Qirong Ho

3.0k total citations · 2 hit papers
38 papers, 1.5k citations indexed

About

Qirong Ho is a scholar working on Artificial Intelligence, Statistical and Nonlinear Physics and Information Systems. According to data from OpenAlex, Qirong Ho has authored 38 papers receiving a total of 1.5k indexed citations (citations by other indexed papers that have themselves been cited), including 30 papers in Artificial Intelligence, 11 papers in Statistical and Nonlinear Physics and 8 papers in Information Systems. Recurrent topics in Qirong Ho's work include Complex Network Analysis Techniques (11 papers), Stochastic Gradient Optimization Techniques (10 papers) and Cloud Computing and Resource Management (8 papers). Qirong Ho is often cited by papers focused on Complex Network Analysis Techniques (11 papers), Stochastic Gradient Optimization Techniques (10 papers) and Cloud Computing and Resource Management (8 papers). Qirong Ho collaborates with scholars based in United States, Singapore and China. Qirong Ho's co-authors include Eric P. Xing, Seunghak Lee, Garth A. Gibson, Wei Dai, Jin Kyu Kim, Jinliang Wei, Pengtao Xie, Xun Zheng, Gregory R. Ganger and Phillip B. Gibbons and has published in prestigious journals such as Journal of the American Statistical Association, Journal of Machine Learning Research and Engineering.

In The Last Decade

Qirong Ho

37 papers receiving 1.4k citations

Hit Papers

align trajectories

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.

More Effective Distributed ML via a Stale Synchronous Parallel Parameter Server

2013 350 citations Qirong Ho, James Cipar et al. PubMed profile →
Petuum: A New Platform for Distributed Machine Learning on Big Data

2015 257 citations Eric P. Xing, Qirong Ho et al. profile →

Peers

Qirong Ho

AI

CVPR

CNC

IS

HA

Markus Weimer United States

Benjamin Moseley United States

Yingxia Shao China

Jason Y. Zien United States

Stephen Tu United States

Seong Oun Hwang South Korea

Zihui Ge United States

Lidan Shou China

Seunghak Lee United States

Jidong Zhai China

Markus Weimer United States

Qirong Ho

934 ×0.9

AI

438 ×0.9

CVPR

320 ×0.8

CNC

559 ×1.7

IS

65 ×0.4

HA

Citations per year, relative to Qirong Ho Qirong Ho (= 1×) peers Markus Weimer

Countries citing papers authored by Qirong Ho

Since Specialization

Citations

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

Fields of papers citing papers by Qirong Ho

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Qirong Ho

This figure shows the co-authorship network connecting the top 25 collaborators of Qirong Ho. A scholar is included among the top collaborators of Qirong Ho 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 Qirong Ho. Qirong Ho 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

1.

Xu, Shizhen, Hao Zhang, Graham Neubig, et al.. (2018). Cavs: An Efficient Runtime System for Dynamic Neural Networks. USENIX Annual Technical Conference. 937–950. 4 indexed citations

2.

Ho, Qirong, Junming Yin, & Eric P. Xing. (2018). On Triangular versus Edge Representations — Towards Scalable Modeling of Networks. Figshare. 25. 2132–2140. 2 indexed citations

3.

Cipar, James, Qirong Ho, Jin Kyu Kim, et al.. (2018). Solving the Straggler Problem with Bounded Staleness. Figshare. 22–22. 32 indexed citations

4.

Neiswanger, Willie, Chong Wang, Qirong Ho, & Eric P. Xing. (2018). Modeling Citation Networks using Latent Random Offsets. Figshare. 633–642. 1 indexed citations

5.

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

6.

Ahmed, Amr, Qirong Ho, Choon Hui Teo, et al.. (2018). Online Inference for the Infinite Topic-Cluster Model: Storylines from Streaming Text. Figshare. 15. 101–109. 8 indexed citations

7.

Ho, Qirong, et al.. (2018). Multiscale Community Blockmodel for Network Exploration.. Research Showcase @ Carnegie Mellon University (Carnegie Mellon University). 333–341. 4 indexed citations

8.

Ho, Qirong, et al.. (2018). Dependent nonparametric trees for dynamic hierarchical clustering. Research Showcase @ Carnegie Mellon University (Carnegie Mellon University). 27. 1152–1160. 2 indexed citations

9.

Dai, Wei, Jinliang Wei, Jin Kyu Kim, et al.. (2018). Petuum: A Framework for Iterative-Convergent Distributed ML. Figshare. 5 indexed citations

10.

Yin, Junming, Qirong Ho, & Eric P. Xing. (2018). A Scalable Approach to Probabilistic Latent Space Inference of Large-Scale Networks. PubMed. 2013. 422–430. 7 indexed citations

11.

Lee, Seunghak, Jin Kyu Kim, Xun Zheng, et al.. (2018). On Model Parallelization and Scheduling Strategies for Distributed Machine Learning. Figshare. 27. 2834–2842. 42 indexed citations

12.

Ho, Qirong, Junming Yin, & Eric P. Xing. (2016). Latent space inference of internet-scale networks. Journal of Machine Learning Research. 17(1). 2756–2796. 4 indexed citations

13.

Xie, Pengtao, Jin Kyu Kim, Yi Zhou, et al.. (2016). Lighter-communication distributed machine learning via Sufficient Factor Broadcasting. Uncertainty in Artificial Intelligence. 795–804. 8 indexed citations

14.

Ho, Qirong, et al.. (2016). ConTrack: A Scalable Method for Tracking Multiple Concepts in Large Scale Multidimensional Data. 759–768. 1 indexed citations

15.

Hu, Zhiting, Qirong Ho, Avinava Dubey, & Eric P. Xing. (2015). Large-scale Distributed Dependent Nonparametric Trees. International Conference on Machine Learning. 1651–1659. 3 indexed citations

16.

Wei, Jinliang, Wei Dai, Qirong Ho, et al.. (2015). Managed communication and consistency for fast data-parallel iterative analytics. 381–394. 72 indexed citations

17.

Xing, Eric P., Qirong Ho, Wei Dai, et al.. (2015). Petuum. 1335–1344. 76 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.

Ho, Qirong, Ankur P. Parikh, & Eric P. Xing. (2012). A Multiscale Community Blockmodel for Network Exploration. Journal of the American Statistical Association. 107(499). 916–934. 17 indexed citations

20.

Ho, Qirong, Le Song, & Eric P. Xing. (2011). Evolving Cluster Mixed-Membership Blockmodel for Time-Evolving Networks. International Conference on Artificial Intelligence and Statistics. 15. 342–350. 37 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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