Ronak Pradeep

1.2k citations
18 papers · 482 · h-index 9

Impact in

Papers in

Journals
Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval (2 papers)Text REtrieval Conference (1 paper)
Partner nations
CanadaUnited StatesItaly

In The Last Decade

Ronak Pradeep

15 papers receiving 457 citations

Peers

Ronak Pradeep
Comparison fields: 5 of 44
  • Artificial Intelligence 423
  • Computer Vision and Pattern Recognition 122
  • Information Systems 124
  • Health Informatics 6
  • Information Systems and Management 17
Replace Xueguang Ma with:
Xueguang Ma Canada
Timo Schick Germany
Jheng-Hong Yang Canada
Wafaa S. El-Kassas Egypt
Todor Mihaylov Germany
Ruidan He Singapore
Leyang Cui China
Tiziano Fagni Italy
Honglei Guo China
Yi Luan United States
Ronak Pradeep relative to Xueguang Ma Canada Xueguang Ma's profile →
Citations per field
00.5×5.5×
Xueguang Ma · 1×
Citations per year

Countries citing papers authored by Ronak Pradeep

Since Specialization
Citations

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

Fields of papers citing papers by Ronak Pradeep

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside Ronak Pradeep, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Ronak Pradeep Line = papers co-authored together Ronak Pradeep links everyone, so they are left out of the graph.

All Works

18 of 18 papers shown
#Work
1 2021179
2 2020175
3 202030
4 202126
5 202116
6 202414
7 202310
8 202210
9 20228
10
H2oloo at TREC 2020: When all you got is a hammer... Deep Learning, Health Misinformation, and Precision Medicine.
20204
11 20243
12 20242
13 20212
14 20212
15 20211
16 20250
17 20250
18 20250

About Ronak Pradeep

Ronak Pradeep is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition, Signal Processing, Molecular Biology and Sociology and Political Science, having authored 18 papers that have together received 482 indexed citations. Recurring topics across this work include Topic Modeling (14 papers), Natural Language Processing Techniques (10 papers), Advanced Text Analysis Techniques (4 papers), Machine Learning in Healthcare (3 papers), Multimodal Machine Learning Applications (2 papers), Semantic Web and Ontologies (1 paper), Time Series Analysis and Forecasting (1 paper) and Digital Imaging for Blood Diseases (1 paper). The work is most often cited by research in Artificial Intelligence (423 citations), Computer Vision and Pattern Recognition (122 citations), Information Systems (124 citations), Health Informatics (6 citations) and Information Systems and Management (17 citations). Ronak Pradeep has collaborated with scholars based in Canada, United States and Italy. Frequent co-authors include Jimmy Lin, Rodrigo Nogueira, Zhiying Jiang, Xueguang Ma, Sheng-Chieh Lin, Jheng-Hong Yang, Hui Fang, Raphael Tang, Kyunghyun Cho and Edwin Zhang. Their work appears in journals such as Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval and Text REtrieval Conference.

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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