Archit Sharma

33 papers receiving 238 citations

Peers

Archit Sharma
Comparison fields: 5 of 61
  • Critical Care and Intensive Care Medicine 32
  • Health Informatics 6
  • Hepatology 26
  • Cardiology and Cardiovascular Medicine 46
  • Artificial Intelligence 56
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Theodore W. Cary United States
Chiara de Sio Italy
Nathan Scales Canada
Qianjun Jia China
Gary Brahm Canada
Sarah Leclerc France
Matthias Hüser Switzerland
Adnan Hussain United States
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Citations per field
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Citations per year

Countries citing papers authored by Archit Sharma

Since Specialization
Citations

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

Fields of papers citing papers by Archit Sharma

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 40 papers — load more, or switch the sort, to bring in the rest.

#Work
1 201546
2 202345
3 202124
4 202423
5 202415
6 202011
7 202110
8 202110
9 20239
10 20197
11 20247
12 20193
13
Dynamics-Aware Unsupervised Skill Discovery
20203
14 20233
15
Variational Empowerment as Representation Learning for Goal-Conditioned Reinforcement Learning
20212
16 20192
17 20192
18 20232
19 20182
20 20232

About Archit Sharma

Archit Sharma is a scholar working on Cardiology and Cardiovascular Medicine, Surgery, Artificial Intelligence, Critical Care and Intensive Care Medicine and Pulmonary and Respiratory Medicine, having authored 40 papers that have together received 244 indexed citations. Recurring topics across this work include Cardiac, Anesthesia and Surgical Outcomes (10 papers), Cardiac Valve Diseases and Treatments (9 papers), Cardiac and Coronary Surgery Techniques (4 papers), Reinforcement Learning in Robotics (3 papers), Infective Endocarditis Diagnosis and Management (3 papers), Ultrasound in Clinical Applications (3 papers), Robot Manipulation and Learning (3 papers) and Respiratory Support and Mechanisms (2 papers). The work is most often cited by research in Critical Care and Intensive Care Medicine (32 citations), Health Informatics (6 citations), Hepatology (26 citations), Cardiology and Cardiovascular Medicine (46 citations) and Artificial Intelligence (56 citations). Archit Sharma has collaborated with scholars based in United States, India and Ireland. Frequent co-authors include Sasha K. Shillcutt, Nicholas W. Markin, Wendy Grant, Chelsea Finn, Rafael Rafailov, Eric Mitchell, Christopher D. Manning, Huaxiu Yao, Sergey Levine and Sudhakar Subramani. Their work appears in journals such as Journal of Cardiothoracic and Vascular Anesthesia, Journal of Clinical Anesthesia, Regional Anesthesia & Pain Medicine, Anesthesiology Clinics and Machine Learning.

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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