo's research, as part of the Pathways to Equitable Healthy Cities collaboration, focuses on small-area mortality trends in London and England, employing Bayesian parametric and nonparametric models. Their interest lies in understanding how environmental and economic deprivation affect various population subgroups. Theo's academic background includes theoretical physics at Imperial College, where they wrote a thesis on hurricane dynamics.

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