Mark Coates

7.0k total citations
185 papers, 4.8k citations indexed

About

Mark Coates is a scholar working on Artificial Intelligence, Computer Networks and Communications and Electrical and Electronic Engineering. According to data from OpenAlex, Mark Coates has authored 185 papers receiving a total of 4.8k indexed citations (citations by other indexed papers that have themselves been cited), including 98 papers in Artificial Intelligence, 78 papers in Computer Networks and Communications and 35 papers in Electrical and Electronic Engineering. Recurrent topics in Mark Coates's work include Target Tracking and Data Fusion in Sensor Networks (49 papers), Distributed Sensor Networks and Detection Algorithms (32 papers) and Microwave Imaging and Scattering Analysis (30 papers). Mark Coates is often cited by papers focused on Target Tracking and Data Fusion in Sensor Networks (49 papers), Distributed Sensor Networks and Detection Algorithms (32 papers) and Microwave Imaging and Scattering Analysis (30 papers). Mark Coates collaborates with scholars based in Canada, United States and China. Mark Coates's co-authors include Michael Rabbat, Boris N. Oreshkin, Robert Nowak, Tuncer C. Aysal, Milica Popović, Emily Porter, Yunpeng Li, Yingxue Zhang, Rui Castro and Yolanda Tsang and has published in prestigious journals such as IEEE Transactions on Signal Processing, IEEE Journal on Selected Areas in Communications and IEEE Transactions on Biomedical Engineering.

In The Last Decade

Mark Coates

180 papers receiving 4.6k 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 Coates Canada 40 2.4k 2.0k 990 674 534 185 4.8k
Li Yu China 60 5.9k 2.4× 2.2k 1.1× 1.9k 1.9× 257 0.4× 164 0.3× 464 11.6k
Minyue Fu Australia 58 4.8k 2.0× 1.7k 0.9× 2.0k 2.0× 264 0.4× 78 0.1× 507 12.5k
Guangdeng Zong China 66 6.4k 2.6× 1.3k 0.7× 1.3k 1.3× 273 0.4× 157 0.3× 377 14.3k
Hans‐Andrea Loeliger Switzerland 22 4.3k 1.8× 2.3k 1.2× 4.3k 4.3× 336 0.5× 134 0.3× 126 7.4k
Hongli Dong China 53 5.4k 2.2× 2.5k 1.2× 1.2k 1.3× 155 0.2× 100 0.2× 289 9.8k
Domenico Ciuonzo Italy 40 2.9k 1.2× 2.5k 1.3× 1.3k 1.3× 232 0.3× 336 0.6× 130 5.1k
Sergio Barbarossa Italy 47 6.0k 2.5× 1.6k 0.8× 5.9k 6.0× 507 0.8× 559 1.0× 274 10.4k
Chaouki T. Abdallah United States 31 1.3k 0.5× 625 0.3× 551 0.6× 574 0.9× 137 0.3× 270 6.7k
Michael Rabbat Canada 36 2.9k 1.2× 1.6k 0.8× 1.1k 1.2× 257 0.4× 57 0.1× 121 5.3k
Ananthram Swami United States 34 2.0k 0.8× 1.6k 0.8× 2.0k 2.1× 125 0.2× 126 0.2× 202 4.8k

Countries citing papers authored by Mark Coates

Since Specialization
Citations

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

Fields of papers citing papers by Mark Coates

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Mark Coates

This figure shows the co-authorship network connecting the top 25 collaborators of Mark Coates. A scholar is included among the top collaborators of Mark Coates 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 Coates. Mark Coates 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.
Chételat, Didier, Wenyi Xiao, Hui‐Ling Zhen, et al.. (2024). GraSS: Combining Graph Neural Networks with Expert Knowledge for SAT Solver Selection. 6301–6311.
2.
Coates, Mark, et al.. (2023). Diffusing Gaussian Mixtures for Generating Categorical Data. Proceedings of the AAAI Conference on Artificial Intelligence. 37(8). 9570–9578.
3.
Zhang, Yingxue, et al.. (2021). Detection and Defense of Topological Adversarial Attacks on Graphs. International Conference on Artificial Intelligence and Statistics. 2989–2997. 1 indexed citations
4.
Zhang, Yingxue, et al.. (2020). Non Parametric Graph Learning for Bayesian Graph Neural Networks. Uncertainty in Artificial Intelligence. 1318–1327. 1 indexed citations
5.
Li, Yunpeng, et al.. (2019). Invertible Particle-Flow-Based Sequential MCMC With Extension to Gaussian Mixture Noise Models. IEEE Transactions on Signal Processing. 67(9). 2499–2512. 14 indexed citations
6.
Coates, Mark, et al.. (2019). Clinical Study with a Time-Domain Microwave Breast Monitor: Analysis of the System Response and Patient Attributes. European Conference on Antennas and Propagation. 9 indexed citations
7.
Coates, Mark, et al.. (2019). Microwave Radar for Breast Screening: Initial Clinical Data with Suspicious-Lesion Patients. PubMed. 2019. 3191–3194. 1 indexed citations
8.
Mallick, Mahendra, et al.. (2015). Comparison of angle-only filtering algorithms in 3D using EKF, UKF, PF, PFF, and ensemble KF. International Conference on Information Fusion. 1649–1656. 34 indexed citations
9.
Li, Yunpeng, Emily Porter, & Mark Coates. (2015). Imaging-based classification algorithms on clinical trial data with injected tumour responses. European Conference on Antennas and Propagation. 1–5. 4 indexed citations
10.
Nannuru, Santosh & Mark Coates. (2013). Multi-Bernoulli filter for superpositional sensors. International Conference on Information Fusion. 1632–1637. 6 indexed citations
11.
Porter, Emily, et al.. (2011). Time-domain microwave breast cancer detection: Experiments with comprehensive glandular phantoms. Asia-Pacific Microwave Conference. 203–206. 7 indexed citations
12.
Chen, Xi, et al.. (2011). Sequential Monte Carlo for simultaneous passive device-free tracking and sensor localization using received signal strength measurements. Information Processing in Sensor Networks. 342–353. 84 indexed citations
13.
Porter, Emily, Adam Santorelli, Mark Coates, & Milica Popović. (2011). An experimental system for time-domain microwave breast imaging. European Conference on Antennas and Propagation. 2906–2910. 24 indexed citations
14.
Nannuru, Santosh, et al.. (2011). Multi-target tracking for measurement models with additive contributions. International Conference on Information Fusion. 1–8. 24 indexed citations
15.
Coates, Mark, et al.. (2011). Evaluation of the mono-static microwave radar algorithms for breast imaging. European Conference on Antennas and Propagation. 881–885. 6 indexed citations
16.
Üstebay, Deniz, Mark Coates, & Michael Rabbat. (2011). Distributed auxiliary particle filters using selective gossip. 3296–3299. 50 indexed citations
17.
Porter, Emily, et al.. (2010). Improved tissue phantoms for experimental validation of microwave breast cancer detection. European Conference on Antennas and Propagation. 1–5. 73 indexed citations
18.
Üstebay, Deniz, Boris N. Oreshkin, Mark Coates, & Michael Rabbat. (2009). Multi-hop Greedy Gossip with Eavesdropping. International Conference on Information Fusion. 140–145. 1 indexed citations
19.
Oreshkin, Boris N., et al.. (2009). Numerical breast models for commercial FDTD simulators. European Conference on Antennas and Propagation. 263–267. 3 indexed citations
20.
Ahmed, Tarem, Boris N. Oreshkin, & Mark Coates. (2007). Machine learning approaches to network anomaly detection. 11(11). 7–702. 95 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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