Jordan Ott
Impact in
- Atmospheric Science top 10%
- Meteorological Phenomena and Simulations
Papers in
-
- Machine Learning and Data Classification 3
- Domain Adaptation and Few-Shot Learning 2
-
- Advanced Image and Video Retrieval Techniques 3
- Multimodal Machine Learning Applications 2
- Co-authors
- Pierre Baldi (8 shared papers)Michael S. Pritchard (2 shared papers)Tom Beucler (3 shared papers)Pierre Gentine (3 shared papers)Stephan Rasp (1 shared paper)Erik Linstead (6 shared papers)Uri Maoz (1 shared paper)Elnaz Lashgari (1 shared paper)
- Journals
- Journal Of Big Data (2 papers)IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing (1 paper)Journal of Neural Engineering (1 paper)Physical Review Letters (1 paper)Scientific Reports (1 paper)
- Partner nations
- United StatesGermany
In The Last Decade
Jordan Ott
14 papers receiving 410 citations
Jordan Ott's Hit Papers
Peers
Comparison fields: 5 of 90
- Atmospheric Science 121
- Software 19
- Global and Planetary Change 98
- Statistical and Nonlinear Physics 53
- Environmental Engineering 38
Countries citing papers authored by Jordan Ott
This map shows the geographic impact of Jordan Ott'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 Jordan Ott with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jordan Ott more than expected).
Fields of papers citing papers by Jordan Ott
This network shows the impact of papers produced by Jordan Ott. 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 Jordan Ott. The network helps show where Jordan Ott may publish in the future.
Co-authors
The 22 scholars most cited alongside Jordan Ott, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | Enforcing Analytic Constraints in Neural Networks Emulating Physical Systems Hit paper breakdown → | 2021 | 210 |
| 2 | 2021 | 33 | |
| 3 | 2018 | 31 | |
| 4 | Assessing the Potential of Deep Learning for Emulating Cloud Superparameterization in Climate Models With Real‐Geography Boundary Conditions | 2021 | 30 |
| 5 | 2020 | 24 | |
| 6 | 2018 | 20 | |
| 7 | 2022 | 15 | |
| 8 | 2019 | 14 | |
| 9 | 2023 | 12 | |
| 10 | 2019 | 12 | |
| 11 | 2021 | 8 | |
| 12 | 2023 | 7 | |
| 13 | 2020 | 2 | |
| 14 | 2021 | 1 |
About Jordan Ott
Jordan Ott is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition, Astronomy and Astrophysics, Information Systems and Global and Planetary Change, having authored 14 papers that have together received 419 indexed citations. Recurring topics across this work include Machine Learning and Data Classification (3 papers), Advanced Image and Video Retrieval Techniques (3 papers), Atmospheric aerosols and clouds (2 papers), Climate variability and models (2 papers), Software Engineering Research (2 papers), Gamma-ray bursts and supernovae (2 papers), Domain Adaptation and Few-Shot Learning (2 papers) and Multimodal Machine Learning Applications (2 papers). The work is most often cited by research in Atmospheric Science (121 citations), Software (19 citations), Global and Planetary Change (98 citations), Statistical and Nonlinear Physics (53 citations) and Environmental Engineering (38 citations). Jordan Ott has collaborated with scholars based in United States and Germany. Frequent co-authors include Pierre Baldi, Michael S. Pritchard, Tom Beucler, Pierre Gentine, Stephan Rasp, Erik Linstead, Uri Maoz, Elnaz Lashgari, Galen Yacalis and Griffin Mooers. Their work appears in journals such as Journal Of Big Data, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Journal of Neural Engineering, Physical Review Letters and Scientific Reports.
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.