Sir David John Cameron MacKay (1967-2016) was a British physicist and professor at the University of Cambridge whose work bridged information theory, machine learning, and energy policy. He earned a first-class degree in natural sciences at Cambridge and a PhD in computation and neural systems at Caltech, then returned to Cambridge, becoming Professor of Natural Philosophy and later Regius Professor of Engineering. He was elected a Fellow of the Royal Society in 2009.
MacKay’s most influential contribution to AI is his 2003 textbook “Information Theory, Inference, and Learning Algorithms,” which he made freely available online. The book unified Claude Shannon’s information theory with Bayesian inference and machine learning in a single, accessible treatment, and it became a standard reference for students learning how coding, compression, and probabilistic learning are facets of the same underlying ideas. He also did important work on error-correcting codes, helping revive the low-density parity-check codes now used in modern communications.
Outside AI, MacKay served as Chief Scientific Advisor to the UK Department of Energy and Climate Change and wrote the widely praised “Sustainable Energy - Without the Hot Air,” applying the same clear, quantitative style to energy choices. For a general reader, MacKay is a model of how a single rigorous framework can connect the mathematics of communication with the mathematics of learning.