Théo Ryffel

1.5k total citations · 1 hit paper
7 papers, 321 citations indexed

About

Théo Ryffel is a scholar working on Artificial Intelligence, Health Informatics and Public Health, Environmental and Occupational Health. According to data from OpenAlex, Théo Ryffel has authored 7 papers receiving a total of 321 indexed citations (citations by other indexed papers that have themselves been cited), including 7 papers in Artificial Intelligence, 2 papers in Health Informatics and 1 paper in Public Health, Environmental and Occupational Health. Recurrent topics in Théo Ryffel's work include Privacy-Preserving Technologies in Data (4 papers), Cryptography and Data Security (3 papers) and Adversarial Robustness in Machine Learning (2 papers). Théo Ryffel is often cited by papers focused on Privacy-Preserving Technologies in Data (4 papers), Cryptography and Data Security (3 papers) and Adversarial Robustness in Machine Learning (2 papers). Théo Ryffel collaborates with scholars based in France, United Kingdom and United States. Théo Ryffel's co-authors include Daniel Rueckert, Rickmer Braren, Jason Mancuso, Alexander Ziller, M. Steinborn, Andreas Saleh, Georgios Kaissis, Marcus R. Makowski, Jonathan Passerat‐Palmbach and Friederike Jungmann and has published in prestigious journals such as SHILAP Revista de lepidopterología, Nature Machine Intelligence and JMIR Medical Informatics.

In The Last Decade

Théo Ryffel

6 papers receiving 311 citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

End-to-end privacy preserving deep learning on multi-institutional medical imaging

2021 255 citations Georgios Kaissis, Alexander Ziller et al. Nature Machine Intelligence profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Théo Ryffel France	5	241	65	64	35	33	7	321
Andrew Trask United Kingdom	5	262 1.1×	70 1.1×	65 1.0×	31 0.9×	38 1.2×	6	354
Jason Mancuso Germany	2	190 0.8×	65 1.0×	60 0.9×	26 0.7×	28 0.8×	2	265
Dmitrii Usynin Germany	6	310 1.3×	90 1.4×	94 1.5×	40 1.1×	42 1.3×	9	412
Alexander Ziller Germany	5	297 1.2×	106 1.6×	101 1.6×	35 1.0×	43 1.3×	8	420
Delaram Kahrobaei United States	7	168 0.7×	25 0.4×	18 0.3×	48 1.4×	41 1.2×	42	266
Imrana Abdullahi Yari Germany	4	250 1.0×	35 0.5×	42 0.7×	71 2.0×	27 0.8×	7	331
El-Sayed Atlam Saudi Arabia	9	153 0.6×	32 0.5×	12 0.2×	38 1.1×	24 0.7×	24	288
Ambeshwar Kumar India	7	92 0.4×	41 0.6×	18 0.3×	17 0.5×	80 2.4×	14	213
Oscar Li United States	5	304 1.3×	35 0.5×	34 0.5×	13 0.4×	101 3.1×	14	383
Mohamed Elaraby Egypt	9	180 0.7×	40 0.6×	7 0.1×	34 1.0×	45 1.4×	17	306

Countries citing papers authored by Théo Ryffel

Since Specialization

Citations

This map shows the geographic impact of Théo Ryffel'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 Théo Ryffel with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Théo Ryffel more than expected).

Fields of papers citing papers by Théo Ryffel

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Théo Ryffel. 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 Théo Ryffel. The network helps show where Théo Ryffel may publish in the future.

Co-authorship network of co-authors of Théo Ryffel

This figure shows the co-authorship network connecting the top 25 collaborators of Théo Ryffel. A scholar is included among the top collaborators of Théo Ryffel 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 Théo Ryffel. Théo Ryffel is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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7 of 7 papers shown

1.

Ryffel, Théo, et al.. (2025). Federated Analysis With Differential Privacy in Oncology Research: Longitudinal Observational Study Across Hospital Data Warehouses. JMIR Medical Informatics. 13. e59685–e59685.

2.

Clergerie, Éric Villemonte de la, et al.. (2023). Large Language Models as Instructors: A Study on Multilingual Clinical Entity Extraction. SPIRE - Sciences Po Institutional REpository. 178–190. 6 indexed citations

3.

Ryffel, Théo, et al.. (2023). Annotate French Clinical Data Using Large Language Model Predictions. 550–557. 1 indexed citations

4.

Ryffel, Théo, et al.. (2022). AriaNN: Low-Interaction Privacy-Preserving Deep Learning via Function Secret Sharing. SHILAP Revista de lepidopterología. 38 indexed citations

5.

Kaissis, Georgios, Alexander Ziller, Jonathan Passerat‐Palmbach, et al.. (2021). End-to-end privacy preserving deep learning on multi-institutional medical imaging. Nature Machine Intelligence. 3(6). 473–484. 255 indexed citations breakdown →

6.

Ryffel, Théo, et al.. (2021). AriaNN: Low-Interaction Privacy-Preserving Deep Learning via Function Secret Sharing. Proceedings on Privacy Enhancing Technologies. 2022(1). 291–316. 6 indexed citations

7.

Ryffel, Théo, et al.. (2019). Partially Encrypted Deep Learning using Functional Encryption. Neural Information Processing Systems. 32. 4517–4528. 15 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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