Richard Peng

3.5k total citations
56 papers, 1.0k citations indexed

About

Richard Peng is a scholar working on Computational Theory and Mathematics, Artificial Intelligence and Computer Networks and Communications. According to data from OpenAlex, Richard Peng has authored 56 papers receiving a total of 1.0k indexed citations (citations by other indexed papers that have themselves been cited), including 39 papers in Computational Theory and Mathematics, 18 papers in Artificial Intelligence and 12 papers in Computer Networks and Communications. Recurrent topics in Richard Peng's work include Complexity and Algorithms in Graphs (28 papers), Advanced Graph Theory Research (14 papers) and Sparse and Compressive Sensing Techniques (9 papers). Richard Peng is often cited by papers focused on Complexity and Algorithms in Graphs (28 papers), Advanced Graph Theory Research (14 papers) and Sparse and Compressive Sensing Techniques (9 papers). Richard Peng collaborates with scholars based in United States, Canada and Puerto Rico. Richard Peng's co-authors include Gary L. Miller, Ioannis Koutis, Shen Xu, Charalampos E. Tsourakakis, Michael B. Cohen, Rasmus Kyng, Jakub Pachocki, Sushant Sachdeva, Kanat Tangwongsan and Michael Mitzenmacher and has published in prestigious journals such as Communications of the ACM, IEEE Transactions on Knowledge and Data Engineering and SIAM Journal on Computing.

In The Last Decade

Richard Peng

53 papers receiving 985 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Richard Peng United States	20	549	437	270	194	174	56	1.0k
Konstantin Makarychev United States	18	553 1.0×	349 0.8×	427 1.6×	106 0.5×	68 0.4×	68	1.2k
Yuri Rabinovich Israel	14	631 1.1×	265 0.6×	325 1.2×	172 0.9×	44 0.3×	38	1.2k
Yin Tat Lee United States	18	373 0.7×	338 0.8×	211 0.8×	85 0.4×	42 0.2×	46	816
Virginia Vassilevska Williams United States	18	853 1.6×	472 1.1×	403 1.5×	152 0.8×	84 0.5×	71	1.3k
David Steurer United States	21	707 1.3×	383 0.9×	264 1.0×	41 0.2×	54 0.3×	47	1.1k
Ravindran Kannan United States	14	336 0.6×	330 0.8×	93 0.3×	131 0.7×	53 0.3×	28	848
Santosh Vempala United States	8	150 0.3×	452 1.0×	112 0.4×	276 1.4×	247 1.4×	8	1.0k
Aarti Singh United States	18	153 0.3×	436 1.0×	225 0.8×	141 0.7×	107 0.6×	66	947
Gopal Pandurangan United States	23	467 0.9×	445 1.0×	1.2k 4.5×	77 0.4×	189 1.1×	109	1.7k
Artur Czumaj Germany	20	511 0.9×	415 0.9×	595 2.2×	82 0.4×	70 0.4×	121	1.4k

Countries citing papers authored by Richard Peng

Since Specialization

Citations

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

Fields of papers citing papers by Richard Peng

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Richard Peng

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

All Works

Sort: Min cites: Since: Top N: Style:

20 of 20 papers shown

Chen, Li, Rasmus Kyng, Yang P. Liu, et al.. (2022). Maximum Flow and Minimum-Cost Flow in Almost-Linear Time. 612–623. 59 indexed citations

Ueno, Aito, Humberto Jijon, Richard Peng, et al.. (2021). Association of Circulating Fibrocytes With Fibrostenotic Small Bowel Crohn’s Disease. Inflammatory Bowel Diseases. 28(2). 246–258. 10 indexed citations

Ali, Syed Imran, Zi‐Ning Lei, Richard Peng, et al.. (2020). Metal (II) Complexes of Fluconazole: Thermal, XRD and Cytotoxicity Studies.. PubMed. 19(3). 171–182. 4 indexed citations

Peng, Richard, et al.. (2020). Faster Graph Embeddings via Coarsening. arXiv (Cornell University). 2 indexed citations

Peng, Richard, et al.. (2020). Flowless: Extracting Densest Subgraphs Without Flow Computations. OpenBU (Boston University). 573–583. 27 indexed citations

Peng, Richard, et al.. (2019). Fast, Provably convergent IRLS Algorithm for p-norm Linear Regression. arXiv (Cornell University). 32. 14166–14177. 2 indexed citations

Gupta, Pranav, Yunkai Zhang, Xiaoyu Zhang, et al.. (2018). Voruciclib, a Potent CDK4/6 Inhibitor, Antagonizes ABCB1 and ABCG2-Mediated Multi-Drug Resistance in Cancer Cells. Cellular Physiology and Biochemistry. 45(4). 1515–1528. 47 indexed citations

Peng, Richard, et al.. (2017). Density Independent Algorithms for Sparsifying k-Step Random Walks. DROPS (Schloss Dagstuhl – Leibniz Center for Informatics). 17. 1 indexed citations

Cohen, Michael B., Jonathan A. Kelner, John Peebles, et al.. (2017). Almost-linear-time algorithms for Markov chains and new spectral primitives for directed graphs. DSpace@MIT (Massachusetts Institute of Technology). 410–419. 23 indexed citations

10.

Peng, Richard, Aaron Sidford, Michael B. Cohen, et al.. (2016). Faster Algorithms for Computing the Stationary Distribution, Simulating Random Walks, and More. DSpace@MIT (Massachusetts Institute of Technology). 21 indexed citations

11.

Cucuringu, Mihai, Ioannis Koutis, Sanjay Chawla, Gary L. Miller, & Richard Peng. (2016). Simple and Scalable Constrained Clustering: a Generalized Spectral Method. Oxford University Research Archive (ORA) (University of Oxford). 445–454. 16 indexed citations

12.

Peng, Richard, et al.. (2016). SPALS: Fast Alternating Least Squares via Implicit Leverage Scores Sampling. Neural Information Processing Systems. 29. 721–729. 12 indexed citations

13.

Peng, Richard. (2016). Approximate undirected maximum flows in o(mpolylog(n)) time. Symposium on Discrete Algorithms. 1862–1867. 15 indexed citations

14.

Cheng, Yu, et al.. (2015). Efficient Sampling for Gaussian Graphical Models via Spectral Sparsification. Conference on Learning Theory. 364–390. 6 indexed citations

15.

Cohen, Michael B., Brittany Terese Fasy, Gary L. Miller, et al.. (2014). Solving 1-Laplacians in nearly linear time: collapsing and expanding a topological ball. Symposium on Discrete Algorithms. 2014. 204–216. 1 indexed citations

16.

Cohen, Michael B., Rasmus Kyng, Gary L. Miller, et al.. (2014). Solving SDD linear systems in nearly m log ^1/2 n time. 343–352. 50 indexed citations

17.

Mądry, Aleksander, et al.. (2013). Runtime guarantees for regression problems. 269–282. 15 indexed citations

18.

Miller, Gary L. & Richard Peng. (2012). Iterative Approaches to Row Sampling. arXiv (Cornell University). 7 indexed citations

19.

Kolountzakis, Mihail N., Gary L. Miller, Richard Peng, & Charalampos E. Tsourakakis. (2012). Efficient Triangle Counting in Large Graphs via Degree-Based Vertex Partitioning. Internet Mathematics. 8(1-2). 161–185. 63 indexed citations

20.

Koutis, Ioannis, Gary L. Miller, & Richard Peng. (2011). Solving SDD linear systems in time Õ(mlog nlog(1/ε)). arXiv (Cornell University). 1 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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