Henggang Cui

2.6k total citations · 2 hit papers
20 papers, 1.5k citations indexed

About

Henggang Cui is a scholar working on Automotive Engineering, Computer Vision and Pattern Recognition and Artificial Intelligence. According to data from OpenAlex, Henggang Cui has authored 20 papers receiving a total of 1.5k indexed citations (citations by other indexed papers that have themselves been cited), including 11 papers in Automotive Engineering, 11 papers in Computer Vision and Pattern Recognition and 7 papers in Artificial Intelligence. Recurrent topics in Henggang Cui's work include Autonomous Vehicle Technology and Safety (11 papers), Advanced Neural Network Applications (7 papers) and Video Surveillance and Tracking Methods (6 papers). Henggang Cui is often cited by papers focused on Autonomous Vehicle Technology and Safety (11 papers), Advanced Neural Network Applications (7 papers) and Video Surveillance and Tracking Methods (6 papers). Henggang Cui collaborates with scholars based in United States, China and Singapore. Henggang Cui's co-authors include Gregory R. Ganger, Phillip B. Gibbons, Eric P. Xing, Nemanja Djuric, Tsung-Han Lin, Thi Nguyen, Fang‐Chieh Chou, Jeff Schneider, Vladan Radosavljević and Garth A. Gibson and has published in prestigious journals such as PubMed, arXiv (Cornell University) and 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).

In The Last Decade

Henggang Cui

20 papers receiving 1.5k citations

Hit Papers

Multimodal Trajectory Pre... 2013 2026 2017 2021 2019 2013 100 200 300 400

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Henggang Cui United States 13 762 695 554 327 214 20 1.5k
Tsz-Chiu Au United States 14 595 0.8× 221 0.3× 404 0.7× 178 0.5× 201 0.9× 33 1.4k
Liangkai Liu United States 13 246 0.3× 408 0.6× 305 0.6× 563 1.7× 225 1.1× 29 1.3k
Yair Wiseman Israel 18 223 0.3× 320 0.5× 246 0.4× 307 0.9× 115 0.5× 82 1.0k
Xuting Duan China 25 253 0.3× 284 0.4× 369 0.7× 720 2.2× 244 1.1× 99 1.7k
Jonathan Petit Netherlands 18 608 0.8× 103 0.1× 501 0.9× 710 2.2× 340 1.6× 54 1.7k
Qi Alfred Chen United States 20 552 0.7× 163 0.2× 184 0.3× 546 1.7× 367 1.7× 87 1.3k
Benjamin Sapp United States 15 362 0.5× 627 0.9× 433 0.8× 40 0.1× 138 0.6× 21 1.2k
Oliver Bringmann Germany 19 215 0.3× 237 0.3× 235 0.4× 439 1.3× 62 0.3× 205 1.5k
Siyu Teng China 12 232 0.3× 406 0.6× 487 0.9× 84 0.3× 56 0.3× 27 1.2k
Tzu-Kuo Huang United States 6 313 0.4× 280 0.4× 349 0.6× 48 0.1× 269 1.3× 10 893

Countries citing papers authored by Henggang Cui

Since Specialization
Citations

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

Fields of papers citing papers by Henggang Cui

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Henggang Cui

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

All Works

20 of 20 papers shown
1.
Liu, Zhongtao, et al.. (2022). Importance is in your attention: agent importance prediction for autonomous driving. 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). 2531–2534. 6 indexed citations
2.
Cui, Henggang, et al.. (2021). Uncertainty-Aware Estimation of Vehicle Orientation for Self-Driving Applications. 18. 2660–2666. 1 indexed citations
3.
Wang, Chao, et al.. (2021). Temporally-Continuous Probabilistic Prediction using Polynomial Trajectory Parameterization. 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). 3837–3843. 5 indexed citations
4.
Cui, Henggang, et al.. (2021). Ellipse Loss for Scene-Compliant Motion Prediction. 4 indexed citations
5.
Djuric, Nemanja, Vladan Radosavljević, Henggang Cui, et al.. (2020). Uncertainty-aware Short-term Motion Prediction of Traffic Actors for Autonomous Driving. 2084–2093. 134 indexed citations
6.
Cui, Henggang, Thi Nguyen, Fang‐Chieh Chou, et al.. (2020). Deep Kinematic Models for Kinematically Feasible Vehicle Trajectory Predictions. 10563–10569. 57 indexed citations
8.
Cui, Henggang, Thi Nguyen, Fang‐Chieh Chou, et al.. (2019). Deep Kinematic Models for Physically Realistic Prediction of Vehicle Trajectories. arXiv (Cornell University). 7 indexed citations
9.
Cui, Henggang, Vladan Radosavljević, Fang‐Chieh Chou, et al.. (2019). Multimodal Trajectory Predictions for Autonomous Driving using Deep Convolutional Networks. 2090–2096. 414 indexed citations breakdown →
10.
Djuric, Nemanja, Vladan Radosavljević, Henggang Cui, et al.. (2018). Motion Prediction of Traffic Actors for Autonomous Driving using Deep Convolutional Networks.. arXiv (Cornell University). 26 indexed citations
11.
Djuric, Nemanja, Vladan Radosavljević, Henggang Cui, et al.. (2018). Short-term Motion Prediction of Traffic Actors for Autonomous Driving using Deep Convolutional Networks. arXiv (Cornell University). 20 indexed citations
12.
Cui, Henggang, James Cipar, Qirong Ho, et al.. (2018). Exploiting bounded staleness to speed up Big Data analytics. Research Showcase @ Carnegie Mellon University (Carnegie Mellon University). 37–48. 53 indexed citations
13.
Cui, Henggang, Hao Zhang, Gregory R. Ganger, Phillip B. Gibbons, & Eric P. Xing. (2016). GeePS. 1–16. 196 indexed citations
14.
Harlap, Aaron, Henggang Cui, Wei Dai, et al.. (2016). Addressing the straggler problem for iterative convergent parallel ML. 98–111. 93 indexed citations
15.
Wei, Jinliang, Wei Dai, Qirong Ho, et al.. (2015). Managed communication and consistency for fast data-parallel iterative analytics. 381–394. 72 indexed citations
16.
Cui, Henggang, Kimberly Keeton, Indrajit Roy, Krishnamurthy Viswanathan, & Gregory R. Ganger. (2015). Using data transformations for low-latency time series analysis. 395–407. 5 indexed citations
17.
Cui, Henggang, Alexey Tumanov, Jinliang Wei, et al.. (2014). Exploiting iterative-ness for parallel ML computations. 1–14. 20 indexed citations
18.
Ho, Qirong, James Cipar, Henggang Cui, et al.. (2013). More Effective Distributed ML via a Stale Synchronous Parallel Parameter Server. PubMed. 2013. 1223–1231. 350 indexed citations breakdown →
19.
Cui, Henggang, Danielle Rasooly, Moisés R. N. Ribeiro, & L.G. Kazovsky. (2012). Optically Cross-Braced Hypercube: a Reconfigurable Physical Layer for Interconnects and Server-Centric Datacenters. Optical Fiber Communication Conference. OW3J.1–OW3J.1. 9 indexed citations
20.
Li, Dan, Henggang Cui, Yan Hu, Yong Xia, & Xin Wang. (2011). Scalable data center multicast using multi-class Bloom Filter. 266–275. 53 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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