Felipe Tobar
- Signal Processing top 5%
- Blind Source Separation Techniques 6
- Speech and Audio Processing 3
- Health Information Management top 10%
- Artificial Intelligence top 10%
- Gaussian Processes and Bayesian Inference 8
- Target Tracking and Data Fusion in Sensor Networks 7
- Neural Networks and Applications 4
- Computational Mechanics top 10%
- Advanced Adaptive Filtering Techniques 5
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- Control Systems and Identification 6
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- Structural Health Monitoring Techniques 5
- Co-authors
- Danilo P. MandicSun‐Yuan KungBárbara PobleteMarcos E. OrchardAnthony KuhRichard E. TurnerJocelyn DunstanThomas A. Glass
- Journals
- IEEE Signal Processing Letters (2 papers)ESAIM Probability and Statistics (1 paper)Medicine (1 paper)
- Partner nations
- ChileUnited KingdomUnited States
In The Last Decade
Felipe Tobar
32 papers receiving 398 citations
Peers
Comparison fields: 5 of 87
- Signal Processing 116
- Health Information Management 21
- Artificial Intelligence 144
- Computational Mechanics 92
- Communication 27
Countries citing papers authored by Felipe Tobar
This map shows the geographic impact of Felipe Tobar'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 Felipe Tobar with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Felipe Tobar more than expected).
Fields of papers citing papers by Felipe Tobar
This network shows the impact of papers produced by Felipe Tobar. 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 Felipe Tobar. The network helps show where Felipe Tobar may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Felipe Tobar, 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 | 2025 | 0 | |
| 2 | 2023 | 1 | |
| 3 | 2022 | 2 | |
| 4 | 2022 | 5 | |
| 5 | 2022 | 3 | |
| 6 | 2022 | 5 | |
| 7 | 2021 | 3 | |
| 8 | 2019 | 17 | |
| 9 | 2019 | 1 | |
| 10 | 2019 | 40 | |
| 11 | 2018 | 47 | |
| 12 | 2018 | 14 | |
| 13 | Spectral Mixture Kernels for Multi-Output Gaussian Processes | 2017 | 15 |
| 14 | 2017 | 15 | |
| 15 | 2017 | 2 | |
| 16 | 2015 | 4 | |
| 17 | 2013 | 59 | |
| 18 | 2013 | 17 | |
| 19 | 2012 | 26 | |
| 20 | 2012 | 5 |
About Felipe Tobar
Felipe Tobar is a scholar working on Signal Processing, Artificial Intelligence, Developmental Biology, Statistics and Probability and Control and Systems Engineering, having authored 35 papers that have together received 407 indexed citations. Recurring topics across this work include Gaussian Processes and Bayesian Inference (8 papers), Target Tracking and Data Fusion in Sensor Networks (7 papers), Blind Source Separation Techniques (6 papers), Control Systems and Identification (6 papers), Advanced Adaptive Filtering Techniques (5 papers), Structural Health Monitoring Techniques (5 papers), Neural Networks and Applications (4 papers) and Speech and Audio Processing (3 papers). The work is most often cited by research in Signal Processing (116 citations), Health Information Management (21 citations), Artificial Intelligence (144 citations), Computational Mechanics (92 citations) and Communication (27 citations). Felipe Tobar has collaborated with scholars based in Chile, United Kingdom and United States. Frequent co-authors include Danilo P. Mandic, Sun‐Yuan Kung, Bárbara Poblete, Marcos E. Orchard, Anthony Kuh, Richard E. Turner, Jocelyn Dunstan, Thomas A. Glass, Claudia Nau and Thang D. Bui. Their work appears in journals such as IEEE Signal Processing Letters, ESAIM Probability and Statistics, Medicine, Neural Networks and IEEE Transactions on Multimedia.
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.