Uma Ramamurthy

1.9k citations
27 papers · 487 · h-index 12

Impact in

Papers in

Uma Ramamurthy

26 papers receiving 466 citations

Peers

Uma Ramamurthy
Comparison fields: 5 of 80
  • Genetics 96
  • Obstetrics and Gynecology 38
  • Cognitive Neuroscience 98
  • Hematology 49
  • Artificial Intelligence 119
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Ying Hu China
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Jiaqi Cui China
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Citations per field
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Citations per year

Countries citing papers authored by Uma Ramamurthy

Since Specialization
Citations

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

Fields of papers citing papers by Uma Ramamurthy

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 27 papers — load more, or switch the sort, to bring in the rest.

#Work
1 201390
2
The Role of Consciousness in Memory
200560
3 201456
4 201854
5 200637
6 201834
7
LIDA: A Computational Model of Global Workspace Theory and Developmental Learning.
200732
8
The Neuroimaging Center of the Pediatric Brain Tumor Consortium-collaborative neuroimaging in pediatric brain tumor research: a work in progress.
200718
9 200816
10 200514
11
Consciousness and conceptual learning in a socially situated agent
200013
12
Motivations, Values and Emotions: 3 sides of the same coin
200611
13
A Cognitive Science Based Machine Learning Architecture
20068
14
Encoding and Retrieval Efficiency of Episodic Data in a Modified Sparse Distributed Memory System
20058
15
Realizing Forgetting in a Modified Sparse Distributed Memory System
20066
16 20125
17 20095
18 20095
19 20125
20 20093

About Uma Ramamurthy

Uma Ramamurthy is a scholar working on Artificial Intelligence, Cognitive Neuroscience, Genetics, Pediatrics, Perinatology and Child Health and Molecular Biology, having authored 27 papers that have together received 487 indexed citations. Recurring topics across this work include AI-based Problem Solving and Planning (6 papers), Reinforcement Learning in Robotics (3 papers), Embodied and Extended Cognition (3 papers), Cancer Genomics and Diagnostics (2 papers), BRCA gene mutations in cancer (2 papers), Blood groups and transfusion (2 papers), Computability, Logic, AI Algorithms (2 papers) and Neuroblastoma Research and Treatments (2 papers). The work is most often cited by research in Genetics (96 citations), Obstetrics and Gynecology (38 citations), Cognitive Neuroscience (98 citations), Hematology (49 citations) and Artificial Intelligence (119 citations). Uma Ramamurthy has collaborated with scholars based in United States, Angola and Brazil. Frequent co-authors include Stan Franklin, Bernard J. Baars, Sidney K. D’Mello, Matthew Ventura, Russell E. Ware, Brígida Santos, Patrick T. McGann, Luís Bernardino, Wesley Lee and Medha Naik. Their work appears in journals such as Journal of Clinical Oncology, Genome Medicine, Blood, American Journal of Obstetrics and Gynecology and Annals of Surgery.

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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