World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
61
Citations
50321
World Ranking
2974
National Ranking
179

Overview

David J. C. MacKay is affiliated with the University of Cambridge in the United Kingdom. Their research spans several interdisciplinary fields, including global and planetary change, strategy and management, social psychology, general health professions, and organizational behavior and human resource management.

The scientific work focuses on a range of interconnected topics such as innovation and knowledge management, competitive and knowledge intelligence, organizational learning and leadership, forest management and policy, sustainability and climate change governance, conservation, biodiversity and resource management, and hybrid renewable energy systems.

Recent publications illustrate this diverse focus. These include "Overcoming multi-stakeholder fragmented narratives in land use, woodland and forestry policy: The role scenario planning and 'dissociative jolts'" (2021, Technological Forecasting and Social Change), "Unlocking dynamic capabilities in the Scotch whisky industry, 1945-present" (2022, Business History), "Hydrogen microgrids to facilitate the clean energy transition in remote, northern communities" (2025, Applied Energy), "Energy Cost Metric. Energy Design Guide for the Civil Engineering Building in West Cambridge. Part 1: Early stage design decisions" (2020, Apollo (University of Cambridge)), and "The relationship of intergenerational poverty and exclusionary school discipline" (2022, Psychology in the Schools).

Frequent coauthors working alongside David J. C. MacKay include George Burt, Robert Chia, Niall MacKenzie, Andrew Perchard, and Kepa Mendibil.

Publication venues reflect the multidisciplinary nature of the research, featuring outlets such as Business History, Technological Forecasting and Social Change, Applied Energy, Apollo (University of Cambridge), and Psychology in the Schools.

Their contributions to academic literature also include a book titled Strategy-In-Practices, published by Cambridge University Press in 2023.

Best Publications

  • Good error-correcting codes based on very sparse matrices

    D.J.C. MacKay

  • Bayesian interpolation

    David J. C. MacKay

  • Near Shannon limit performance of low density parity check codes

    David J. C. MacKay;Radford M. Neal

  • A practical Bayesian framework for backpropagation networks

    David J. C. MacKay

  • Low-density parity check codes over GF(q)

    M.C. Davey;D. MacKay

  • Near Shannon limit performance of low density parity check codes

    Unknown

  • Low density parity check codes over GF(q)

    M.C. Davey;D.J.C. MacKay

  • Information-based objective functions for active data selection

    David J. C. MacKay

  • Turbo decoding as an instance of Pearl's "belief propagation" algorithm

    R.J. McEliece;D.J.C. MacKay;Jung-Fu Cheng

  • Probable networks and plausible predictions - a review of practical Bayesian methods for supervised neural networks

    David J C Mackay

  • Introduction to Gaussian processes

    D. J. C. Mackay

  • The evidence framework applied to classification networks

    David J. C. MacKay

  • Bayesian methods for adaptive models

    David John Cameron Mackay

  • Fountain codes

    Unknown

  • Bayesian Online Changepoint Detection

    Ryan Prescott Adams;David J. C. MacKay

  • Introduction to Monte Carlo methods

    D. J. C. MacKay

  • Good Codes Based on Very Sparse Matrices

    David J. C. MacKay

  • Information Theory, Inference and Learning Algorithms

    David J. C. MacKay

  • The role of constraints in Hebbian learning

    Kenneth D. Miller;David J. C. MacKay

  • Dasher—a data entry interface using continuous gestures and language models

    David J. Ward;Alan F. Blackwell;David J. C. MacKay

  • Evaluation of Gallager Codes for Short Block Length and High Rate Applications

    David J. C. MacKay;Matthew C. Davey

  • A Revolution: Belief Propagation in Graphs with Cycles

    Brendan J. Frey;David J. C. MacKay

  • MCMC for doubly-intractable distributions

    Iain Murray;Zoubin Ghahramani;David J. C. MacKay

  • Advances in Neural Information Processing Systems 18

    Iain Murray;David J. C. MacKay;Zoubin Ghahramani;John Skilling

Frequent Co-Authors

Iain Murray
Iain Murray University of Edinburgh
Ryan P. Adams
Ryan P. Adams Princeton University
H. K. D. H. Bhadeshia
H. K. D. H. Bhadeshia Queen Mary University of London
Zoubin Ghahramani
Zoubin Ghahramani University of Cambridge
Coryn A. L. Bailer-Jones
Coryn A. L. Bailer-Jones Max Planck Society
Per Ola Kristensson
Per Ola Kristensson University of Cambridge
Alan F. Blackwell
Alan F. Blackwell University of Cambridge
Kenneth D. Miller
Kenneth D. Miller Columbia University
Oliver Stegle
Oliver Stegle German Cancer Research Center
Philip J. Withers
Philip J. Withers University of Manchester

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