Yani Ioannou

1.1k total citations
10 papers, 223 citations indexed

About

Yani Ioannou is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and General Health Professions. According to data from OpenAlex, Yani Ioannou has authored 10 papers receiving a total of 223 indexed citations (citations by other indexed papers that have themselves been cited), including 7 papers in Computer Vision and Pattern Recognition, 5 papers in Artificial Intelligence and 1 paper in General Health Professions. Recurrent topics in Yani Ioannou's work include Advanced Neural Network Applications (5 papers), Adversarial Robustness in Machine Learning (3 papers) and Medical Image Segmentation Techniques (2 papers). Yani Ioannou is often cited by papers focused on Advanced Neural Network Applications (5 papers), Adversarial Robustness in Machine Learning (3 papers) and Medical Image Segmentation Techniques (2 papers). Yani Ioannou collaborates with scholars based in United Kingdom, United States and Canada. Yani Ioannou's co-authors include Antonio Criminisi, Darko Zikic, Matthew A. Brown, Duncan Robertson, Roberto Cipolla, Cem Keskin, Yann Dauphin, Utku Evci, Jamie Shotton and Megan Ansdell and has published in prestigious journals such as Child Care Health and Development, Canadian Journal on Aging / La Revue canadienne du vieillissement and International Conference on Learning Representations.

In The Last Decade

Yani Ioannou

9 papers receiving 211 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Yani Ioannou United Kingdom	6	155	138	53	40	10	10	223
Chandrakanta Mahanty India	7	81 0.5×	102 0.7×	107 2.0×	101 2.5×	15 1.5×	21	250
Varghese Alex India	3	63 0.4×	35 0.3×	68 1.3×	64 1.6×	8 0.8×	3	133
Junyan Lyu China	8	73 0.5×	20 0.1×	51 1.0×	132 3.3×	21 2.1×	23	205
Xiaoman Zhang China	7	49 0.3×	11 0.1×	174 3.3×	88 2.2×	12 1.2×	19	319
A. Chabouis France	7	291 1.9×	34 0.2×	62 1.2×	693 17.3×	5 0.5×	12	751
P. Saranya India	9	102 0.7×	27 0.2×	25 0.5×	178 4.5×	4 0.4×	18	229
Yunlu Yan China	4	82 0.5×	25 0.2×	60 1.1×	114 2.9×	51 5.1×	6	213
Tianyuan Yao United States	6	47 0.3×	9 0.1×	48 0.9×	48 1.2×	7 0.7×	29	118
Chi Mai Luong Vietnam	6	37 0.2×	16 0.1×	70 1.3×	99 2.5×	12 1.2×	16	187

Countries citing papers authored by Yani Ioannou

Since Specialization

Citations

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

Fields of papers citing papers by Yani Ioannou

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Yani Ioannou

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

All Works

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10 of 10 papers shown

Evci, Utku, Yani Ioannou, Cem Keskin, & Yann Dauphin. (2022). Gradient Flow in Sparse Neural Networks and How Lottery Tickets Win. Proceedings of the AAAI Conference on Artificial Intelligence. 36(6). 6577–6586. 12 indexed citations

Osborn, H. P., Megan Ansdell, Yani Ioannou, et al.. (2020). Rapid classification of TESS planet candidates with convolutional neural networks. Springer Link (Chiba Institute of Technology). 11 indexed citations

Hillier, Loretta M., et al.. (2018). Automated Fall Detection Technology in Inpatient Geriatric Psychiatry: Nurses’ Perceptions and Lessons Learned. Canadian Journal on Aging / La Revue canadienne du vieillissement. 37(3). 245–260. 12 indexed citations

Ioannou, Yani, Duncan Robertson, Roberto Cipolla, & Antonio Criminisi. (2017). Deep Roots: Improving CNN Efficiency by Learning a Basis for Filter Dependencies. Computer Vision and Pattern Recognition.

Ioannou, Yani. (2016). Training CNNs with Low-Rank Filters for Efficient Image Classification: ICLR 2016 Poster. Zenodo (CERN European Organization for Nuclear Research). 1 indexed citations

Ioannou, Yani, Duncan Robertson, Jamie Shotton, et al.. (2016). Training Convolutional Neural Networks with Low-rank Filters for Efficient Image Classification. International Conference on Learning Representations. 4 indexed citations

Ioannou, Yani, Duncan Robertson, Darko Zikic, et al.. (2016). Decision Forests, Convolutional Networks and the Models in-Between Microsoft Research Technical Report 2015-58. 1 indexed citations

Robertson, Duncan, et al.. (2016). Refining Architectures of Deep Convolutional Neural Networks. 2212–2220. 17 indexed citations

Zikic, Darko, Yani Ioannou, Antonio Criminisi, & Matthew A. Brown. (2014). Segmentation of Brain Tumor Tissues with Convolutional Neural Networks. 157 indexed citations

10.

González-Izquierdo, Arturo, et al.. (2014). Notifications for child safeguarding from an acute hospital in response to presentations to healthcare by parents. Child Care Health and Development. 41(2). 186–193. 8 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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