Jonathan Tepper
- Artificial Intelligence
- Economics and Econometrics
- Management Science and Operations Research top 10%
- General Economics, Econometrics and Finance
- Electrical and Electronic Engineering
- Co-authors
- Dominic Palmer-BrownHeather M. PowellJane M. BinnerMufti MahmudT.M. McGinnityAhmad LotfiChris RoadknightLeo E. Hollister
- Topics
- Stock Market Forecasting Methods (8 papers)Neural Networks and Applications (4 papers)Neural Networks and Reservoir Computing (3 papers)
- Cited by
- General Economics, Econometrics and FinanceManagement Science and Operations ResearchArtificial Intelligence
- Partner nations
- United KingdomUnited StatesSweden
In The Last Decade
Jonathan Tepper
21 papers receiving 192 citations
Peers
Comparison fields: 5 of 76
- Artificial Intelligence 70
- Economics and Econometrics 52
- Management Science and Operations Research 42
- General Economics, Econometrics and Finance 31
- Electrical and Electronic Engineering 22
Countries citing papers authored by Jonathan Tepper
This map shows the geographic impact of Jonathan Tepper'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 Jonathan Tepper with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jonathan Tepper more than expected).
Fields of papers citing papers by Jonathan Tepper
This network shows the impact of papers produced by Jonathan Tepper. 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 Jonathan Tepper. The network helps show where Jonathan Tepper may publish in the future.
Co-authorship network of co-authors of Jonathan Tepper
This figure shows the co-authorship network connecting the top 25 collaborators of Jonathan Tepper. A scholar is included among the top collaborators of Jonathan Tepper 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 Jonathan Tepper. Jonathan Tepper is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 0 | |
| 2 | 2 | |
| 3 | 2 | |
| 4 | 4 | |
| 5 | 2 | |
| 6 | 14 | |
| 7 | 32 | |
| 8 | 10 | |
| 9 | Characterizing the Magnetospheric State for Sawtooth Events | 1 |
| 10 | 0 | |
| 11 | Endgame: The End of the Debt SuperCycle and How It Changes Everything | 9 |
| 12 | 17 | |
| 13 | 3 | |
| 14 | 21 | |
| 15 | 12 | |
| 16 | FAST LEARNING NEURAL NETS WITH ADAPTIVE LEARNING STYLES | 2 |
| 17 | 2 | |
| 18 | 14 | |
| 19 | 7 | |
| 20 | 13 |
About Jonathan Tepper
Jonathan Tepper is a scholar working on Management Science and Operations Research, General Economics, Econometrics and Finance and Artificial Intelligence, having authored 25 papers that have together received 207 indexed citations. Recurring topics across this work include Stock Market Forecasting Methods (8 papers), Neural Networks and Applications (4 papers) and Neural Networks and Reservoir Computing (3 papers). The work is most often cited by research in General Economics, Econometrics and Finance (31 citations), Management Science and Operations Research (42 citations) and Artificial Intelligence (70 citations). Jonathan Tepper has collaborated with scholars based in United Kingdom, United States and Sweden. Frequent co-authors include Dominic Palmer-Brown, Heather M. Powell, Jane M. Binner, Mufti Mahmud, T.M. McGinnity, Ahmad Lotfi, Chris Roadknight, Leo E. Hollister, Kenneth L. Davis and Barry E. Jones. Their work appears in journals such as Cancer, Trends in Cognitive Sciences and Psychopharmacology.
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.