Benjamin Shih

2.5k citations
18 papers · 1.7k indexed · 2 hit papers · h-index 13

Benjamin Shih

17 papers receiving 1.7k citations

Hit Papers

Electronic skins and machine learning for intelligent sof...5342019202620212023100200300400500

Peers

Benjamin Shih
Comparison fields: 5 of 94
  • Biomedical Engineering 1.4k
  • Orthodontics 96
  • Condensed Matter Physics 238
  • Cognitive Neuroscience 380
  • General Dentistry 31
Replace Guo Liang Goh with:
Guo Liang Goh Singapore
Zeang Zhao China
Amir Hosein Sakhaei United Kingdom
Veronica J. Santos United States
Thomas J. Wallin United States
Anil Bastola Singapore
Jiayi Yang China
Zicai Zhu China
Joseph T. Muth United States
Yiğit Mengüç United States
Benjamin Shih relative to Guo Liang Goh Singapore Guo Liang Goh's profile →
Citations per field
00.5×10.3×
Guo Liang Goh · 1×
Citations per year

Countries citing papers authored by Benjamin Shih

Since Specialization
Citations

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

Fields of papers citing papers by Benjamin Shih

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network

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

All Works

18 of 18 papers shown
#Work
1 202381
2 20223
3 202183
4 2021115
5
Electronic skins and machine learning for intelligent soft robotsbreakdown →
2020534
6 20204
7 201981
8
Soft robot perception using embedded soft sensors and recurrent neural networksbreakdown →
2019507
9 201950
10 20180
11 201812
12 201762
13 201773
14 201750
15 20151
16 201426
17 20112
18
Unsupervised Discovery of Student Strategies.
201018

About Benjamin Shih

Benjamin Shih is a scholar working on Condensed Matter Physics, Biomedical Engineering, Cognitive Neuroscience, Orthodontics and Computer Science Applications, having authored 18 papers that have together received 1.7k indexed citations. Recurring topics across this work include Soft Robotics and Applications (11 papers), Advanced Sensor and Energy Harvesting Materials (10 papers), Tactile and Sensory Interactions (5 papers), Micro and Nano Robotics (4 papers), Robot Manipulation and Learning (2 papers), Robotic Locomotion and Control (2 papers), AI-based Problem Solving and Planning (2 papers) and Intelligent Tutoring Systems and Adaptive Learning (2 papers). The work is most often cited by research in Biomedical Engineering (1.4k citations), Orthodontics (96 citations), Condensed Matter Physics (238 citations), Cognitive Neuroscience (380 citations) and General Dentistry (31 citations). Benjamin Shih has collaborated with scholars based in United States, South Korea and Australia. Frequent co-authors include Michael T. Tolley, Thomas George Thuruthel, Cecilia Laschi, Yong‐Lae Park, Rebecca Kramer‐Bottiglio, Fumiya Iida, Zhenan Bao, Jinxing Li, Dylan Shah and Dylan Drotman. Their work appears in journals such as Science Robotics, Soft Robotics, IEEE Robotics and Automation Letters, Advanced Materials and Dental Materials.

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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