World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
39
Citations
7215
World Ranking
9683
National Ranking
383

Overview

Peter van Beek is affiliated with the University of Waterloo in Canada and contributes to fields spanning Computer Science and Engineering. Their research focuses on areas including Artificial Intelligence, Computer Vision and Pattern Recognition, Electrical and Electronic Engineering, Industrial and Manufacturing Engineering, and Management Science and Operations Research.

Their work covers several main topics of study such as Bayesian Modeling and Causal Inference, CCD and CMOS Imaging Sensors, Advanced Neural Network Applications, Industrial Vision Systems and Defect Detection, Data Quality and Management, Machine Learning and Data Classification, and Advanced Image and Video Retrieval Techniques.

Van Beek has published research in various venues, notably Electronic Imaging and arXiv (Cornell University), with a balanced output in both journals and preprint archives. Recent papers include:

  • Boosting computer vision performance by enhancing camera ISP, 2021, Electronic Imaging
  • A Score-and-Search Approach to Learning Bayesian Networks with Noisy-OR Relations, 2020, arXiv (Cornell University)
  • Evaluation of semi-frozen semi-fixed neural network for efficient computer vision inference, 2021, Electronic Imaging
  • Learning All Credible Bayesian Network Structures for Model Averaging, 2020, arXiv (Cornell University)
  • Scalable Bayesian Network Structure Learning with Splines, 2021, arXiv (Cornell University)

Frequent coauthors collaborating with van Beek include Chyuan-Tyng Wu, Charupriya Sharma, Thomas R. Gardos, Zhenyu A. Liao, and James Cussens.

The research interests span across diverse topics related to computational models, camera imaging systems, and neural networks, often intersecting machine learning techniques with industrial applications such as vision systems and defect detection. The emphasis on Bayesian networks and causal inference signifies a strong focus on probabilistic models and data-driven decision-making frameworks within their body of work.

Best Publications

  • Handbook of Constraint Programming

    Francesca Rossi;Peter van Beek;Toby Walsh

  • Constraint propagation algorithms for temporal reasoning: a revised report

    Marc Vilain;Henry Kautz;Peter van Beek

  • Reasoning about qualitative temporal information

    Peter van Beek

  • Reasoning about qualitative temporal information

    Peter van Beek

  • Handbook of Constraint Programming (Foundations of Artificial Intelligence)

    Francesca Rossi;Peter van Beek;Toby Walsh

  • A theoretical evaluation of selected backtracking algorithms

    Grzegorz Kondrak;Peter van Beek

  • Exact and approximate reasoning about temporal relations

    Peter van Beek;Peter van Beek;Robin Cohen

  • CPlan: a constraint programming approach to planning

    Peter van Beek;Xinguang Chen

  • On the Conversion between Non-Binary and Binary Constraint Satisfaction Problems

    Fahiem Bacchus;Peter van Beek

  • Principles and Practice of Constraint Programming - CP 2005

    Peter van Beek

  • The design and experimental analysis of algorithms for temporal reasoning

    Peter van Beek;Dennis W. Manchak

  • Backtracking Search Algorithms

    Peter van Beek

  • Approximation algorithms for temporal reasoning

    Peter Van Beek

  • A fast and simple algorithm for bounds consistency of the all different constraint

    Alejandro Lopez-Ortiz;Claude-Guy Quimper;John Tromp;Peter Van Beek

  • On the minimality and global consistency of row-convex constraint networks

    Peter van Beek;Rina Dechter

  • Metadata-driven multimedia access

    P. van Beek;J.R. Smith;T. Ebrahimi;T. Suzuki

  • Local and global relational consistency

    Rina Dechter;Peter van Beek

  • Conflict-directed backjumping revisited

    Xinguang Chen;Peter van Beek

  • On the minimality and decomposability of constraint networks

    Peter van Beek

  • Binary vs. non-binary constraints

    Fahiem Bacchus;Xinguang Chen;Peter van Beek;Toby Walsh

  • Hierarchical 2-D mesh representation, tracking, and compression for object-based video

    P. van Beek;A.M. Tekalp;N. Zhuang;I. Celasun

  • Two-dimensional mesh-based visual-object representation for interactive synthetic/natural digital video

    A.M. Tekalp;P. Van Beek;C. Toklu;B. Gunsel

  • On the conversion between non-binary constraint satisfaction problems

    Fahiem Bacchus;Peter van Beek

Frequent Co-Authors

Robin Cohen
Robin Cohen University of Waterloo
Toby Walsh
Toby Walsh University of New South Wales
Rina Dechter
Rina Dechter University of California, Irvine
Pascal Poupart
Pascal Poupart University of Waterloo
Fahiem Bacchus
Fahiem Bacchus University of Toronto
Henry Kautz
Henry Kautz University of Virginia
Otman A. Basir
Otman A. Basir University of Waterloo
Krzysztof Czarnecki
Krzysztof Czarnecki University of Waterloo
Francesca Rossi
Francesca Rossi IBM (United States)

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