Imme Ebert‐Uphoff

4.6k total citations
94 papers, 2.7k citations indexed

About

Imme Ebert‐Uphoff is a scholar working on Control and Systems Engineering, Artificial Intelligence and Global and Planetary Change. According to data from OpenAlex, Imme Ebert‐Uphoff has authored 94 papers receiving a total of 2.7k indexed citations (citations by other indexed papers that have themselves been cited), including 34 papers in Control and Systems Engineering, 28 papers in Artificial Intelligence and 23 papers in Global and Planetary Change. Recurrent topics in Imme Ebert‐Uphoff's work include Robotic Mechanisms and Dynamics (30 papers), Meteorological Phenomena and Simulations (20 papers) and Climate variability and models (18 papers). Imme Ebert‐Uphoff is often cited by papers focused on Robotic Mechanisms and Dynamics (30 papers), Meteorological Phenomena and Simulations (20 papers) and Climate variability and models (18 papers). Imme Ebert‐Uphoff collaborates with scholars based in United States, Canada and Switzerland. Imme Ebert‐Uphoff's co-authors include Gregory S. Chirikjian, Elizabeth A. Barnes, Paul Bosscher, Yi Deng, Benjamin A. Toms, Antonios Mamalakis, Kyle Hilburn, Clément Gosselin, William Singhose and Ryan Lagerquist and has published in prestigious journals such as Nature, SHILAP Revista de lepidopterología and Journal of Climate.

In The Last Decade

Imme Ebert‐Uphoff

91 papers receiving 2.6k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Imme Ebert‐Uphoff United States 29 1.1k 667 623 594 575 94 2.7k
Matthias Müller Germany 24 707 0.7× 173 0.3× 302 0.5× 59 0.1× 219 0.4× 42 4.1k
Asgeir J. Sørensen Norway 38 2.5k 2.4× 143 0.2× 90 0.1× 91 0.2× 509 0.9× 219 5.1k
Sara Fleury France 19 338 0.3× 144 0.2× 76 0.1× 355 0.6× 222 0.4× 47 1.6k
Roberto Sabatini Australia 34 354 0.3× 384 0.6× 122 0.2× 53 0.1× 116 0.2× 253 3.9k
Wei He China 38 289 0.3× 173 0.3× 241 0.4× 619 1.0× 222 0.4× 184 5.3k
Jiang Li United States 25 102 0.1× 343 0.5× 291 0.5× 198 0.3× 72 0.1× 175 2.1k
Richard J. Radke United States 27 157 0.1× 125 0.2× 510 0.8× 364 0.6× 91 0.2× 102 3.5k
Steve Chien United States 34 280 0.3× 211 0.3× 71 0.1× 190 0.3× 358 0.6× 318 4.1k
Jing Xiao China 24 222 0.2× 104 0.2× 158 0.3× 237 0.4× 98 0.2× 136 2.2k
Zhengxia Zou China 34 240 0.2× 140 0.2× 148 0.2× 497 0.8× 98 0.2× 113 5.4k

Countries citing papers authored by Imme Ebert‐Uphoff

Since Specialization
Citations

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

Fields of papers citing papers by Imme Ebert‐Uphoff

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Imme Ebert‐Uphoff. 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 Imme Ebert‐Uphoff. The network helps show where Imme Ebert‐Uphoff may publish in the future.

Co-authorship network of co-authors of Imme Ebert‐Uphoff

This figure shows the co-authorship network connecting the top 25 collaborators of Imme Ebert‐Uphoff. A scholar is included among the top collaborators of Imme Ebert‐Uphoff 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 Imme Ebert‐Uphoff. Imme Ebert‐Uphoff 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.
Mamalakis, Antonios, et al.. (2025). The influence of correlated features on neural network attribution methods in geoscience. SHILAP Revista de lepidopterología. 4. 1 indexed citations
2.
Ebert‐Uphoff, Imme, et al.. (2024). Accelerating Community-Wide Evaluation of AI Models for Global Weather Prediction by Facilitating Access to Model Output. Bulletin of the American Meteorological Society. 106(1). E68–E76.
3.
McGovern, Amy, Imme Ebert‐Uphoff, Elizabeth A. Barnes, et al.. (2024). AI2ES: The NSF AI Institute for Research on Trustworthy AI for Weather, Climate, and Coastal Oceanography. AI Magazine. 45(1). 105–110. 1 indexed citations
4.
Lagerquist, Ryan, David D. Turner, Imme Ebert‐Uphoff, & Jebb Q. Stewart. (2023). Estimating Full Longwave and Shortwave Radiative Transfer with Neural Networks of Varying Complexity. Journal of Atmospheric and Oceanic Technology. 40(11). 1407–1432. 5 indexed citations
5.
Lagerquist, Ryan & Imme Ebert‐Uphoff. (2022). Can We Integrate Spatial Verification Methods into Neural Network Loss Functions for Atmospheric Science?. NOAA Institutional Repository. 1(4). 4 indexed citations
6.
Kummerow, Christian D., et al.. (2021). Applying machine learning methods to detect convection using Geostationary Operational Environmental Satellite-16 (GOES-16) advanced baseline imager (ABI) data. Atmospheric measurement techniques. 14(4). 2699–2716. 29 indexed citations
7.
Barnes, Elizabeth A., et al.. (2021). Strengthened Causal Connections Between the MJO and the North Atlantic With Climate Warming. Geophysical Research Letters. 48(5). 9 indexed citations
8.
Haynes, John M., Yoo‐Jeong Noh, Steven D. Miller, et al.. (2021). Low Cloud Detection in Multilayer Scenes Using Satellite Imagery with Machine Learning Methods. Journal of Atmospheric and Oceanic Technology. 39(3). 319–334. 11 indexed citations
9.
Kummerow, Christian D., et al.. (2020). Applying machine learning methods to detect convection usingGOES-16 ABI data. 4 indexed citations
10.
Pennington, Deana, Imme Ebert‐Uphoff, Natalie Freed, Jo Martin, & Suzanne A. Pierce. (2019). Bridging sustainability science, earth science, and data science through interdisciplinary education. Sustainability Science. 15(2). 647–661. 21 indexed citations
11.
Cooley, Daniel, et al.. (2019). New Exploratory Tools for Extremal Dependence: $$\chi $$ χ Networks and Annual Extremal Networks. Open MIND. 6 indexed citations
12.
Barnes, Elizabeth A., et al.. (2019). Tropospheric and Stratospheric Causal Pathways Between the MJO and NAO. Journal of Geophysical Research Atmospheres. 124(16). 9356–9371. 53 indexed citations
13.
McGraw, Marie C., et al.. (2018). A study of links between the Arctic and the midlatitude jet stream using Granger and Pearl causality. Environmetrics. 30(4). 22 indexed citations
14.
Ebert‐Uphoff, Imme, David R. Thompson, İbrahim Demir, et al.. (2017). A VISION FOR THE DEVELOPMENT OF BENCHMARKS TO BRIDGE GEOSCIENCE AND DATA SCIENCE. 14 indexed citations
15.
Baker, Allison H., Dorit Hammerling, Sheri Mickelson, et al.. (2016). Evaluating lossy data compression on climate simulation data within a large ensemble. Geoscientific model development. 9(12). 4381–4403. 46 indexed citations
16.
Baker, Allison H., Dorit Hammerling, Haiying Xu, et al.. (2016). Evaluating Lossy Data Compression on Climate Simulation Datawithin a Large Ensemble. 5 indexed citations
17.
Ebert‐Uphoff, Imme & Yi Deng. (2012). A new type of climate network based on probabilistic graphical models: Results of boreal winter versus summer. Geophysical Research Letters. 39(19). 44 indexed citations
18.
Singhose, William, et al.. (2005). Performance Measures For Input Shaping and Command Generation. Journal of Dynamic Systems Measurement and Control. 128(3). 731–736. 33 indexed citations
19.
Bosscher, Paul & Imme Ebert‐Uphoff. (2004). A stability measure for underconstrained cable-driven robots. 4943–4949 Vol.5. 28 indexed citations
20.
Ebert‐Uphoff, Imme. (2003). Introducing parallel manipulators through laboratory experiments. IEEE Robotics & Automation Magazine. 10(3). 13–19. 6 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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