Sana Tonekaboni
- Cellular and Molecular Neuroscience
- Cognitive Neuroscience
- Electrical and Electronic Engineering
- Artificial Intelligence
- Biomedical Engineering
- Co-authors
- Anna GoldenbergNima SoltaniJosé Luis Pérez VelázquezKarim AbdelhalimHossein KassiriMuhammad Tariqus SalamRoman GenovDanny Eytan
- Topics
- Machine Learning in Healthcare (4 papers)Explainable Artificial Intelligence (XAI) (4 papers)Time Series Analysis and Forecasting (3 papers)
- Journals
- npj Digital MedicineIEEE Transactions on Biomedical Circuits and SystemsBMJ evidence-based medicine
- Partner nations
- CanadaUnited StatesAustralia
In The Last Decade
Sana Tonekaboni
8 papers receiving 155 citations
Peers
Comparison fields: 5 of 55
- Cellular and Molecular Neuroscience 68
- Cognitive Neuroscience 64
- Electrical and Electronic Engineering 44
- Artificial Intelligence 38
- Biomedical Engineering 34
Countries citing papers authored by Sana Tonekaboni
This map shows the geographic impact of Sana Tonekaboni'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 Sana Tonekaboni with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Sana Tonekaboni more than expected).
Fields of papers citing papers by Sana Tonekaboni
This network shows the impact of papers produced by Sana Tonekaboni. 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 Sana Tonekaboni. The network helps show where Sana Tonekaboni may publish in the future.
Co-authorship network of co-authors of Sana Tonekaboni
This figure shows the co-authorship network connecting the top 25 collaborators of Sana Tonekaboni. A scholar is included among the top collaborators of Sana Tonekaboni 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 Sana Tonekaboni. Sana Tonekaboni is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 1 | |
| 2 | 9 | |
| 3 | 15 | |
| 4 | What went wrong and when? Instance-wise feature importance for time-series black-box models | 14 |
| 5 | What Clinicians Want: Contextualizing Explainable Machine Learning for Clinical End Use. | 9 |
| 6 | Explaining Time Series by Counterfactuals | 3 |
| 7 | Prediction of Cardiac Arrest from Physiological Signals in the Pediatric ICU | 15 |
| 8 | 91 |
About Sana Tonekaboni
Sana Tonekaboni is a scholar working on Health Informatics, Signal Processing and Artificial Intelligence, having authored 8 papers that have together received 157 indexed citations. Recurring topics across this work include Machine Learning in Healthcare (4 papers), Explainable Artificial Intelligence (XAI) (4 papers) and Time Series Analysis and Forecasting (3 papers). The work is most often cited by research in Health Informatics (9 citations), Cellular and Molecular Neuroscience (68 citations) and Cognitive Neuroscience (64 citations). Sana Tonekaboni has collaborated with scholars based in Canada, United States and Australia. Frequent co-authors include Anna Goldenberg, Nima Soltani, José Luis Pérez Velázquez, Karim Abdelhalim, Hossein Kassiri, Muhammad Tariqus Salam, Roman Genov, Danny Eytan, Shalmali Joshi and David Duvenaud. Their work appears in journals such as npj Digital Medicine, IEEE Transactions on Biomedical Circuits and Systems and BMJ evidence-based medicine.
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.