Dawen Liang

6.1k total citations · 2 hit papers
27 papers, 3.3k citations indexed

About

Dawen Liang is a scholar working on Signal Processing, Artificial Intelligence and Information Systems. According to data from OpenAlex, Dawen Liang has authored 27 papers receiving a total of 3.3k indexed citations (citations by other indexed papers that have themselves been cited), including 13 papers in Signal Processing, 12 papers in Artificial Intelligence and 10 papers in Information Systems. Recurrent topics in Dawen Liang's work include Music and Audio Processing (12 papers), Recommender Systems and Techniques (10 papers) and Speech and Audio Processing (6 papers). Dawen Liang is often cited by papers focused on Music and Audio Processing (12 papers), Recommender Systems and Techniques (10 papers) and Speech and Audio Processing (6 papers). Dawen Liang collaborates with scholars based in United States, Canada and United Kingdom. Dawen Liang's co-authors include Daniel P. W. Ellis, Colin Raffel, Brian McFee, Oriol Nieto, Matt McVicar, Eric Battenberg, Matthew D. Hoffman, Tony Jebara, Rahul G. Krishnan and Laurent Charlin and has published in prestigious journals such as AI Magazine, Computer Music Journal and arXiv (Cornell University).

In The Last Decade

Dawen Liang

25 papers receiving 3.1k citations

Hit Papers

librosa: Audio and Music Signal Analysis in Python 2015 2026 2018 2022 2015 2018 500 1000 1.5k
What are hit papers?

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.

  1. librosa: Audio and Music Signal Analysis in Python
    2015 1.7k citations Brian McFee, Colin Raffel et al. Proceedings of the Python in Science Conferences profile →
  2. Variational Autoencoders for Collaborative Filtering
    2018 739 citations Dawen Liang, Rahul G. Krishnan et al. profile →

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Dawen Liang United States 11 1.5k 1.4k 1.1k 965 349 27 3.3k
Robert C. Moore United States 29 2.3k 1.5× 3.4k 2.4× 245 0.2× 1.6k 1.7× 82 0.2× 67 6.4k
Colin Raffel United States 18 1.6k 1.1× 2.1k 1.5× 155 0.1× 1.2k 1.3× 53 0.2× 41 3.9k
Hsiao-Wuen Hon United States 23 2.0k 1.3× 2.9k 2.1× 557 0.5× 635 0.7× 83 0.2× 65 3.8k
Ron J. Weiss United States 31 3.8k 2.5× 3.9k 2.8× 327 0.3× 1.2k 1.3× 43 0.1× 53 6.2k
Brian McFee United States 23 2.3k 1.5× 987 0.7× 183 0.2× 1.6k 1.6× 39 0.1× 57 3.3k
Miguel A. Ferrer Spain 33 1.2k 0.8× 936 0.7× 505 0.5× 2.3k 2.4× 85 0.2× 211 4.1k
Gerhard Widmer Austria 34 3.7k 2.4× 2.4k 1.7× 543 0.5× 3.1k 3.2× 278 0.8× 241 5.8k
George R. Doddington United States 21 1.9k 1.3× 4.0k 2.9× 495 0.4× 808 0.8× 91 0.3× 65 4.9k
Manoj Plakal United States 6 2.3k 1.5× 1.0k 0.7× 270 0.2× 1.4k 1.4× 15 0.0× 7 3.5k
Xavier Serra Spain 32 3.5k 2.3× 813 0.6× 241 0.2× 2.5k 2.6× 41 0.1× 235 4.2k

Countries citing papers authored by Dawen Liang

Since Specialization
Citations

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

Fields of papers citing papers by Dawen Liang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Dawen Liang

This figure shows the co-authorship network connecting the top 25 collaborators of Dawen Liang. A scholar is included among the top collaborators of Dawen Liang 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 Dawen Liang. Dawen Liang 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.
Zhu, Yaochen, et al.. (2025). Collaborative Retrieval for Large Language Model-based Conversational Recommender Systems. 3323–3334. 2 indexed citations
2.
3.
Wang, Lequn, et al.. (2024). Off-Policy Evaluation for Large Action Spaces via Policy Convolution. 3576–3585. 1 indexed citations
4.
Steck, Harald, et al.. (2021). Deep learning for recommender systems: A Netflix case study. AI Magazine. 42(3). 7–18. 66 indexed citations
5.
Liang, Dawen, Rahul G. Krishnan, Matthew D. Hoffman, & Tony Jebara. (2018). Variational Autoencoders for Collaborative Filtering. 689–698. 739 indexed citations breakdown →
6.
Liang, Dawen, Jaan Altosaar, Laurent Charlin, & David M. Blei. (2016). Factorization Meets the Item Embedding. 59–66. 162 indexed citations
7.
Liang, Dawen, Laurent Charlin, James O. McInerney, & David M. Blei. (2016). Modeling User Exposure in Recommendation. 951–961. 224 indexed citations
8.
Liang, Dawen & John Paisley. (2015). Landmarking Manifolds with Gaussian Processes. International Conference on Machine Learning. 466–474. 4 indexed citations
9.
McFee, Brian, Colin Raffel, Dawen Liang, et al.. (2015). librosa: Audio and Music Signal Analysis in Python. Proceedings of the Python in Science Conferences. 18–24. 1676 indexed citations breakdown →
10.
McFee, Brian, Matt McVicar, Colin Raffel, et al.. (2015). librosa: 0.4.1. Zenodo (CERN European Organization for Nuclear Research). 7 indexed citations
11.
Liang, Dawen, Matthew D. Hoffman, & Gautham J. Mysore. (2015). Speech dereverberation using a learned speech model. 13. 1871–1875. 6 indexed citations
12.
Liang, Dawen, et al.. (2015). Content-Aware Collaborative Music Recommendation Using Pre-Trained Neural Networks.. Zenodo (CERN European Organization for Nuclear Research). 295–301. 33 indexed citations
13.
Liang, Dawen, Matthew D. Hoffman, & Gautham J. Mysore. (2014). A Generative Product-of-Filters Model of Audio. arXiv (Cornell University). 2 indexed citations
14.
Raffel, Colin, Brian McFee, Eric J. Humphrey, et al.. (2014). Mir_Eval: A Transparent Implementation Of Common Mir Metrics.. Zenodo (CERN European Organization for Nuclear Research). 367–372. 190 indexed citations
15.
Dannenberg, Roger B., et al.. (2014). Active Scores: Representation and Synchronization in Human–Computer Performance of Popular Music. Computer Music Journal. 38(2). 51–62. 5 indexed citations
16.
Dannenberg, Roger B., et al.. (2014). Methods and Prospects for Human–Computer Performance of Popular Music. Computer Music Journal. 38(2). 36–50. 4 indexed citations
17.
Liang, Dawen, John Paisley, & Dan Ellis. (2014). Codebook-Based Scalable Music Tagging With Poisson Matrix Factorization.. Zenodo (CERN European Organization for Nuclear Research). 167–172. 10 indexed citations
18.
McFee, Brian, et al.. (2014). librosa: v0.3.1. Zenodo (CERN European Organization for Nuclear Research). 2 indexed citations
19.
Liang, Dawen, et al.. (2011). A Framework For Coordination And Synchronization Of Media. Zenodo (CERN European Organization for Nuclear Research). 167–172. 8 indexed citations
20.
Liang, Dawen, et al.. (2011). Segmentation, Clustering, And Display In A Personal Audio Database For Musicians.. Zenodo (CERN European Organization for Nuclear Research). 139–144. 3 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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