Distinguished Lecturer
A. Description
The Distinguished Lecturer prepares a series of lectures preferably on a variety of subjects in the mathematical geosciences to be presented in places where IAMG Annual Meetings are not normally held.
B. Guidelines
The IAMG council voted in 2000 to establish a Distinguished Lecture series and approved a committee charged with implementing the recommendations contained in the report of the IAMG Lecture Series Commission (July 2000).
The purpose of the IAMG Distinguished Lecture series is to demonstrate to the broader geological community the power of mathematical geology to address routine geological interpretation and to deliver this knowledge to audiences in selected parts the world. Therefore, the Lectures Committee is seeking nominations for outstanding individuals who meet the following criteria:
- A demonstrated ability to communicate mathematical concepts to a general geological audience.
- A clear enthusiasm for mathematical geology.
- Recognition for work in their field.
- Established skill in working with individuals and in group discussions on geological problems.
The Distinguished Lecturer must be ready to travel and to perform the following duties:
- Prepare and present a lecture suitable for a general geological audience.
- Prepare and present one or two lectures on a more specialized topic.
- Interact and hold discussions with individuals, both professionals and students, on applications of mathematical geology to local problems of interest.
Letters of nomination should include a curriculum vitae of the nominee and a short statement summarizing the ways in which he or she fulfills the nomination criteria.
Letters should be directed to the Chair of the Distinguished Lecture Series Committee by e-mail to :
Or by regular mail to :
Christien Thiart
Department of Statistical Sciences University of Cape Town
Private Bag Rondebogch 7700
South Africa
C. Current Distinguished Lecturer (2023) – Jennifer McKinley
Professor Jennifer McKinley, Director of the Centre for GIS and Geomatics, is based in Geography, in the School of Natural and Built Environment, Queen’s University Belfast. Her research expertise comprises the development and application of spatial analysis techniques, geostatistics and compositional data analysis in ground and remote sensed earth processes, health and the environment, criminal and environmental forensics. Jennifer’s recent research seeks to gain a greater understanding of the link between human health and environmental impacts from natural and anthropogenic sources, including air pollution and more recently surveillance of SARS-CoV-2. She has authored more than 150 scientific articles, including peer-reviewed journal articles and numerous international conference contributions which have helped shape policy development. Interdisciplinary collaboration, strong partnership working and a commitment to generate actionable insight, are familiar hallmarks of her research. Jennifer’s international leadership roles include: Councillor of the International Union of Geosciences (IUGS 2020-2024), current President of the Governing Council of the Deep-time Digital Earth Initiative (DDE) and Past President of the International Association for Mathematical Geosciences (IAMG). Jennifer has served on learned committees including the Royal Irish Academy and Geological Society of London and sits on the Giant’s Causeway UNESCO World Heritage Site Steering Group.
Contact by email at j.mckinley@qub.ac.uk
Lecture Series
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Environmental Geochemistry and Compositional Data Analysis
Geochemical data are recognised to be compositional in nature in that they convey relative information. As a result, correlations between raw geochemical compositional data are spurious, prone to artefacts and potentially unrelated to any natural processes. Compositional data analysis (CoDA) methods are frequently used to extract information from geochemical data by treating log ratio or equivalently transformed data of analysing the raw constant sum values. However, the results obtained from the use of compositionally-compliant methods can be difficult to interpret. In this lecture series, Jennifer will present case studies involving the use of compositional data analysis, including the use of log ratios and balances, to analyse geochemical data. Case studies in natural resource estimation and assessment, medical geology and environmental management with be used to demonstrate the approaches.
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Exploring the effects of environmental factors on health
Jennifer’s research in this area examines possible links between soil elements, social deprivation and chronic disease (including cancer and chronic kidney disease) to investigate the impact of environmental toxins including from anthropogenic sources such as air pollution on human health. The approaches presented acknowledge the compositional nature of the environmental data such as geochemistry data and offer the opportunity to identify environmental toxins with relative abundances most associated with elevated incidences of chronic disease. Human-environmental relations are explored including recent work in the application of spatial data analysis in the integrated wastewater testing and geographic surveillance programme for SARS-CoV-2. Jennifer’s collaboration in this area involves a multidisciplinary team from geography, geoscience, biological science, mathematics, public health practitioners and government policy makers. The findings from this work are important to gain a greater understanding of the link between human health and environmental toxins.
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Forensic geoscience and Spatial Data Analysis
Jennifer’s role within the IUGS Initiative on Forensic Geology (IFG) has increased awareness in the use of GI Science and geoinformatics in forensic geoscience in collaboration with UK, European Forensic Science Regulators, and law enforcement agencies worldwide. In this lecture series, Jennifer will present case study examples involving ground-based geological and geochemical data in addition to remotely sensed data.
D. Upcoming Distinguished Lecturer (2024) – Michael Pyrcz
Michael Pyrcz, University of Texas at Austin