Inho Ha

2.8k citations
26 papers · 2.5k indexed · 3 hit papers · h-index 18

Inho Ha

23 papers receiving 2.4k citations

Hit Papers

Digital selective transformation and patterning o...2022019202620212023100200300

Peers

Inho Ha
Comparison fields: 5 of 100
  • Polymers and Plastics 632
  • Biomedical Engineering 1.9k
  • Electronic, Optical and Magnetic Materials 446
  • Cognitive Neuroscience 410
  • Human-Computer Interaction 105
Replace Phillip Won with:
Phillip Won South Korea
Zhaohe Dai China
Rui Guo China
J. William Boley United States
Sungwoo Chun South Korea
Young‐Geun Park South Korea
Taisong Pan China
Eric J. Markvicka United States
Toan Dinh Australia
Inho Ha relative to Phillip Won South Korea Phillip Won's profile →
Citations per field
00.5×1.5×
Phillip Won · 1×
Citations per year

Countries citing papers authored by Inho Ha

Since Specialization
Citations

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

Fields of papers citing papers by Inho Ha

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network

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

All Works

20 of 20 papers shown
#Work
1 20250
2 20250
3 202427
4 202334
5 202249
6 202214
7 20215
8 202168
9
A deep-learned skin sensor decoding the epicentral human motionsbreakdown →
2020243
10 2020211
11 2020133
12 202075
13 20203
14 201973
15 20190
16
Stretchable and Transparent Kirigami Conductor of Nanowire Percolation Network for Electronic Skin Applicationsbreakdown →
2019367
17 2018266
18 2017291
19 2017232
20 201710

About Inho Ha

Inho Ha is a scholar working on Biomedical Engineering, Human-Computer Interaction and Condensed Matter Physics, having authored 26 papers that have together received 2.5k indexed citations. Recurring topics across this work include Advanced Sensor and Energy Harvesting Materials (15 papers), Advanced Materials and Mechanics (8 papers), Tactile and Sensory Interactions (5 papers), Micro and Nano Robotics (4 papers), Advanced Memory and Neural Computing (3 papers), Dielectric materials and actuators (3 papers), Neuroscience and Neural Engineering (2 papers) and Soft Robotics and Applications (2 papers). The work is most often cited by research in Polymers and Plastics (632 citations), Biomedical Engineering (1.9k citations) and Electronic, Optical and Magnetic Materials (446 citations). Inho Ha has collaborated with scholars based in South Korea, United States and Puerto Rico. Frequent co-authors include Seung Hwan Ko, Phillip Won, Sukjoon Hong, Jinhyeong Kwon, Habeom Lee, Hyunmin Cho, Jinwook Jung, Kyun Kyu Kim, Kyu‐Jin Cho and Seonggeun Han. Their work appears in journals such as Advanced Functional Materials, Advanced Materials, Science Advances, Chemical Engineering Journal and Nano Letters.

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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