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.
OBBTree
19961.5k citationsMing C. Lin, Dinesh Manocha et al.profile →
Reciprocal Velocity Obstacles for real-time multi-agent navigation
20081.1k citationsJur van den Berg, Ming C. Lin et al.profile →
Example-guided physically based modal sound synthesis
2013319 citationsHengchin Yeh, Ming C. Lin et al.profile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
hero ref
This map shows the geographic impact of Ming C. Lin'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 Ming C. Lin with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ming C. Lin more than expected).
This network shows the impact of papers produced by Ming C. Lin. 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 Ming C. Lin. The network helps show where Ming C. Lin may publish in the future.
Co-authorship network of co-authors of Ming C. Lin
This figure shows the co-authorship network connecting the top 25 collaborators of Ming C. Lin.
A scholar is included among the top collaborators of Ming C. Lin 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 Ming C. Lin. Ming C. Lin is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Shen, Yu, et al.. (2021). Gradient-Free Adversarial Training Against Image Corruption for Learning-based Steering. Neural Information Processing Systems. 34.7 indexed citations
Liang, Junbang, Ming C. Lin, & Vladlen Koltun. (2019). Differentiable Cloth Simulation for Inverse Problems. Neural Information Processing Systems. 32. 771–780.48 indexed citations
9.
Snape, Jamie, Stephen J. Guy, Ming C. Lin, Dinesh Manocha, & Jur van den Berg. (2012). Reciprocal collision avoidance and multi-agent navigation for video games. National Conference on Artificial Intelligence. 49–52.21 indexed citations
Yeh, Hengchin, Sean Curtis, Sachin Patil, et al.. (2008). Composite agents. 39–47.30 indexed citations
13.
Berg, Jur van den, Ming C. Lin, & Dinesh Manocha. (2008). Reciprocal Velocity Obstacles for real-time multi-agent navigation. 1928–1935.1090 indexed citations breakdown →
Parent, Rick, Karan Singh, David E. Breen, & Ming C. Lin. (2003). Proceedings of the 2003 ACM SIGGRAPH/Eurographics symposium on Computer animation.138 indexed citations
17.
Lin, Ming C., et al.. (2001). Projects in VR. IEEE Computer Graphics and Applications. 21. 14–17.5 indexed citations
Lin, Ming C. & John Canny. (1993). Efficient collision detection for animation and robotics. UC Berkeley.166 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.