Jaya Kawale
- Information Systems top 2%
- Artificial Intelligence top 5%
- Computer Vision and Pattern Recognition top 5%
- Management Science and Operations Research top 5%
- Global and Planetary Change
- Co-authors
- Sheng LiYun FuJaideep SrivastavaAditya PalStefan LiessVipin KumarMichael SteinbachHung Bui
- Topics
- Climate variability and models (8 papers)Recommender Systems and Techniques (5 papers)Complex Systems and Time Series Analysis (5 papers)
- Journals
- Journal of ClimateStatistical Analysis and Data Mining The ASA Data Science JournalANU Open Research (Australian National University)
- Partner nations
- United StatesSouth AfricaQatar
In The Last Decade
Jaya Kawale
16 papers receiving 612 citations
Hit Papers
Peers
Comparison fields: 5 of 72
- Information Systems 360
- Artificial Intelligence 268
- Computer Vision and Pattern Recognition 154
- Management Science and Operations Research 95
- Global and Planetary Change 75
Countries citing papers authored by Jaya Kawale
This map shows the geographic impact of Jaya Kawale'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 Jaya Kawale with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jaya Kawale more than expected).
Fields of papers citing papers by Jaya Kawale
This network shows the impact of papers produced by Jaya Kawale. 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 Jaya Kawale. The network helps show where Jaya Kawale may publish in the future.
Co-authorship network of co-authors of Jaya Kawale
This figure shows the co-authorship network connecting the top 25 collaborators of Jaya Kawale. A scholar is included among the top collaborators of Jaya Kawale 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 Jaya Kawale. Jaya Kawale is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 7 | |
| 2 | Practical linear models for large-scale one-class collaborative filtering | 10 |
| 3 | Matching via dimensionality reduction for estimation of treatment effects in digital marketing campaigns | 14 |
| 4 | 54 | |
| 5 | Deep Collaborative Filtering via Marginalized Denoising Auto-encoderbreakdown → | 293 |
| 6 | 18 | |
| 7 | 11 | |
| 8 | 16 | |
| 9 | 15 | |
| 10 | 26 | |
| 11 | 4 | |
| 12 | 13 | |
| 13 | 15 | |
| 14 | Data guided discovery of dynamic climate dipoles | 9 |
| 15 | 16 | |
| 16 | 110 |
About Jaya Kawale
Jaya Kawale is a scholar working on Global and Planetary Change, Transportation and Signal Processing, having authored 16 papers that have together received 631 indexed citations. Recurring topics across this work include Climate variability and models (8 papers), Recommender Systems and Techniques (5 papers) and Complex Systems and Time Series Analysis (5 papers). The work is most often cited by research in Information Systems (360 citations), Computational Mathematics (8 citations) and Artificial Intelligence (268 citations). Jaya Kawale has collaborated with scholars based in United States, South Africa and Qatar. Frequent co-authors include Sheng Li, Yun Fu, Jaideep Srivastava, Aditya Pal, Stefan Liess, Vipin Kumar, Michael Steinbach, Hung Bui, Branislav Kveton and Sanjay Chawla. Their work appears in journals such as Journal of Climate, Statistical Analysis and Data Mining The ASA Data Science Journal and ANU Open Research (Australian National University).
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.