Sana Tonekaboni

616 total citations
8 papers, 157 citations indexed

About

Sana Tonekaboni is a scholar working on Artificial Intelligence, Signal Processing and Cardiology and Cardiovascular Medicine. According to data from OpenAlex, Sana Tonekaboni has authored 8 papers receiving a total of 157 indexed citations (citations by other indexed papers that have themselves been cited), including 5 papers in Artificial Intelligence, 3 papers in Signal Processing and 2 papers in Cardiology and Cardiovascular Medicine. Recurrent topics in Sana Tonekaboni's work include Machine Learning in Healthcare (4 papers), Explainable Artificial Intelligence (XAI) (4 papers) and Time Series Analysis and Forecasting (3 papers). Sana Tonekaboni is often cited by papers focused on Machine Learning in Healthcare (4 papers), Explainable Artificial Intelligence (XAI) (4 papers) and Time Series Analysis and Forecasting (3 papers). Sana Tonekaboni collaborates with scholars based in Canada, United States and Australia. Sana Tonekaboni's co-authors include Muhammad Tariqus Salam, José Luis Pérez Velázquez, Nima Soltani, Anna Goldenberg, Karim Abdelhalim, Hossein Kassiri, Roman Genov, Danny Eytan, Shalmali Joshi and David Duvenaud and has published in prestigious journals such as npj Digital Medicine, IEEE Transactions on Biomedical Circuits and Systems and BMJ evidence-based medicine.

In The Last Decade

Sana Tonekaboni

8 papers receiving 155 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Sana Tonekaboni Canada	6	68	64	44	38	34	8	157
Mauro F. Pinto Portugal	9	19 0.3×	170 2.7×	14 0.3×	44 1.2×	17 0.5×	17	265
Niranjan Chakravarthy United States	8	96 1.4×	143 2.2×	24 0.5×	16 0.4×	29 0.9×	16	344
Eike Petersen Germany	9	13 0.2×	75 1.2×	8 0.2×	27 0.7×	90 2.6×	18	203
Fábio Lopes Portugal	10	16 0.2×	157 2.5×	13 0.3×	62 1.6×	6 0.2×	20	265
Sami Arıca Türkiye	9	126 1.9×	265 4.1×	45 1.0×	33 0.9×	48 1.4×	27	337
Rodrigo Echeveste Argentina	5	27 0.4×	78 1.2×	24 0.5×	55 1.4×	18 0.5×	9	201
Ali Gharaviri Netherlands	13	33 0.5×	100 1.6×	10 0.2×	19 0.5×	34 1.0×	32	532
Jianbiao Xiao China	7	48 0.7×	120 1.9×	161 3.7×	62 1.6×	80 2.4×	16	317
Yevgeny Perelman Israel	9	155 2.3×	128 2.0×	233 5.3×	12 0.3×	120 3.5×	13	314

Countries citing papers authored by Sana Tonekaboni

Since Specialization

Citations

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

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

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8 of 8 papers shown

1.

McCradden, Melissa D., et al.. (2025). What makes a ‘good’ decision with artificial intelligence? A grounded theory study in paediatric care. BMJ evidence-based medicine. 30(3). 183–193. 1 indexed citations

2.

Nazaret, Achille, et al.. (2023). Modeling personalized heart rate response to exercise and environmental factors with wearables data. npj Digital Medicine. 6(1). 207–207. 9 indexed citations

3.

Tonekaboni, Sana, Danny Eytan, & Anna Goldenberg. (2021). Unsupervised Representation Learning for Time Series with Temporal Neighborhood Coding. arXiv (Cornell University). 15 indexed citations

4.

Tonekaboni, Sana, Shalmali Joshi, Kieran R. Campbell, David Duvenaud, & Anna Goldenberg. (2020). What went wrong and when? Instance-wise feature importance for time-series black-box models. Neural Information Processing Systems. 33. 799–809. 14 indexed citations

5.

Tonekaboni, Sana, Shalmali Joshi, Melissa D. McCradden, & Anna Goldenberg. (2019). What Clinicians Want: Contextualizing Explainable Machine Learning for Clinical End Use.. 359–380. 9 indexed citations

6.

Tonekaboni, Sana, Shalmali Joshi, David Duvenaud, & Anna Goldenberg. (2019). Explaining Time Series by Counterfactuals. 3 indexed citations

7.

Tonekaboni, Sana, Mjaye Mazwi, Peter C. Laussen, et al.. (2018). Prediction of Cardiac Arrest from Physiological Signals in the Pediatric ICU. 534–550. 15 indexed citations

8.

Kassiri, Hossein, Sana Tonekaboni, Muhammad Tariqus Salam, et al.. (2017). Closed-Loop Neurostimulators: A Survey and A Seizure-Predicting Design Example for Intractable Epilepsy Treatment. IEEE Transactions on Biomedical Circuits and Systems. 11(5). 1026–1040. 91 indexed citations

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.

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