2024 Data Science and Statistics Lecture

Statistical Science is the science of uncertainty. More specifically, it is the principled collection, analysis, and interpretation of data, taking into account the uncertainties within and between each of these steps. A critical component of excellent science is the ability to weigh evidence appropriately – statistical thinking lies at the heart of this. The annual Data Science and Statistic Lecture showcases the interdisciplinarity and key role that statistics in data science plays in extracting scientific knowledge from data in the presence of uncertainty.

2024 Data Science and Statistical Lecturer

Distinguished Professor and Chair of the Department of Statistics at the University of Missouri (MU)

Lecture Title:

The Ship Has Sailed: Where Should we Steer It?

(Climate Adaptation needs Uncertainty Quantification)

Abstract:

Earth’s climate is changing due to anthropogenic influences. Although there are still well-meaning attempts to mitigate the drivers of this change (e.g., reduction of greenhouse gas emissions), it is widely believed that such changes will be “too little, too late.” Thus, for many, the focus has shifted to “climate change adaptation” in which decision makers modify their response to or anticipation of the numerous risks associated with climate change. There are many different approaches that can be taken when one adapts to climate change, ranging from resistance to retreat. The decision on the most appropriate way forward (how to steer the ship) requires a coherent, cohesive, and collective response across localities, sectors of society, and scales of governance. Such decisions require information from many different sources (e.g., from climate models, from impact assessments, from political and social scientists, … ), and these sources come with uncertainty. In addition, this process is inherently multi-disciplinary and requires teams of scientists and decision makers working together. Although it is well known that informed decisions must account for uncertainty, quantification of that uncertainty across multiple disciplines, information sources, and complex decision pathways is in its infancy. This relatively non-technical talk will describe some of the challenges and will argue that statistical science offers a path forward through multi-level (deep) modelling. Such approaches will likely borrow from Bayesian statistics as well as utilise modern surrogate modelling techniques and hybrid “AI”-statistical methods. Several examples will be presented to illustrate these points.

Join us for an in-person event, the "2024 Data Science and Statistics Lecture" with light refreshments to follow

Host or Publisher School of Mathematics and Applied Statistics