James H. Faghmous

3.0k total citations · 2 hit papers
19 papers, 1.8k citations indexed

About

James H. Faghmous is a scholar working on Oceanography, Artificial Intelligence and Global and Planetary Change. According to data from OpenAlex, James H. Faghmous has authored 19 papers receiving a total of 1.8k indexed citations (citations by other indexed papers that have themselves been cited), including 6 papers in Oceanography, 6 papers in Artificial Intelligence and 5 papers in Global and Planetary Change. Recurrent topics in James H. Faghmous's work include Oceanographic and Atmospheric Processes (6 papers), Climate variability and models (4 papers) and Data Analysis with R (3 papers). James H. Faghmous is often cited by papers focused on Oceanographic and Atmospheric Processes (6 papers), Climate variability and models (4 papers) and Data Analysis with R (3 papers). James H. Faghmous collaborates with scholars based in United States, Norway and United Kingdom. James H. Faghmous's co-authors include Vipin Kumar, Auroop R. Ganguly, Michael Steinbach, Arindam Banerjee, Anuj Karpatne, Gowtham Atluri, Shashi Shekhar, Sanjay Basu, Patrick Doupé and Ivy Frenger and has published in prestigious journals such as Movement Disorders, Computer and IEEE Transactions on Knowledge and Data Engineering.

In The Last Decade

James H. Faghmous

19 papers receiving 1.7k citations

Hit Papers

align trajectories

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.

Theory-Guided Data Science: A New Paradigm for Scientific Discovery from Data

2017 905 citations Anuj Karpatne, Gowtham Atluri et al. IEEE Transactions on Knowledge and Data Engineering profile →
A daily global mesoscale ocean eddy dataset from satellite altimetry

2015 273 citations James H. Faghmous, Ivy Frenger et al. Scientific Data profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
James H. Faghmous United States	13	338	324	309	219	197	19	1.8k
Nina Golyandina Russia	14	251 0.7×	275 0.8×	233 0.8×	149 0.7×	116 0.6×	36	2.4k
Gastón Schlotthauer Argentina	14	220 0.7×	146 0.5×	390 1.3×	357 1.6×	128 0.6×	47	3.5k
Nelson F. F. Ebecken Brazil	23	153 0.5×	157 0.5×	565 1.8×	204 0.9×	84 0.4×	209	2.0k
Xiaowei Jia United States	20	366 1.1×	68 0.2×	417 1.3×	555 2.5×	147 0.7×	125	2.2k
Xiaoli Liu China	28	632 1.9×	156 0.5×	77 0.2×	367 1.7×	343 1.7×	132	3.3k
Qiao Wang China	21	114 0.3×	259 0.8×	193 0.6×	151 0.7×	95 0.5×	125	1.9k
Minghui Hu China	17	107 0.3×	376 1.2×	543 1.8×	181 0.8×	172 0.9×	63	2.4k
Thomas Bengtsson Sweden	28	440 1.3×	102 0.3×	333 1.1×	195 0.9×	494 2.5×	111	2.8k
Rossella Arcucci United Kingdom	22	302 0.9×	72 0.2×	270 0.9×	246 1.1×	449 2.3×	87	1.5k
Marcelo A. Colominas Argentina	11	179 0.5×	143 0.4×	332 1.1×	336 1.5×	116 0.6×	29	3.3k

Countries citing papers authored by James H. Faghmous

Since Specialization

Citations

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

Fields of papers citing papers by James H. Faghmous

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by James H. Faghmous. 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 James H. Faghmous. The network helps show where James H. Faghmous may publish in the future.

Co-authorship network of co-authors of James H. Faghmous

This figure shows the co-authorship network connecting the top 25 collaborators of James H. Faghmous. A scholar is included among the top collaborators of James H. Faghmous 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 James H. Faghmous. James H. Faghmous is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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19 of 19 papers shown

Faghmous, James H., Shyam Boriah, Frode B. Vikebø, et al.. (2021). A Novel and Scalable Spatio-Temporal Technique for Ocean Eddy Monitoring. Proceedings of the AAAI Conference on Artificial Intelligence. 26(1). 281–287. 5 indexed citations

Basu, Sanjay, James H. Faghmous, & Patrick Doupé. (2020). Machine Learning Methods for Precision Medicine Research Designed to Reduce Health Disparities: A Structured Tutorial. Ethnicity & Disease. 30(Suppl 1). 217–228. 15 indexed citations

Doupé, Patrick, James H. Faghmous, & Sanjay Basu. (2019). Machine Learning for Health Services Researchers. Value in Health. 22(7). 808–815. 167 indexed citations

Scarpa, Joseph R., Emilie Bruzelius, Patrick Doupé, et al.. (2019). Assessment of Risk of Harm Associated With Intensive Blood Pressure Management Among Patients With Hypertension Who Smoke. JAMA Network Open. 2(3). e190005–e190005. 22 indexed citations

Bruzelius, Emilie, Joseph R. Scarpa, Yiyi Zhao, et al.. (2019). Huntington's disease in the United States: Variation by demographic and socioeconomic factors. Movement Disorders. 34(6). 858–865. 52 indexed citations

Baum, Aaron, Joseph R. Scarpa, Emilie Bruzelius, et al.. (2017). Targeting weight loss interventions to reduce cardiovascular complications of type 2 diabetes: a machine learning-based post-hoc analysis of heterogeneous treatment effects in the Look AHEAD trial. The Lancet Diabetes & Endocrinology. 5(10). 808–815. 76 indexed citations

Karpatne, Anuj, Gowtham Atluri, James H. Faghmous, et al.. (2017). Theory-Guided Data Science: A New Paradigm for Scientific Discovery from Data. IEEE Transactions on Knowledge and Data Engineering. 29(10). 2318–2331. 905 indexed citations breakdown →

Karpatne, Anuj, Gowtham Atluri, James H. Faghmous, et al.. (2016). Theory-guided Data Science: A New Paradigm for Scientific Discovery.. arXiv (Cornell University). 10 indexed citations

Doupé, Patrick, et al.. (2016). Equitable development through deep learning. 1–10. 18 indexed citations

10.

Chen, X. Chelsea, James H. Faghmous, Ankush Khandelwal, & Vipin Kumar. (2015). Clustering dynamic spatio-temporal patterns in the presence of noise and missing data. International Conference on Artificial Intelligence. 2575–2581. 5 indexed citations

11.

Faghmous, James H., et al.. (2015). A daily global mesoscale ocean eddy dataset from satellite altimetry. Scientific Data. 2(1). 150028–150028. 273 indexed citations breakdown →

12.

Chen, X. Chelsea, Vipin Kumar, & James H. Faghmous. (2015). Online Change Detection Algorithm for Noisy Time-Series: An Application Tonear-Real Time Burned Area Mapping. 1536–1537. 2 indexed citations

13.

Faghmous, James H., Vipin Kumar, & Shashi Shekhar. (2015). Computing and Climate. Computing in Science & Engineering. 17(6). 6–8. 2 indexed citations

14.

Faghmous, James H. & Vipin Kumar. (2014). A Big Data Guide to Understanding Climate Change: The Case for Theory-Guided Data Science. Big Data. 2(3). 155–163. 133 indexed citations

15.

Faghmous, James H., Arindam Banerjee, Shashi Shekhar, et al.. (2014). Theory-Guided Data Science for Climate Change. Computer. 47(11). 74–78. 26 indexed citations

16.

Faghmous, James H., Hung Son Nguyen, Matthew Le, & Vipin Kumar. (2014). Spatio-Temporal Consistency as a Means to Identify Unlabeled Objects in a Continuous Data Field. Proceedings of the AAAI Conference on Artificial Intelligence. 28(1). 5 indexed citations

17.

Faghmous, James H., et al.. (2013). A Parameter-Free Spatio-Temporal Pattern Mining Model to Catalog Global Ocean Dynamics. 31 indexed citations

18.

Faghmous, James H., et al.. (2013). Multiple Hypothesis Object Tracking For Unsupervised Self-Learning: An Ocean Eddy Tracking Application. Proceedings of the AAAI Conference on Artificial Intelligence. 27(1). 1277–1283. 12 indexed citations

19.

Faghmous, James H., Varun Mithal, Shyam Boriah, et al.. (2012). EddyScan: A physically consistent ocean eddy monitoring application. 96–103. 20 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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