Liudmila Ulanova
- Signal Processing top 1%
- Artificial Intelligence top 5%
- Economics and Econometrics top 10%
- Computer Vision and Pattern Recognition top 10%
- Computer Networks and Communications top 10%
- Co-authors
- Eamonn KeoghNurjahan BegumYifei DingChin‐Chia Michael YehDiego Furtado SilvaYan ZhuAbdullah MueenHoang Anh Dau
- Topics
- Time Series Analysis and Forecasting (9 papers)Anomaly Detection Techniques and Applications (7 papers)Music and Audio Processing (3 papers)
- Partner nations
- United StatesBrazilAustralia
In The Last Decade
Liudmila Ulanova
11 papers receiving 739 citations
Hit Papers
Peers
Comparison fields: 5 of 94
- Signal Processing 489
- Artificial Intelligence 414
- Economics and Econometrics 117
- Computer Vision and Pattern Recognition 105
- Computer Networks and Communications 102
Countries citing papers authored by Liudmila Ulanova
This map shows the geographic impact of Liudmila Ulanova'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 Liudmila Ulanova with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Liudmila Ulanova more than expected).
Fields of papers citing papers by Liudmila Ulanova
This network shows the impact of papers produced by Liudmila Ulanova. 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 Liudmila Ulanova. The network helps show where Liudmila Ulanova may publish in the future.
Co-authorship network of co-authors of Liudmila Ulanova
This figure shows the co-authorship network connecting the top 25 collaborators of Liudmila Ulanova. A scholar is included among the top collaborators of Liudmila Ulanova 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 Liudmila Ulanova. Liudmila Ulanova is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 87 | |
| 2 | 51 | |
| 3 | Matrix Profile I: All Pairs Similarity Joins for Time Series: A Unifying View That Includes Motifs, Discords and Shapeletsbreakdown → | 350 |
| 4 | 9 | |
| 5 | 49 | |
| 6 | 28 | |
| 7 | 77 | |
| 8 | 12 | |
| 9 | 1 | |
| 10 | 72 | |
| 11 | 23 |
About Liudmila Ulanova
Liudmila Ulanova is a scholar working on Signal Processing, Artificial Intelligence and Computer Science Applications, having authored 11 papers that have together received 759 indexed citations. Recurring topics across this work include Time Series Analysis and Forecasting (9 papers), Anomaly Detection Techniques and Applications (7 papers) and Music and Audio Processing (3 papers). The work is most often cited by research in Signal Processing (489 citations), Artificial Intelligence (414 citations) and Software (35 citations). Liudmila Ulanova has collaborated with scholars based in United States, Brazil and Australia. Frequent co-authors include Eamonn Keogh, Nurjahan Begum, Yifei Ding, Chin‐Chia Michael Yeh, Diego Furtado Silva, Yan Zhu, Abdullah Mueen, Hoang Anh Dau, Jun Wang and Pamela Bhattacharya. Their work appears in journals such as Sensors, Data Mining and Knowledge Discovery and Journal of Computer Science.
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.