Lillian Chang

19 papers receiving 669 citations

Peers

Lillian Chang
Comparison fields: 5 of 76
  • Control and Systems Engineering 400
  • Human-Computer Interaction 87
  • Cognitive Neuroscience 153
  • Biomedical Engineering 311
  • Computer Vision and Pattern Recognition 127
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Adam J. Spiers United Kingdom
Tomas Kulvičius Germany
Tomoya Tamei Japan
Yoshiyuki Ohmura Japan
Ryuta Ozawa Japan
Hegao Cai China
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Citations per year

Countries citing papers authored by Lillian Chang

Since Specialization
Citations

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

Fields of papers citing papers by Lillian Chang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

20 of 20 papers shown
#Work
1 2011137
2 201276
3 200655
4 200655
5 200849
6 201149
7 201043
8 200739
9 200434
10 200730
11 201029
12 200825
13 201123
14 201013
15 201812
16
Shifting Understandings of Community College Faculty Members: Results of an Equity-Focused Professional Development Experience.
201610
17 20216
18 20182
19
Pre-grasp interaction as a manipulation strategy for movable objects
20101
20 20180

About Lillian Chang

Lillian Chang is a scholar working on Control and Systems Engineering, Biomedical Engineering, Computer Vision and Pattern Recognition, Cognitive Neuroscience and Mechanical Engineering, having authored 20 papers that have together received 688 indexed citations. Recurring topics across this work include Robot Manipulation and Learning (12 papers), Muscle activation and electromyography studies (4 papers), Hand Gesture Recognition Systems (4 papers), Robotic Mechanisms and Dynamics (4 papers), Robotic Path Planning Algorithms (3 papers), Teleoperation and Haptic Systems (2 papers), Robotic Locomotion and Control (2 papers) and Neuroscience and Neural Engineering (2 papers). The work is most often cited by research in Control and Systems Engineering (400 citations), Human-Computer Interaction (87 citations), Cognitive Neuroscience (153 citations), Biomedical Engineering (311 citations) and Computer Vision and Pattern Recognition (127 citations). Lillian Chang has collaborated with scholars based in United States, Germany and Spain. Frequent co-authors include Nancy S. Pollard, Yoky Matsuoka, Joshua R. Smith, Dieter Fox, David Wilkinson, Jonathan Ko, Zhe Xü, Ashish D. Deshpande, Sean M. Bidic and Siddhartha S Srinivasa. Their work appears in journals such as Journal of Biomechanics, Robotics and Autonomous Systems, Frontiers in Bioengineering and Biotechnology, IEEE Transactions on Biomedical Engineering and Genome Biology and Evolution.

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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