Jess Tate

684 total citations
51 papers, 480 citations indexed

About

Jess Tate is a scholar working on Cardiology and Cardiovascular Medicine, Radiology, Nuclear Medicine and Imaging and Statistics, Probability and Uncertainty. According to data from OpenAlex, Jess Tate has authored 51 papers receiving a total of 480 indexed citations (citations by other indexed papers that have themselves been cited), including 40 papers in Cardiology and Cardiovascular Medicine, 14 papers in Radiology, Nuclear Medicine and Imaging and 7 papers in Statistics, Probability and Uncertainty. Recurrent topics in Jess Tate's work include Cardiac electrophysiology and arrhythmias (30 papers), Cardiovascular Function and Risk Factors (14 papers) and ECG Monitoring and Analysis (10 papers). Jess Tate is often cited by papers focused on Cardiac electrophysiology and arrhythmias (30 papers), Cardiovascular Function and Risk Factors (14 papers) and ECG Monitoring and Analysis (10 papers). Jess Tate collaborates with scholars based in United States, Netherlands and France. Jess Tate's co-authors include Rob MacLeod, Wilson Good, Brett Burton, Brian Zenger, Dana H. Brooks, Jake Bergquist, Kedar Aras, Peter van Dam, Jaume Coll‐Font and Akil Narayan and has published in prestigious journals such as Circulation, IEEE Transactions on Biomedical Engineering and Frontiers in Physiology.

In The Last Decade

Jess Tate

49 papers receiving 472 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Jess Tate United States	13	357	109	51	47	46	51	480
Jaume Coll‐Font United States	13	294 0.8×	210 1.9×	65 1.3×	25 0.5×	55 1.2×	57	520
F. Hanser Austria	12	347 1.0×	155 1.4×	104 2.0×	22 0.5×	95 2.1×	49	532
Sam Coveney United Kingdom	11	188 0.5×	36 0.3×	25 0.5×	46 1.0×	40 0.9×	25	322
Laura Bear France	14	507 1.4×	143 1.3×	92 1.8×	9 0.2×	66 1.4×	55	604
R. Modre Austria	15	487 1.4×	266 2.4×	172 3.4×	34 0.7×	116 2.5×	46	707
Dongdong Deng China	10	567 1.6×	114 1.0×	27 0.5×	11 0.2×	68 1.5×	35	691
Simone Pezzuto Switzerland	14	394 1.1×	64 0.6×	38 0.7×	39 0.8×	195 4.2×	41	638
Brett Burton United States	9	219 0.6×	80 0.7×	42 0.8×	17 0.4×	26 0.6×	19	280
Matthijs Cluitmans Netherlands	12	467 1.3×	142 1.3×	70 1.4×	13 0.3×	52 1.1×	48	565
Subham Ghosh United States	16	572 1.6×	74 0.7×	47 0.9×	7 0.1×	40 0.9×	26	665

Countries citing papers authored by Jess Tate

Since Specialization

Citations

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

Fields of papers citing papers by Jess Tate

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Jess Tate

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

All Works

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20 of 20 papers shown

Tate, Jess, Jake Bergquist, Sumientra Rampersad, et al.. (2023). UncertainSCI: A Python Package for NoninvasiveParametric Uncertainty Quantification of Simulation Pipelines. The Journal of Open Source Software. 8(90). 4249–4249. 3 indexed citations

Bergquist, Jake, Brian Zenger, Jess Tate, et al.. (2023). Uncertainty quantification of the effect of cardiac position variability in the inverse problem of electrocardiographic imaging. Physiological Measurement. 44(10). 105003–105003. 4 indexed citations

Švehlíková, Jana, Dana H. Brooks, Peter van Dam, et al.. (2022). The Effect of Segmentation Variability in Forward ECG Simulation. Computing in cardiology. 49.

Tate, Jess, Néjib Zemzemi, Shireen Elhabian, et al.. (2022). Segmentation Uncertainty Quantification in Cardiac Propagation Models. Computing in cardiology. 1 indexed citations

Zenger, Brian, Wilson Good, Jaume Coll‐Font, et al.. (2022). Heart Position Uncertainty Quantification in the Inverse Problem of ECGI. Computing in cardiology. 3 indexed citations

Narayan, Akil, Jake Bergquist, Sumientra Rampersad, et al.. (2022). UncertainSCI: Uncertainty quantification for computational models in biomedicine and bioengineering. Computers in Biology and Medicine. 152. 106407–106407. 18 indexed citations

Tate, Jess, Wilson Good, Néjib Zemzemi, et al.. (2021). Uncertainty Quantification of the Effects of Segmentation Variability in ECGI. Lecture notes in computer science. 12738. 515–522. 17 indexed citations

Bergquist, Jake, Brian Zenger, Karli Gillette, et al.. (2021). The Role of Myocardial Fiber Direction in Epicardial Activation Patterns via Uncertainty Quantification. PubMed. 48. 1–4. 4 indexed citations

Bergquist, Jake, Sumientra Rampersad, Jess Tate, et al.. (2020). Using UncertainSCI to Quantify Uncertainty in Cardiac Simulations. Computing in cardiology. 47. 11 indexed citations

10.

Bergquist, Jake, Wilson Good, Brian Zenger, Jess Tate, & Rob MacLeod. (2019). Optimizing the Reconstruction of Cardiac Potentials Using a Novel High Resolution Pericardiac Cage. Computing in cardiology. 6 indexed citations

11.

Tate, Jess, Steffen Schuler, Olaf Dössel, Rob MacLeod, & Thom F. Oostendorp. (2019). Correcting Undersampled Cardiac Sources in Equivalent Double Layer Forward Simulations. Lecture notes in computer science. 11504. 147–155. 5 indexed citations

12.

Tate, Jess, et al.. (2019). Validating defibrillation simulation in a human-shaped phantom. Heart Rhythm. 17(4). 661–668. 5 indexed citations

13.

Zenger, Brian, Wilson Good, Jake Bergquist, et al.. (2019). Novel experimental model for studying the spatiotemporal electrical signature of acute myocardial ischemia: a translational platform. Physiological Measurement. 41(1). 15002–15002. 23 indexed citations

14.

Tate, Jess, Néjib Zemzemi, Wilson Good, et al.. (2018). Effect of Segmentation Variation on ECG Imaging. Computing in cardiology. 45. 13 indexed citations

15.

Coll‐Font, Jaume, et al.. (2017). Overcoming Barriers to Quantification and Comparison of Electrocardiographic Imaging Methods: a Community-Based Approach. Computing in cardiology. 44. 10 indexed citations

16.

Aras, Kedar, Wilson Good, Jess Tate, et al.. (2015). Experimental Data and Geometric Analysis Repository—EDGAR. Journal of Electrocardiology. 48(6). 975–981. 65 indexed citations

17.

Tate, Jess, et al.. (2013). Abstract 13241: Effect of Left Ventricular Assist Devices on Defibrillation Thresholds a Computational Simulation. Circulation. 128. 1 indexed citations

18.

Tate, Jess, et al.. (2011). Measuring defibrillator surface potentials for simulation verification. PubMed. 2011. 239–242. 3 indexed citations

19.

Burton, Brett, Jess Tate, Burak Erem, et al.. (2011). A toolkit for forward/inverse problems in electrocardiography within the SCIRun problem solving environment. PubMed. 2011. 267–270. 44 indexed citations

20.

Levine, Joshua A., et al.. (2010). The effect of non-conformal finite element boundaries on electrical monodomain and Bidomain simulations. Computing in Cardiology. 97–100. 3 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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