The chart shows the distribution of publications by all Research.com ranked scientists in the field of Computer Science in 2026. The highlighted bar marks where Peter van Beek sits on this spectrum.
This scientist: 129 publications — 18th percentile
18% of scientists in this discipline score the same or lower.
The last bar groups every scientist with 991 publications or more.
The chart shows the D-index (discipline H-index) distribution of Computer Science scientists ranked by Research.com in 2026. The highlighted bar marks where Peter van Beek sits on this spectrum.
This scientist: 39 D-Index — 33rd percentile
33% of scientists in this discipline score the same or lower.
The last bar groups every scientist with 131 D-Index or more.
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:
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.
Francesca Rossi;Peter van Beek;Toby Walsh
Marc Vilain;Henry Kautz;Peter van Beek
Peter van Beek
Peter van Beek
Francesca Rossi;Peter van Beek;Toby Walsh
Grzegorz Kondrak;Peter van Beek
Peter van Beek;Peter van Beek;Robin Cohen
Peter van Beek;Xinguang Chen
Fahiem Bacchus;Peter van Beek
Peter van Beek
Peter van Beek;Dennis W. Manchak
Peter van Beek
Peter Van Beek
Alejandro Lopez-Ortiz;Claude-Guy Quimper;John Tromp;Peter Van Beek
Peter van Beek;Rina Dechter
P. van Beek;J.R. Smith;T. Ebrahimi;T. Suzuki
Rina Dechter;Peter van Beek
Xinguang Chen;Peter van Beek
Peter van Beek
Fahiem Bacchus;Xinguang Chen;Peter van Beek;Toby Walsh
P. van Beek;A.M. Tekalp;N. Zhuang;I. Celasun
A.M. Tekalp;P. Van Beek;C. Toklu;B. Gunsel
Fahiem Bacchus;Peter van Beek
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