Overview

This volume honors Alexander Gammerman on the occasion of his 80th birthday, Prof. Gammerman is one of the leading figures in the area of AI uncertainty quantification, most notably he coinvented the Conformal Prediction algorithm, widely used by researchers, industry practitioners, and government policymakers. He began his academic career as a researcher at the Agrophysical Research Institute in St. Petersburg, followed by a lecturer position at Heriot-Watt University in Edinburgh. He joined Royal Holloway, University of London in 1993, where he served as head of the Computer Science department for 10 years and founded the Centre for Reliable Machine Learning. Prof. Gammerman’s career exemplifies the transformative impact of interdisciplinary research, he has written over 250 research papers, with nearly 12,000 citations, and among his 9 books is the highly cited Algorithmic Learning in a Random World. He founded the Kolmogorov Lecture series in 2003 and the COPA conference in 2012, and he has chaired many international events on Machine Learning and Bayesian methods. He has also influenced future generations through his university teaching and his mentorship, he was the lead supervisor for over 30 PhD students, many of whom are now also at the forefront of AI research and applications. From pioneering mathematical models of plant photoreceptors to advancing the formal treatment of uncertainty in artificial intelligence, Alexander Gammerman’s work is a rare confluence of analytical precision, conceptual depth, and visionary application. The contributions in this volume recognize the breadth and depth of his intellectual influence and his long-lasting impact as a researcher, educator, and mentor.

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