Benjamin Shih

2.5k total citations · 2 hit papers
18 papers, 1.7k citations indexed

About

Benjamin Shih is a scholar working on Biomedical Engineering, Cognitive Neuroscience and Condensed Matter Physics. According to data from OpenAlex, Benjamin Shih has authored 18 papers receiving a total of 1.7k indexed citations (citations by other indexed papers that have themselves been cited), including 14 papers in Biomedical Engineering, 6 papers in Cognitive Neuroscience and 4 papers in Condensed Matter Physics. Recurrent topics in Benjamin Shih's work include Soft Robotics and Applications (11 papers), Advanced Sensor and Energy Harvesting Materials (10 papers) and Tactile and Sensory Interactions (5 papers). Benjamin Shih is often cited by papers focused on Soft Robotics and Applications (11 papers), Advanced Sensor and Energy Harvesting Materials (10 papers) and Tactile and Sensory Interactions (5 papers). Benjamin Shih collaborates with scholars based in United States, South Korea and Australia. Benjamin Shih's co-authors include Michael T. Tolley, Thomas George Thuruthel, Cecilia Laschi, Yong‐Lae Park, Fumiya Iida, Jinxing Li, Rebecca Kramer‐Bottiglio, Zhenan Bao, Dylan Shah and Dylan Drotman and has published in prestigious journals such as Advanced Materials, Dental Materials and Science Robotics.

In The Last Decade

Benjamin Shih

17 papers receiving 1.7k citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

Electronic skins and machine learning for intelligent soft robots

2020 534 citations Benjamin Shih, Dylan Shah et al. Science Robotics profile →
Soft robot perception using embedded soft sensors and recurrent neural networks

2019 507 citations Thomas George Thuruthel, Benjamin Shih et al. Science Robotics profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Benjamin Shih United States	13	1.4k	476	380	293	238	18	1.7k
Bryan Peele United States	9	1.6k 1.2×	481 1.0×	418 1.1×	99 0.3×	177 0.7×	11	1.9k
Thomas J. Wallin United States	16	1.6k 1.1×	812 1.7×	91 0.2×	114 0.4×	334 1.4×	22	2.1k
Ja Choon Koo South Korea	30	2.2k 1.5×	1.1k 2.3×	382 1.0×	478 1.6×	127 0.5×	227	3.1k
Tess Hellebrekers United States	12	863 0.6×	286 0.6×	234 0.6×	121 0.4×	113 0.5×	18	1.0k
Perla Maiolino United Kingdom	18	996 0.7×	337 0.7×	583 1.5×	405 1.4×	59 0.2×	79	1.4k
Changhong Linghu China	17	924 0.6×	435 0.9×	130 0.3×	33 0.1×	65 0.3×	35	1.4k
Joseph T. Muth United States	8	1.7k 1.2×	400 0.8×	428 1.1×	47 0.2×	75 0.3×	9	2.2k
Veronica J. Santos United States	17	1.1k 0.8×	187 0.4×	617 1.6×	375 1.3×	40 0.2×	39	1.7k
Zicai Zhu China	25	1.8k 1.3×	444 0.9×	116 0.3×	36 0.1×	65 0.3×	108	2.0k
Amir Hosein Sakhaei United Kingdom	13	1.5k 1.0×	1.1k 2.2×	80 0.2×	61 0.2×	139 0.6×	29	2.4k

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 of co-authors of Benjamin Shih

This figure shows the co-authorship network connecting the top 25 collaborators of Benjamin Shih. A scholar is included among the top collaborators of Benjamin Shih 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 Benjamin Shih. Benjamin Shih is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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18 of 18 papers shown

Zhai, Yichen, Jiayao Yan, Benjamin Shih, et al.. (2023). Desktop fabrication of monolithic soft robotic devices with embedded fluidic control circuits. Science Robotics. 8(79). eadg3792–eadg3792. 81 indexed citations

Chen, Jun‐Da, et al.. (2022). Open-loop printing of liquid metal for the low-cost rapid fabrication of soft sensors. 889–895. 3 indexed citations

Jadhav, Saurabh, et al.. (2021). Variable Stiffness Devices Using Fiber Jamming for Application in Soft Robotics and Wearable Haptics. Soft Robotics. 9(1). 173–186. 83 indexed citations

Aati, Sultan, Zohaib Akram, Barsha Shrestha, et al.. (2021). Effect of post-curing light exposure time on the physico–mechanical properties and cytotoxicity of 3D-printed denture base material. Dental Materials. 38(1). 57–67. 115 indexed citations

Shih, Benjamin, et al.. (2020). Classification of components of affective touch using rapidly-manufacturable soft sensor skins. 182–187. 4 indexed citations

Shih, Benjamin, Dylan Shah, Jinxing Li, et al.. (2020). Electronic skins and machine learning for intelligent soft robots. Science Robotics. 5(41). 534 indexed citations breakdown →

Shih, Benjamin, et al.. (2019). Design Considerations for 3D Printed, Soft, Multimaterial Resistive Sensors for Soft Robotics. Frontiers in Robotics and AI. 6. 30–30. 81 indexed citations

Thuruthel, Thomas George, Benjamin Shih, Cecilia Laschi, & Michael T. Tolley. (2019). Soft robot perception using embedded soft sensors and recurrent neural networks. Science Robotics. 4(26). 507 indexed citations breakdown →

Drotman, Dylan, et al.. (2019). Morphing Structure for Changing Hydrodynamic Characteristics of a Soft Underwater Walking Robot. IEEE Robotics and Automation Letters. 4(4). 4163–4169. 50 indexed citations

10.

Shih, Benjamin, Kenneth R. Koedinger, & Richard Scheines. (2018). Optimizing Student Models for Causality. Research Showcase @ Carnegie Mellon University (Carnegie Mellon University). 644–646.

11.

Shih, Benjamin, et al.. (2018). 3D printed resistive soft sensors. 152–157. 12 indexed citations

12.

Wang, Yueqi, Benjamin Shih, Dylan Drotman, et al.. (2017). Differential pressure control of 3D printed soft fluidic actuators. 6207–6213. 62 indexed citations

13.

Shih, Benjamin, Dylan Drotman, Caleb Christianson, et al.. (2017). Custom soft robotic gripper sensor skins for haptic object visualization. 494–501. 73 indexed citations

14.

Kim, Yun‐Soung, Jesse Lu, Benjamin Shih, et al.. (2017). Scalable Manufacturing of Solderable and Stretchable Physiologic Sensing Systems. Advanced Materials. 29(39). 50 indexed citations

15.

Brooks, Justin, et al.. (2015). Target Class Induction through Image Feedback Manipulation in Rapid Serial Visual Presentation Experiments. 1047–1052. 1 indexed citations

16.

Florez, Juan Manuel, et al.. (2014). Soft pneumatic actuators for legged locomotion. Infoscience (Ecole Polytechnique Fédérale de Lausanne). 27–34. 26 indexed citations

17.

Shih, Benjamin. (2011). Target Sequence Clustering. Research Showcase @ Carnegie Mellon University (Carnegie Mellon University). 2 indexed citations

18.

Shih, Benjamin, Kenneth R. Koedinger, & Richard Scheines. (2010). Unsupervised Discovery of Student Strategies.. Educational Data Mining. 201–210. 18 indexed citations

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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