Gopal Sarma

14 papers receiving 259 citations

Gopal Sarma's Hit Papers

ECG-Based Deep Learning and Clinical Risk Factors to Predict Atrial Fibrillation 2021 · 167 citations
1670+1+3Years since publication50100150

Peers

Gopal Sarma
Comparison fields: 5 of 73
  • Aging 21
  • Health Informatics 12
  • Cardiology and Cardiovascular Medicine 138
  • Health Information Management 11
  • Cognitive Neuroscience 43
Replace Aviv A. Rosenberg with:
Aviv A. Rosenberg Israel
Zhijian Yang United States
Carolyn J. Park United States
Ioanna Skampardoni United States
Edward S.C. Shih Taiwan
Hidde Bleijendaal Netherlands
Agnieszka Kitlas Golińska Poland
Marcella C. Woods United States
Lucas Plagwitz Germany
Gopal Sarma relative to Aviv A. Rosenberg Israel Aviv A. Rosenberg's profile →
Citations per field
00.5×1.5×2.2×
Aviv A. Rosenberg · 1×
Citations per year

Countries citing papers authored by Gopal Sarma

Since Specialization
Citations

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

Fields of papers citing papers by Gopal Sarma

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

17 of 17 papers shown
#Work
1
ECG-Based Deep Learning and Clinical Risk Factors to Predict Atrial Fibrillation
Hit paper breakdown →
2021167
2 201846
3 200810
4 20078
5 20176
6 20136
7 20135
8 20244
9 20173
10 20173
11 20173
12 20202
13 20172
14 20212
15 20181
16 20210
17 20150

About Gopal Sarma

Gopal Sarma is a scholar working on Artificial Intelligence, Cardiology and Cardiovascular Medicine, Information Systems, Atomic and Molecular Physics, and Optics and Cognitive Neuroscience, having authored 17 papers that have together received 268 indexed citations. Recurring topics across this work include Quantum Information and Cryptography (3 papers), Psychology of Moral and Emotional Judgment (2 papers), AI-based Problem Solving and Planning (2 papers), Quantum optics and atomic interactions (2 papers), Neuroethics, Human Enhancement, Biomedical Innovations (2 papers), Software Engineering Research (2 papers), Optical Network Technologies (2 papers) and Adversarial Robustness in Machine Learning (2 papers). The work is most often cited by research in Aging (21 citations), Health Informatics (12 citations), Cardiology and Cardiovascular Medicine (138 citations), Health Information Management (11 citations) and Cognitive Neuroscience (43 citations). Gopal Sarma has collaborated with scholars based in United States, Russia and Iran. Frequent co-authors include Christopher Reeder, Steven A. Lubitz, Anthony Philippakis, Christopher D. Anderson, Pulkit Singh, Sam Friedman, Paolo Di Achille, Patrick T. Ellinor, Jennifer E. Ho and Andrea S. Foulkes. Their work appears in journals such as Circulation, Physical Review A, Philosophical Transactions of the Royal Society B Biological Sciences, Nature Medicine and IEEE photonics journal.

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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