World's Best Scientists 2026 revealed!
William G. Macready

William G. Macready

D-Index & Metrics

Computer Science

D-Index
36
Citations
22860
World Ranking
10954
National Ranking
436

Overview

William G. Macready is affiliated with D-Wave Systems in Canada, where their research primarily focuses on computational and engineering challenges related to quantum computing and robotics.

Their recent publications cover a range of topics and venues, including:

  • Solving SAT (and MaxSAT) with a quantum annealer: Foundations, encodings, and preliminary results, 2020, Information and Computation
  • Neural-Guided Runtime Prediction of Planners for Improved Motion and Task Planning with Graph Neural Networks, 2022, 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
  • Neural-Guided RuntimePrediction of Planners for Improved Motion and Task Planning with Graph Neural Networks, 2022, arXiv (Cornell University)

The scientist frequently collaborates with other researchers, including:

  • Simon Odense (2 publications)
  • Zhengbing Bian (1 publication)
  • Fabián A. Chudak (1 publication)
  • Aidan Roy (1 publication)
  • Roberto Sebastiani (1 publication)

Research venues where their work appears regularly are:

  • Information and Computation
  • 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
  • arXiv (Cornell University)

William G. Macready's fields of study are rooted mainly in computer science and engineering, with specific attention to the following subfields:

  • Computer Vision and Pattern Recognition
  • Artificial Intelligence
  • Control and Systems Engineering
  • Information Systems

Their research spans multiple main topics including:

  • Robotic Path Planning Algorithms
  • Robot Manipulation and Learning
  • Multimodal Machine Learning Applications
  • Quantum Computing Algorithms and Architecture
  • Quantum Information and Cryptography
  • Cloud Computing and Resource Management

This profile highlights Macready's involvement in advancing methods for motion and task planning through neural-guided runtime prediction, as well as foundational work on quantum annealing applied to satisfiability problems. The integration of machine learning techniques with robotics and quantum computing underlines the interdisciplinary nature of their research portfolio.

Best Publications

  • No free lunch theorems for optimization

    D.H. Wolpert;W.G. Macready

  • No Free Lunch Theorems for Search

    David H. Wolpert;William G. Macready

  • Optimal search on a technology landscape

    Stuart Kauffman;José Lobo;William G. Macready

  • A practical heuristic for finding graph minors

    Jun Cai;William G. Macready;Aidan Roy

  • Coevolutionary free lunches

    D.H. Wolpert;W.G. Macready

  • A Robust Learning Approach to Domain Adaptive Object Detection

    Mehran Khodabandeh;Arash Vahdat;Mani Ranjbar;William Macready

  • An Efficient Method To Estimate Bagging‘s Generalization Error

    David H. Wolpert;William G. Macready

  • Bandit problems and the exploration/exploitation tradeoff

    W.G. Macready;D.H. Wolpert

  • Discrete optimization using quantum annealing on sparse Ising models

    Zhengbing Bian;Fabian Chudak;Robert Israel;Brad Lackey

  • Experimental determination of Ramsey numbers.

    Zhengbing Bian;Fabian Chudak;William G. Macready;Lane Clark

  • Image recognition with an adiabatic quantum computer I. Mapping to quadratic unconstrained binary optimization

    Hartmut Neven;Geordie Rose;William G. Macready

  • CATARACTS: Challenge on automatic tool annotation for cataRACT surgery

    Hassan Al Hajj;Mathieu Lamard;Pierre-Henri Conze;Soumali Roychowdhury

  • Mapping Constrained Optimization Problems to Quantum Annealing with Application to Fault Diagnosis

    Zhengbing Bian;Fabian Chudak;Robert Brian Israel;Brad Lackey

  • Adaptive and reliable system and method for operations management

    Isaac Saias;Vince Darley;Stuart Kauffman;Fred Federspiel

  • Training a Binary Classifier with the Quantum Adiabatic Algorithm

    Hartmut Neven;Vasil S. Denchev;Geordie Rose;William G. Macready

  • Semi-Supervised Semantic Image Segmentation With Self-Correcting Networks

    Mostafa S. Ibrahim;Arash Vahdat;Mani Ranjbar;William G. Macready

  • Graph embedding techniques

    Michael Coury;William G. Macready;David Grant

  • Processing relational database problems using analog processors

    William G. Macready;Michael D. Coury;Ivan King Yu Sham

  • Parameter space exploration with Gaussian process trees

    Robert B. Gramacy;Herbert K. H. Lee;William G. Macready

  • What Makes an Optimization Problem Hard

    William G. Macready;David H. Wolpert

  • Training a Large Scale Classifier with the Quantum Adiabatic Algorithm

    Hartmut Neven;Vasil S. Denchev;Geordie Rose;William G. Macready

Frequent Co-Authors

David H. Wolpert
David H. Wolpert Santa Fe Institute
Stuart A. Kauffman
Stuart A. Kauffman University of Vermont
Hartmut Neven
Hartmut Neven Google (United States)
Alán Aspuru-Guzik
Alán Aspuru-Guzik University of Toronto
Roberto Sebastiani
Roberto Sebastiani University of Trento
Danail Stoyanov
Danail Stoyanov University College London
Pheng-Ann Heng
Pheng-Ann Heng Chinese University of Hong Kong
Eleanor Rieffel
Eleanor Rieffel Ames Research Center
Robert B. Gramacy
Robert B. Gramacy Virginia Tech
Aurélio Campilho
Aurélio Campilho University of Porto

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