Event Details

Data Dialogues is an informal, discussion-driven event where members of the DSAIL and the broader university community share insights into their work, spark interdisciplinary conversations and explore potential collaborations. The focus is on interactive engagement rather than formal presentations—so no slides (or just a few, if needed)! Instead, the idea is to introduce your work in an accessible way, followed by an open discussion and Q&A with attendees. Get fresh perspectives and think about

Abstract: Truth and measurement are separated from each other in our universe; as a result, there exists a relation between them. We find that the truth is separated from its corresponding measurements in quantifiable ways, and this property of the relation between truth and measurement is a fundamental one. It is independent of the methodology we use to make or analyze the measurement. This foundational property manifests as a regression-to-the-mean effect in statistics, and it will certainly manifest in machine learning methods which are part of the family of non-parametric non-linear regression analysis. The popular understanding of this effect is that extreme measurements are more likely to be followed by measurements closer to the mean. We show that, at its core, this effect implies that the truth corresponding to a measurement is likely to be closer to the more probable value. This property leads to a stunning result in space physics: extreme geomagnetic storms may have twice the impact on Earth than previously thought, overthrowing 10 existing theories of how solar wind affects the Earth. The development of the fundamental result was only possible because of a different way of examining the world: the world is a complex network of relations, rather than a collection of objects. This view (the principle of internal relations) has applications in the study of other complex systems, such as social systems, and may be of interest to a broader audience.