Daniel Suo

671 citations
2 papers · 405 · 1 hit paper · h-index 2

Impact in

Papers in

Journals
Proceedings - IEEE International Conference on Robotics and Automation/Proceedings (1 paper)PubMed (1 paper)
Partner nations
United States

In The Last Decade

Daniel Suo

2 papers receiving 393 citations

Daniel Suo's Hit Papers

Multi-view self-supervised deep learning for 6D pose estimation in the Amazon Picking Challenge 2017 · 372 citations
3720+3+6Years since publication100200300

Peers

Daniel Suo
Comparison fields: 5 of 38
  • Control and Systems Engineering 266
  • Computer Vision and Pattern Recognition 132
  • Computational Mechanics 50
  • Cognitive Neuroscience 28
  • Aerospace Engineering 27
Replace Zhixing Xue with:
Zhixing Xue Germany
Thomas Mörwald Austria
Ferenc Bálint-Benczédi Germany
Wenzhao Lian United States
Nan Zhou China
Manuel Wüthrich United States
Yuming Du France
Romain Brégier South Korea
Connor Schenck United States
Anirban Roy United States
Daniel Suo relative to Zhixing Xue Germany Zhixing Xue's profile →
Citations per field
00.5×2×3.3×
Zhixing Xue · 1×
Citations per year

Countries citing papers authored by Daniel Suo

Since Specialization
Citations

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

Fields of papers citing papers by Daniel Suo

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 5 scholars most cited alongside Daniel Suo, 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 Daniel Suo Line = papers co-authored together Daniel Suo links everyone, so they are left out of the graph.

All Works

2 of 2 papers shown
#Work
1
Multi-view self-supervised deep learning for 6D pose estimation in the Amazon Picking Challenge
Hit paper breakdown →
2017372
2 202233

About Daniel Suo

Daniel Suo is a scholar working on Cognitive Neuroscience, Computer Vision and Pattern Recognition, Control and Systems Engineering, Nuclear Energy and Engineering and Waste Management and Disposal, having authored 2 papers that have together received 405 indexed citations. Recurring topics across this work include Functional Brain Connectivity Studies (1 paper), Robot Manipulation and Learning (1 paper) and Advanced Neural Network Applications (1 paper). The work is most often cited by research in Control and Systems Engineering (266 citations), Computer Vision and Pattern Recognition (132 citations), Computational Mechanics (50 citations), Cognitive Neuroscience (28 citations) and Aerospace Engineering (27 citations). Daniel Suo has collaborated with scholars based in United States. Frequent co-authors include Alberto Rodríguez, Shuran Song, Kuan‐Ting Yu, Jianxiong Xiao and Andy Zeng. Their work appears in journals such as Proceedings - IEEE International Conference on Robotics and Automation/Proceedings and PubMed.

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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