Jean Oh

3.1k citations
84 papers · 1.5k · 2 hit papers · h-index 18

Impact in

Papers in

Jean Oh

80 papers receiving 1.4k citations

Jean Oh's Hit Papers

Core Challenges of Social Robot Navigation: A Survey 2023 · 128 citations
1280+2+5Years since publication100200300400

Peers

Jean Oh
Comparison fields: 5 of 82
  • Computer Vision and Pattern Recognition 758
  • Automotive Engineering 440
  • Artificial Intelligence 624
  • Safety, Risk, Reliability and Quality 170
  • Computer Graphics and Computer-Aided Design 39
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Citations per field
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Citations per year

Countries citing papers authored by Jean Oh

Since Specialization
Citations

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

Fields of papers citing papers by Jean Oh

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 84 papers — load more, or switch the sort, to bring in the rest.

#Work
1
Social Attention: Modeling Attention in Human Crowds
Hit paper breakdown →
2018445
2
Core Challenges of Social Robot Navigation: A Survey
Hit paper breakdown →
2023128
3 2016110
4
Electric Elves: Applying Agent Technology to Support Human Organizations
200181
5 202253
6 200242
7 202232
8 201530
9 200128
10 202327
11 201225
12 202022
13 202122
14 202121
15 202220
16 202220
17 202318
18 201517
19 202017
20 202217

About Jean Oh

Jean Oh is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence, Control and Systems Engineering, Aerospace Engineering and Computer Networks and Communications, having authored 84 papers that have together received 1.5k indexed citations. Recurring topics across this work include Multimodal Machine Learning Applications (12 papers), Robotics and Sensor-Based Localization (9 papers), Robotic Path Planning Algorithms (8 papers), AI-based Problem Solving and Planning (7 papers), Multi-Agent Systems and Negotiation (7 papers), Advanced Vision and Imaging (7 papers), Autonomous Vehicle Technology and Safety (6 papers) and Human-Automation Interaction and Safety (6 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (758 citations), Automotive Engineering (440 citations), Artificial Intelligence (624 citations), Safety, Risk, Reliability and Quality (170 citations) and Computer Graphics and Computer-Aided Design (39 citations). Jean Oh has collaborated with scholars based in United States, South Korea and Switzerland. Frequent co-authors include Anirudh Vemula, Katharina Muelling, Craig A. Knoblock, Chris Dyer, Po-Yao Huang, Frederick Liu, Christoforos Mavrogiannis, Pete Trautman, Francesca Baldini and Allan Wang. Their work appears in journals such as IEEE Robotics and Automation Letters, IEEE Transactions on Pattern Analysis and Machine Intelligence, Engineering Applications of Artificial Intelligence, AI Magazine and IEEE Robotics & Automation Magazine.

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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