Andrew Feng

3.0k total citations
50 papers, 918 citations indexed

About

Andrew Feng is a scholar working on Computer Vision and Pattern Recognition, Control and Systems Engineering and Artificial Intelligence. According to data from OpenAlex, Andrew Feng has authored 50 papers receiving a total of 918 indexed citations (citations by other indexed papers that have themselves been cited), including 24 papers in Computer Vision and Pattern Recognition, 21 papers in Control and Systems Engineering and 12 papers in Artificial Intelligence. Recurrent topics in Andrew Feng's work include Human Motion and Animation (21 papers), Human Pose and Action Recognition (15 papers) and Hand Gesture Recognition Systems (7 papers). Andrew Feng is often cited by papers focused on Human Motion and Animation (21 papers), Human Pose and Action Recognition (15 papers) and Hand Gesture Recognition Systems (7 papers). Andrew Feng collaborates with scholars based in United States, United Kingdom and Australia. Andrew Feng's co-authors include Ari Shapiro, Yuyu Xu, Daguang Xu, Wenqi Li, Stacy Marsella, Yan Cheng, M. Jorge Cardoso, Sébastien Ourselin, Maximilian Baust and Wentao Zhu and has published in prestigious journals such as SHILAP Revista de lepidopterología, Journal of Hepatology and IEEE Transactions on Medical Imaging.

In The Last Decade

Andrew Feng

45 papers receiving 886 citations

Peers

Andrew Feng
Nataniel Ruiz United States
Rohit Girdhar United States
Brandon Rothrock United States
Vinay P. Namboodiri India
Hazım Kemal Ekenel Türkiye
Zhengyuan Yang United States
Abdulhameed Alelaiwi Saudi Arabia
Supasorn Suwajanakorn Thailand
Wayne Wu China
Vitomir Štruc Slovenia
Nataniel Ruiz United States
Andrew Feng
Citations per year, relative to Andrew Feng Andrew Feng (= 1×) peers Nataniel Ruiz

Countries citing papers authored by Andrew Feng

Since Specialization
Citations

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

Fields of papers citing papers by Andrew Feng

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Andrew Feng

This figure shows the co-authorship network connecting the top 25 collaborators of Andrew Feng. A scholar is included among the top collaborators of Andrew Feng 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 Andrew Feng. Andrew Feng 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.
Sun, David, et al.. (2025). Deformable Beta Splatting. 1–11.
2.
Roth, Holger R., Ziyue Xu, Daguang Xu, et al.. (2025). Overview of real-world applications of federated learning with NVIDIA FLARE. Journal of Biopharmaceutical Statistics. 1–11.
3.
Ke, Pei, Andrew Feng, Xiao Liu, et al.. (2024). CritiqueLLM: Towards an Informative Critique Generation Model for Evaluation of Large Language Model Generation. 13034–13054. 3 indexed citations
4.
Han, Ki Jin, et al.. (2024). An Aerial Photogrammetry Benchmark Dataset for Point Cloud Segmentation and Style Translation. Remote Sensing. 16(22). 4240–4240. 3 indexed citations
5.
Murray, Benjamin, Richard Brown, Eric Kerfoot, et al.. (2024). Lazy Resampling: Fast and information preserving preprocessing for deep learning. Computer Methods and Programs in Biomedicine. 257. 108422–108422. 1 indexed citations
6.
Huang, Yi, Andrew Feng, Pei Ke, et al.. (2024). AlignBench: Benchmarking Chinese Alignment of Large Language Models. 11621–11640. 4 indexed citations
7.
Feng, Andrew, et al.. (2024). Synthetic-to-real adaptation for complex action recognition in surveillance applications. 14–14. 1 indexed citations
8.
Hatamizadeh, Ali, Hongxu Yin, Pavlo Molchanov, et al.. (2023). Do Gradient Inversion Attacks Make Federated Learning Unsafe?. IEEE Transactions on Medical Imaging. 42(7). 2044–2056. 64 indexed citations
9.
Feng, Andrew, et al.. (2023). Discrete Diffusion for Co-Speech Gesture Synthesis. 186–192. 1 indexed citations
10.
11.
Feng, Andrew, et al.. (2022). The DeepMotion entry to the GENEA Challenge 2022. 790–796. 8 indexed citations
12.
Bönsch, Andrea, Andrew Feng, P. V. Patel, & Ari Shapiro. (2019). Volumetric Video Capture using Unsynchronized, Low-cost Cameras. 255–261.
13.
Li, Wenqi, Fausto Milletarì, Daguang Xu, et al.. (2019). Privacy-Preserving Federated Brain Tumour Segmentation. Lecture notes in computer science. 11861. 133–141. 294 indexed citations
14.
15.
16.
Feng, Andrew, Evan Suma Rosenberg, & Ari Shapiro. (2017). Just‐in‐time, viable, 3‐D avatars from scans. Computer Animation and Virtual Worlds. 28(3-4). 17 indexed citations
17.
Abuzaid, Firas, et al.. (2016). Yggdrasil: An Optimized System for Training Deep Decision Trees at Scale. Neural Information Processing Systems. 29. 3810–3818. 14 indexed citations
18.
Casas, Dan, Oleg Alexander, Andrew Feng, et al.. (2015). Rapid photorealistic blendshapes from commodity RGB-D sensors. 134–134. 8 indexed citations
19.
Miguel, Eder, Andrew Feng, Yuyu Xu, & Ari Shapiro. (2014). Towards Cloth-Manipulating Characters. 3 indexed citations
20.
Marsella, Stacy, Yuyu Xu, Margaux Lhommet, et al.. (2013). Virtual character performance from speech. 25–35. 104 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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2026