Harsh Agarwal

747 citations
5 papers · 464 indexed · 1 hit paper · h-index 3
Topics
Software-Defined Networks and 5G (2 papers)Recommender Systems and Techniques (1 paper)Caching and Content Delivery (1 paper)
Journals
Journal of the American College of CardiologyAdelaide Research & Scholarship (AR&S) (University of Adelaide)
Partner nations
IndiaAustralia

In The Last Decade

Harsh Agarwal

4 papers receiving 451 citations

Hit Papers

Unsupervised Learning of Monocular Depth Estimation and V...20182026202020232018100200300400

Peers

Harsh Agarwal
Comparison fields: 5 of 36
  • Computer Vision and Pattern Recognition 403
  • Aerospace Engineering 235
  • Media Technology 153
  • Computer Networks and Communications 35
  • Electrical and Electronic Engineering 30
Replace Shuling Wang with:
Shuling Wang China
Mu Hu China
Yevhen Kuznietsov Switzerland
Chamara Saroj Weerasekera Australia
Lukas von Stumberg Germany
Huangying Zhan Australia
Erik Ringaby Sweden
Zhong-Dan Lan France
In-So Kweon South Korea
Harsh Agarwal relative to Shuling Wang China Shuling Wang's profile →
Citations per field
00.5×20×40×64×
Shuling Wang · 1×
Citations per year

Countries citing papers authored by Harsh Agarwal

Since Specialization
Citations

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

Fields of papers citing papers by Harsh Agarwal

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Harsh Agarwal

This figure shows the co-authorship network connecting the top 25 collaborators of Harsh Agarwal. A scholar is included among the top collaborators of Harsh Agarwal based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Harsh Agarwal. Harsh Agarwal is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

5 of 5 papers shown
#WorkIndexed citations
1 0
2 2
3 21
4 17
5
Unsupervised Learning of Monocular Depth Estimation and Visual Odometry with Deep Feature Reconstructionbreakdown →
424

About Harsh Agarwal

Harsh Agarwal is a scholar working on Media Technology, Computer Vision and Pattern Recognition and Computer Networks and Communications, having authored 5 papers that have together received 464 indexed citations. Recurring topics across this work include Software-Defined Networks and 5G (2 papers), Recommender Systems and Techniques (1 paper) and Caching and Content Delivery (1 paper). The work is most often cited by research in Computer Vision and Pattern Recognition (403 citations), Media Technology (153 citations) and Aerospace Engineering (235 citations). Harsh Agarwal has collaborated with scholars based in India and Australia. Frequent co-authors include Ravi Garg, Ian Reid, Chamara Saroj Weerasekera, Kejie Li, Huangying Zhan, Bheemarjuna Reddy Tamma, A. Antony Franklin and Vuyisile T. Nkomo. Their work appears in journals such as Journal of the American College of Cardiology and Adelaide Research & Scholarship (AR&S) (University of Adelaide).

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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