Pablo Rivas

2.7k citations
127 papers · 1.8k · h-index 21

Impact in

Papers in

Pablo Rivas

116 papers receiving 1.7k citations

Peers

Pablo Rivas
Comparison fields: 5 of 163
  • Health Informatics 75
  • Hepatology 174
  • Virology 98
  • Infectious Diseases 346
  • Emergency Medicine 135
Replace Dominik Heider with:
Dominik Heider Germany
Hui Chen China
Mohammad M. Ghassemi United States
Hongjun Li China
Ramy Arnaout United States
Eva K. Lee United States
Francesco Gargiulo Italy
Hongyu Miao United States
Carl Taswell United States
Richard J. Maude Thailand
Pablo Rivas relative to Dominik Heider Germany Dominik Heider's profile →
Citations per field
00.5×11.3×
Dominik Heider · 1×
Citations per year

Countries citing papers authored by Pablo Rivas

Since Specialization
Citations

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

Fields of papers citing papers by Pablo Rivas

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside Pablo Rivas, 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 Pablo Rivas Line = papers co-authored together Pablo Rivas links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 127 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2009240
2 2006135
3 2023125
4 200698
5 200558
6 202356
7 200351
8 200441
9 201641
10 202439
11 201338
12 201733
13 201030
14 201230
15 201330
16 202228
17 201327
18 200622
19 200922
20
HIV and malaria.
200722

About Pablo Rivas

Pablo Rivas is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition, Signal Processing, Infectious Diseases and Computer Networks and Communications, having authored 127 papers that have together received 1.8k indexed citations. Recurring topics across this work include Adversarial Robustness in Machine Learning (12 papers), Quantum Information and Cryptography (11 papers), Quantum Computing Algorithms and Architecture (11 papers), Face and Expression Recognition (8 papers), Topic Modeling (8 papers), Ethics and Social Impacts of AI (8 papers), HIV/AIDS drug development and treatment (6 papers) and HIV Research and Treatment (6 papers). The work is most often cited by research in Health Informatics (75 citations), Hepatology (174 citations), Virology (98 citations), Infectious Diseases (346 citations) and Emergency Medicine (135 citations). Pablo Rivas has collaborated with scholars based in United States, Spain and Mexico. Frequent co-authors include Vincent Soriano, Pablo Barreiro, Luz Martín‐Carbonero, Miguel Górgolas, Sonia Rodríguez‐Nóvoa, Pablo Labarga, Javier Orduz, Ernesto Sifuentes, J. Medraño and Eugenia Vispo. Their work appears in journals such as AIDS Research and Human Retroviruses, International Journal of Machine Learning and Cybernetics, American Journal of Tropical Medicine and Hygiene, JAIDS Journal of Acquired Immune Deficiency Syndromes and HIV Clinical Trials.

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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