Andrew Trask
Impact in
- Health Informatics top 2%
- Artificial Intelligence in Healthcare and Education
- Artificial Intelligence top 5%
- Privacy-Preserving Technologies in Data
- Cryptography and Data Security
- Adversarial Robustness in Machine Learning
- AI in cancer detection
- Natural Language Processing Techniques
Papers in
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- Privacy-Preserving Technologies in Data 2
- Natural Language Processing Techniques 1
- Cryptography and Data Security 1
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- Software Engineering Research 1
- Co-authors
- Dmitrii Usynin (1 shared paper)Alexander Ziller (1 shared paper)Jonathan Passerat‐Palmbach (1 shared paper)Andreas Saleh (1 shared paper)Théo Ryffel (1 shared paper)Friederike Jungmann (1 shared paper)Georgios Kaissis (1 shared paper)Daniel Rueckert (1 shared paper)
- Journals
- Nature Machine Intelligence (1 paper)arXiv (Cornell University) (3 papers)
- Partner nations
- United KingdomUnited StatesCanada
In The Last Decade
Andrew Trask
6 papers receiving 360 citations
Andrew Trask's Hit Papers
Peers
Comparison fields: 5 of 75
- Health Informatics 57
- Artificial Intelligence 262
- Radiology, Nuclear Medicine and Imaging 65
- Health Information Management 9
- Computer Science Applications 11
Countries citing papers authored by Andrew Trask
This map shows the geographic impact of Andrew Trask'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 Andrew Trask with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Andrew Trask more than expected).
Fields of papers citing papers by Andrew Trask
This network shows the impact of papers produced by Andrew Trask. 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 Andrew Trask. The network helps show where Andrew Trask may publish in the future.
Co-authors
The 25 scholars most cited alongside Andrew Trask, 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 | End-to-end privacy preserving deep learning on multi-institutional medical imaging Hit paper breakdown → | 2021 | 270 |
| 2 | 2020 | 36 | |
| 3 | 2018 | 27 | |
| 4 | 2015 | 18 | |
| 5 | Sample-efficient adaptive text-to-speech | 2018 | 16 |
| 6 | Neuronale Netze und Deep Learning kapieren | 2019 | 1 |
About Andrew Trask
Andrew Trask is a scholar working on Artificial Intelligence, Information Systems, Statistical and Nonlinear Physics, Health Informatics and Signal Processing, having authored 6 papers that have together received 368 indexed citations. Recurring topics across this work include Privacy-Preserving Technologies in Data (2 papers), Artificial Intelligence in Healthcare and Education (1 paper), Numerical Methods and Algorithms (1 paper), Natural Language Processing Techniques (1 paper), Software Engineering Research (1 paper), Cryptography and Data Security (1 paper), Mobile Crowdsensing and Crowdsourcing (1 paper) and Model Reduction and Neural Networks (1 paper). The work is most often cited by research in Health Informatics (57 citations), Artificial Intelligence (262 citations), Radiology, Nuclear Medicine and Imaging (65 citations), Health Information Management (9 citations) and Computer Science Applications (11 citations). Andrew Trask has collaborated with scholars based in United Kingdom, United States and Canada. Frequent co-authors include Dmitrii Usynin, Alexander Ziller, Jonathan Passerat‐Palmbach, Andreas Saleh, Théo Ryffel, Friederike Jungmann, Georgios Kaissis, Daniel Rueckert, Jason Mancuso and Rickmer Braren. Their work appears in journals such as Nature Machine Intelligence and arXiv (Cornell University).
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.