Mark Crowley

1.7k total citations
33 papers, 392 citations indexed

About

Mark Crowley is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Management Science and Operations Research. According to data from OpenAlex, Mark Crowley has authored 33 papers receiving a total of 392 indexed citations (citations by other indexed papers that have themselves been cited), including 15 papers in Artificial Intelligence, 6 papers in Computer Vision and Pattern Recognition and 6 papers in Management Science and Operations Research. Recurrent topics in Mark Crowley's work include Reinforcement Learning in Robotics (7 papers), Face and Expression Recognition (4 papers) and Autonomous Vehicle Technology and Safety (4 papers). Mark Crowley is often cited by papers focused on Reinforcement Learning in Robotics (7 papers), Face and Expression Recognition (4 papers) and Autonomous Vehicle Technology and Safety (4 papers). Mark Crowley collaborates with scholars based in Canada, United States and Germany. Mark Crowley's co-authors include Benyamin Ghojogh, Fakhri Karray, Thomas G. Dietterich, Ali Ghodsi, Fakhri Karray, Sebastian Fischmeister, David E. Calkin, Claire A. Montgomery, Sean McGregor and Rachel M. Houtman and has published in prestigious journals such as SHILAP Revista de lepidopterología, IEEE Transactions on Computers and Journal of Machine Learning Research.

In The Last Decade

Mark Crowley

32 papers receiving 371 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Mark Crowley Canada 11 103 89 53 41 39 33 392
Zijiang Zhu China 9 59 0.6× 53 0.6× 36 0.7× 19 0.5× 25 0.6× 30 383
Bo Yan China 12 175 1.7× 80 0.9× 76 1.4× 101 2.5× 34 0.9× 28 809
Maria Vasardani Australia 14 157 1.5× 31 0.3× 59 1.1× 23 0.6× 32 0.8× 50 592
Haifeng Zhao China 15 142 1.4× 26 0.3× 100 1.9× 36 0.9× 14 0.4× 56 489
Hoe-Kyung Jung South Korea 9 63 0.6× 41 0.5× 77 1.5× 24 0.6× 48 1.2× 80 322
Kai H. Chang United States 13 111 1.1× 63 0.7× 55 1.0× 19 0.5× 22 0.6× 56 486
Ilias Gialampoukidis Greece 11 101 1.0× 102 1.1× 67 1.3× 20 0.5× 36 0.9× 57 458
Kamalika Das United States 11 218 2.1× 35 0.4× 62 1.2× 22 0.5× 21 0.5× 33 454
Misbah Ahmad South Korea 14 110 1.1× 34 0.4× 215 4.1× 16 0.4× 48 1.2× 31 426
Doris Esenarro Peru 10 109 1.1× 33 0.4× 67 1.3× 15 0.4× 26 0.7× 87 485

Countries citing papers authored by Mark Crowley

Since Specialization
Citations

This map shows the geographic impact of Mark Crowley'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 Mark Crowley with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Mark Crowley more than expected).

Fields of papers citing papers by Mark Crowley

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Mark Crowley. 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 Mark Crowley. The network helps show where Mark Crowley may publish in the future.

Co-authorship network of co-authors of Mark Crowley

This figure shows the co-authorship network connecting the top 25 collaborators of Mark Crowley. A scholar is included among the top collaborators of Mark Crowley 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 Mark Crowley. Mark Crowley 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.
Li, Xinkai, et al.. (2024). ChemGymRL: A customizable interactive framework for reinforcement learning for digital chemistry. Digital Discovery. 3(4). 742–758. 3 indexed citations
2.
Ghojogh, Benyamin, Mark Crowley, Fakhri Karray, & Ali Ghodsi. (2023). Elements of Dimensionality Reduction and Manifold Learning. 44 indexed citations
3.
Saab, Rami, Alireza Sadeghian, Gordon D. Rubenfeld, et al.. (2023). Machine-learning assisted swallowing assessment: a deep learning-based quality improvement tool to screen for post-stroke dysphagia. Frontiers in Neuroscience. 17. 1302132–1302132. 5 indexed citations
4.
Bellinger, Colin, et al.. (2022). Balancing Information with Observation Costs in Deep Reinforcement Learning. 1 indexed citations
5.
Moshiri, Behzad, et al.. (2022). Real-Time Pedestrian Detection Using Enhanced Representations from Light-Weight YOLO Network. 2022 8th International Conference on Control, Decision and Information Technologies (CoDIT). 1524–1529. 2 indexed citations
6.
Crowley, Mark, et al.. (2022). Investigation of independent reinforcement learning algorithms in multi-agent environments. Frontiers in Artificial Intelligence. 5. 805823–805823. 9 indexed citations
7.
8.
Ghojogh, Benyamin, Fakhri Karray, & Mark Crowley. (2021). Quantile–Quantile Embedding for distribution transformation and manifold embedding with ability to choose the embedding distribution. SHILAP Revista de lepidopterología. 6. 100088–100088. 1 indexed citations
9.
Ghojogh, Benyamin, Ali Ghodsi, Fakhri Karray, & Mark Crowley. (2021). Generative locally linear embedding: A module for manifold unfolding and visualization. Software Impacts. 9. 100105–100105. 2 indexed citations
10.
Ghojogh, Benyamin, et al.. (2021). Acceleration of Large Margin Metric Learning for Nearest Neighbor Classification Using Triplet Mining and Stratified Sampling. arXiv (Cornell University). 3 indexed citations
11.
12.
Ghafurian, Moojan, et al.. (2020). Using Emotions to Complement Multi-Modal Human-Robot Interaction in Urban Search and Rescue Scenarios. 575–584. 3 indexed citations
13.
Ghojogh, Benyamin, et al.. (2020). Fisher Discriminant Triplet and Contrastive Losses for Training Siamese Networks. arXiv (Cornell University). 1–7. 16 indexed citations
14.
Crowley, Mark, et al.. (2019). Training Cooperative Agents for Multi-Agent Reinforcement Learning. Adaptive Agents and Multi-Agents Systems. 1826–1828. 1 indexed citations
15.
Ghojogh, Benyamin, et al.. (2019). Feature Selection and Feature Extraction in Pattern Analysis: A\n Literature Review. arXiv (Cornell University). 51 indexed citations
16.
Ghojogh, Benyamin, et al.. (2019). Artificial Counselor System for Stock Investment. Proceedings of the AAAI Conference on Artificial Intelligence. 33(1). 9558–9564. 10 indexed citations
17.
Eaton, Eric, Sven Koenig, Cláudia Schulz, et al.. (2018). Blue sky ideas in artificial intelligence education from the EAAI 2017 new and future AI educator program. arXiv (Cornell University). 3(4). 23–31. 33 indexed citations
18.
Dietterich, Thomas G., et al.. (2015). PAC optimal MDP planning with application to invasive species management. Journal of Machine Learning Research. 16(1). 3877–3903. 10 indexed citations
19.
Houtman, Rachel M., Claire A. Montgomery, David E. Calkin, et al.. (2013). Allowing a wildfire to burn: estimating the effect on future fire suppression costs. International Journal of Wildland Fire. 22(7). 871–882. 64 indexed citations
20.
Crowley, Mark, John D. Nelson, & David Poole. (2012). Seeing the Forest Despite the Trees: Large Scale Spatial-Temporal Decision Making. arXiv (Cornell University). 126–134.

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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