Trevor Campbell

878 total citations · 1 hit paper
24 papers, 364 citations indexed

About

Trevor Campbell is a scholar working on Artificial Intelligence, Statistics and Probability and Computer Vision and Pattern Recognition. According to data from OpenAlex, Trevor Campbell has authored 24 papers receiving a total of 364 indexed citations (citations by other indexed papers that have themselves been cited), including 16 papers in Artificial Intelligence, 10 papers in Statistics and Probability and 2 papers in Computer Vision and Pattern Recognition. Recurrent topics in Trevor Campbell's work include Gaussian Processes and Bayesian Inference (10 papers), Bayesian Methods and Mixture Models (7 papers) and Statistical Methods and Inference (5 papers). Trevor Campbell is often cited by papers focused on Gaussian Processes and Bayesian Inference (10 papers), Bayesian Methods and Mixture Models (7 papers) and Statistical Methods and Inference (5 papers). Trevor Campbell collaborates with scholars based in Canada, United States and Luxembourg. Trevor Campbell's co-authors include Reza Vaziri, Anoush Poursartip, Sina Amini Niaki, Ehsan Haghighat, Jonathan P. How, Tamara Broderick, Jonathan H. Huggins, Brian Kulis, Miao Liu and Lawrence Carin and has published in prestigious journals such as Computer Methods in Applied Mechanics and Engineering, Composites Part A Applied Science and Manufacturing and Journal of Machine Learning Research.

In The Last Decade

Trevor Campbell

21 papers receiving 333 citations

Hit Papers

Physics-informed neural network for modelling the thermoc... 2021 2026 2022 2024 2021 50 100 150 200
What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

  1. Physics-informed neural network for modelling the thermochemical curing process of composite-tool systems during manufacture
    2021 201 citations Trevor Campbell, Anoush Poursartip et al. profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Trevor Campbell Canada 8 118 98 62 55 43 24 364
Kevin McFall United States 9 143 1.2× 80 0.8× 29 0.5× 28 0.5× 34 0.8× 30 349
Zhicheng Wang China 8 134 1.1× 47 0.5× 62 1.0× 26 0.5× 43 1.0× 18 312
Alberto Badías Spain 11 177 1.5× 67 0.7× 36 0.6× 29 0.5× 21 0.5× 19 316
Levi D. McClenny United States 6 195 1.7× 58 0.6× 46 0.7× 39 0.7× 34 0.8× 7 342
Mingxu Yi China 15 82 0.7× 40 0.4× 20 0.3× 74 1.3× 40 0.9× 53 651
Xianqi Chen China 11 42 0.4× 89 0.9× 49 0.8× 28 0.5× 32 0.7× 22 393
Jingbo Guo China 11 71 0.6× 34 0.3× 188 3.0× 92 1.7× 103 2.4× 46 421
Badr Abou El Majd Morocco 10 90 0.8× 32 0.3× 32 0.5× 14 0.3× 60 1.4× 42 344
Huatao Chen China 11 85 0.7× 34 0.3× 39 0.6× 40 0.7× 13 0.3× 34 331
Pawan Goyal Germany 13 302 2.6× 41 0.4× 58 0.9× 31 0.6× 109 2.5× 47 634

Countries citing papers authored by Trevor Campbell

Since Specialization
Citations

This map shows the geographic impact of Trevor Campbell's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Trevor Campbell with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Trevor Campbell more than expected).

Fields of papers citing papers by Trevor Campbell

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Trevor Campbell. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Trevor Campbell. The network helps show where Trevor Campbell may publish in the future.

Co-authorship network of co-authors of Trevor Campbell

This figure shows the co-authorship network connecting the top 25 collaborators of Trevor Campbell. A scholar is included among the top collaborators of Trevor Campbell based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Trevor Campbell. Trevor Campbell is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Tiede, Paul, et al.. (2025). Pigeons.jl: Distributed sampling from intractable distributions. 7(69). 139–139. 1 indexed citations
2.
Bhattacharjee, A., et al.. (2025). A Bayesian framework for quantifying uncertainty in the thermal history of curing composite structures. Composites Part A Applied Science and Manufacturing. 193. 108843–108843. 2 indexed citations
3.
Timbers, Tiffany, et al.. (2024). Data Science. 2 indexed citations
4.
Winter, Steven L., Trevor Campbell, Lizhen Lin, Sanvesh Srivastava, & David B. Dunson. (2024). Emerging Directions in Bayesian Computation. Statistical Science. 39(1). 2 indexed citations
5.
Bouchard‐Côté, Alexandre, et al.. (2022). Pseudo-Marginal Inference for CTMCs on Infinite Spaces via Monotonic Likelihood Approximations. Journal of Computational and Graphical Statistics. 32(2). 513–527.
6.
Timbers, Tiffany, Trevor Campbell, & Melissa Lee. (2022). Data Science. 7 indexed citations
7.
Li, Xinglong & Trevor Campbell. (2021). Truncated simulation and inference in edge-exchangeable networks. Electronic Journal of Statistics. 15(2). 1 indexed citations
8.
Cai, Diana, Trevor Campbell, & Tamara Broderick. (2020). Finite mixture models are typically inconsistent for the number of components. arXiv (Cornell University). 1 indexed citations
9.
Huggins, Jonathan H., et al.. (2020). Validated Variational Inference via Practical Posterior Error Bounds. OpenBU (Boston University). 1792–1802. 4 indexed citations
10.
Campbell, Trevor & Tamara Broderick. (2019). Automated Scalable Bayesian Inference via Hilbert Coresets. Journal of Machine Learning Research. 20(15). 1–38. 22 indexed citations
11.
Huggins, Jonathan H., et al.. (2019). Practical Posterior Error Bounds from Variational Objectives. arXiv (Cornell University). 1 indexed citations
12.
Huggins, Jonathan H., et al.. (2019). Scalable Gaussian Process Inference with Finite-data Mean and Variance Guarantees. DSpace@MIT (Massachusetts Institute of Technology). 796–805. 1 indexed citations
13.
Campbell, Trevor, et al.. (2018). Data-dependent compression of random features for large-scale kernel approximation. arXiv (Cornell University). 1822–1831. 3 indexed citations
14.
Huggins, Jonathan H., Trevor Campbell, & Tamara Broderick. (2016). Coresets for Scalable Bayesian Logistic Regression. DSpace@MIT (Massachusetts Institute of Technology). 29. 4080–4088. 11 indexed citations
15.
Campbell, Trevor & Jonathan P. How. (2015). Bayesian nonparametric set construction for robust optimization. 4216–4221. 33 indexed citations
16.
Campbell, Trevor & Jonathan P. How. (2014). Decentralized Variational Bayesian Inference.. arXiv (Cornell University). 2 indexed citations
17.
Campbell, Trevor, Miao Liu, Brian Kulis, Jonathan P. How, & Lawrence Carin. (2013). Dynamic Clustering via Asymptotics of the Dependent Dirichlet Process Mixture. arXiv (Cornell University). 26. 449–457. 18 indexed citations
18.
Campbell, Trevor, Luke B. Johnson, & Jonathan P. How. (2013). Multiagent allocation of Markov decision process tasks. 2356–2361. 7 indexed citations
19.
Campbell, Trevor, Sameera Ponda, Girish Chowdhary, & Jonathan P. How. (2012). Planning under Uncertainty using Nonparametric Bayesian Models. DSpace@MIT (Massachusetts Institute of Technology). 2 indexed citations
20.
Campbell, Trevor, Sameera Ponda, Girish Chowdhary, & Jonathan P. How. (2012). Planning under Uncertainty using Bayesian Nonparametric Models. AIAA Guidance, Navigation, and Control Conference. 2 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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