Julia Ive

473 total citations
28 papers, 235 citations indexed

About

Julia Ive is a scholar working on Artificial Intelligence, Molecular Biology and Health Informatics. According to data from OpenAlex, Julia Ive has authored 28 papers receiving a total of 235 indexed citations (citations by other indexed papers that have themselves been cited), including 25 papers in Artificial Intelligence, 8 papers in Molecular Biology and 3 papers in Health Informatics. Recurrent topics in Julia Ive's work include Topic Modeling (16 papers), Natural Language Processing Techniques (10 papers) and Machine Learning in Healthcare (9 papers). Julia Ive is often cited by papers focused on Topic Modeling (16 papers), Natural Language Processing Techniques (10 papers) and Machine Learning in Healthcare (9 papers). Julia Ive collaborates with scholars based in United Kingdom, Sweden and France. Julia Ive's co-authors include Lucia Specia, Sumithra Velupillai, Pranava Madhyastha, George Gkotsis, Rina Dutta, Robert C. Stewart, Stephen Puntis, Angus Roberts, Robert Stewart and Frédéric Blain and has published in prestigious journals such as Experimental Biology and Medicine, Computational Linguistics and npj Digital Medicine.

In The Last Decade

Julia Ive

25 papers receiving 221 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Julia Ive United Kingdom	6	186	53	53	28	25	28	235
Luca Soldaini United States	8	152 0.8×	39 0.7×	18 0.3×	35 1.3×	23 0.9×	34	206
Dongfang Xu United States	9	158 0.8×	54 1.0×	7 0.1×	90 3.2×	41 1.6×	19	225
Jamin Shin Hong Kong	7	188 1.0×	35 0.7×	33 0.6×	5 0.2×	13 0.5×	14	240
Farhad Bin Siddique Hong Kong	7	146 0.8×	23 0.4×	31 0.6×	10 0.4×	5 0.2×	9	180
Shen Yan United States	7	134 0.7×	11 0.2×	26 0.5×	3 0.1×	10 0.4×	9	225
Ivan Smirnov Russia	8	109 0.6×	59 1.1×	6 0.1×	5 0.2×	32 1.3×	44	192
Chengfeng Mao United States	3	291 1.6×	20 0.4×	102 1.9×	7 0.3×	3 0.1×	4	367
Zeqian Ju China	5	111 0.6×	7 0.1×	53 1.0×	21 0.8×	5 0.2×	7	172
James Ryan United States	7	113 0.6×	7 0.1×	9 0.2×	48 1.7×	3 0.1×	21	166
Luis Chiruzzo Uruguay	9	233 1.3×	81 1.5×	56 1.1×	7 0.3×		46	296

Countries citing papers authored by Julia Ive

Since Specialization

Citations

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

Fields of papers citing papers by Julia Ive

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Julia Ive

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

All Works

Sort: Min cites: Since: Top N: Style:

20 of 20 papers shown

Horsfall, Laura, et al.. (2025). Clinical Algorithms and the Legacy of Race-Based Correction: Historical Errors, Contemporary Revisions and Equity-Oriented Methodologies for Epidemiologists. Clinical Epidemiology. Volume 17. 647–662. 1 indexed citations

Ive, Julia, et al.. (2025). AI assisted prediction of unplanned intensive care admissions using natural language processing in elective neurosurgery. npj Digital Medicine. 8(1). 549–549.

Ive, Julia, et al.. (2024). Modeling disagreement in automatic data labeling for semi-supervised learning in Clinical Natural Language Processing. Frontiers in Artificial Intelligence. 7. 1374162–1374162.

Song, Jiayu, et al.. (2024). Combining Hierachical VAEs with LLMs for clinically meaningful timeline summarisation in social media. Queen Mary Research Online (Queen Mary University of London). 14651–14672. 2 indexed citations

Ive, Julia, et al.. (2024). MoralBERT: A Fine-Tuned Language Model for Capturing Moral Values in Social Discussions. 433–442. 4 indexed citations

Ive, Julia, et al.. (2024). Evaluating Synthetic Data Generation from User Generated Text. Computational Linguistics. 51(1). 191–233. 1 indexed citations

Ive, Julia, et al.. (2023). Embracing the uncertainty in human–machine collaboration to support clinical decision-making for mental health conditions. Frontiers in Digital Health. 5. 1188338–1188338. 8 indexed citations

Ive, Julia, et al.. (2022). Clinical utility of automatic phenotype annotation in unstructured clinical notes: intensive care unit use. BMJ Health & Care Informatics. 29(1). e100519–e100519. 4 indexed citations

Ive, Julia. (2022). Leveraging the potential of synthetic text for AI in mental healthcare. Frontiers in Digital Health. 4. 1010202–1010202. 4 indexed citations

10.

Ive, Julia, et al.. (2022). SURF: Semantic-level Unsupervised Reward Function for Machine Translation. Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 4508–4522. 1 indexed citations

11.

Zhang, Jingqing, et al.. (2021). Self-Supervised Detection of Contextual Synonyms in a Multi-Class Setting: Phenotype Annotation Use Case. Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing. 8754–8769. 5 indexed citations

12.

Ive, Julia, et al.. (2020). A Post-Editing Dataset in the Legal Domain: Do we Underestimate Neural Machine Translation Quality?. Language Resources and Evaluation. 3692–3697. 3 indexed citations

13.

Ive, Julia, et al.. (2020). Exploring Transformer Text Generation for Medical Dataset Augmentation.. Language Resources and Evaluation. 4699–4708. 21 indexed citations

14.

Ive, Julia, Stephen Puntis, Rudolf N. Cardinal, et al.. (2020). Generation and evaluation of artificial mental health records for Natural Language Processing. npj Digital Medicine. 3(1). 69–69. 47 indexed citations

15.

Ive, Julia, Pranava Madhyastha, & Lucia Specia. (2019). Distilling Translations with Visual Awareness. 6525–6538. 51 indexed citations

16.

Ive, Julia, et al.. (2019). Transformer-based Cascaded Multimodal Speech Translation. Zenodo (CERN European Organization for Nuclear Research). 1 indexed citations

17.

Ive, Julia, Pranava Madhyastha, & Lucia Specia. (2019). Deep Copycat Networks for Text-to-Text Generation. 3225–3234. 3 indexed citations

18.

Ive, Julia, Frédéric Blain, & Lucia Specia. (2018). deepQuest: A Framework for Neural-based Quality Estimation. White Rose Research Online (University of Leeds, The University of Sheffield, University of York). 3146–3157. 15 indexed citations

19.

Ive, Julia, Aurélien Max, & François Yvon. (2018). Reassessing the proper place of man and machine in translation: a pre-translation scenario. Machine Translation. 32(4). 279–308. 5 indexed citations

20.

Ive, Julia, Carolina Scarton, Frédéric Blain, & Lucia Specia. (2018). Sheffield Submissions for the WMT18 Quality Estimation Shared Task. Wolverhampton Intellectual Repository and E-Theses (University of Wolverhampton). 794–800. 1 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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