Chun-Nam Yu

2.4k total citations · 2 hit papers
16 papers, 1.5k citations indexed

About

Chun-Nam Yu is a scholar working on Artificial Intelligence, Molecular Biology and Electrical and Electronic Engineering. According to data from OpenAlex, Chun-Nam Yu has authored 16 papers receiving a total of 1.5k indexed citations (citations by other indexed papers that have themselves been cited), including 10 papers in Artificial Intelligence, 4 papers in Molecular Biology and 4 papers in Electrical and Electronic Engineering. Recurrent topics in Chun-Nam Yu's work include Natural Language Processing Techniques (5 papers), Topic Modeling (4 papers) and Machine Learning and Data Classification (3 papers). Chun-Nam Yu is often cited by papers focused on Natural Language Processing Techniques (5 papers), Topic Modeling (4 papers) and Machine Learning and Data Classification (3 papers). Chun-Nam Yu collaborates with scholars based in United States, Canada and Switzerland. Chun-Nam Yu's co-authors include Thorsten Joachims, Thomas Finley, Tin Kam Ho, Piotr Mirowski, Russell Greiner, Hsiu-Chin Lin, Vickie E. Baracos, Thomas Hofmann, Yisong Yue and Shreyas Shah and has published in prestigious journals such as ACS Nano, Communications of the ACM and IEEE Transactions on Smart Grid.

In The Last Decade

Chun-Nam Yu

15 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.

Cutting-plane training of structural SVMs

2009 607 citations Thorsten Joachims, Thomas Finley et al. Machine Learning profile →
Learning structural SVMs with latent variables

2009 390 citations Chun-Nam Yu, Thorsten Joachims profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Chun-Nam Yu United States	10	818	698	192	118	82	16	1.5k
Buyue Qian United States	23	897 1.1×	454 0.7×	85 0.4×	93 0.8×	141 1.7×	59	1.5k
Lingfeng Niu China	16	711 0.9×	447 0.6×	63 0.3×	73 0.6×	104 1.3×	82	1.2k
Takashi Onoda Japan	12	811 1.0×	476 0.7×	218 1.1×	101 0.9×	67 0.8×	73	1.4k
Wei-Jie Chen China	20	790 1.0×	823 1.2×	106 0.6×	83 0.7×	62 0.8×	57	1.3k
Julio López Chile	17	708 0.9×	351 0.5×	94 0.5×	67 0.6×	74 0.9×	54	1.2k
Chong Fu China	20	618 0.8×	1.1k 1.6×	78 0.4×	77 0.7×	92 1.1×	134	1.8k
Xijiong Xie China	19	825 1.0×	1.1k 1.5×	52 0.3×	95 0.8×	71 0.9×	50	1.6k
Diyar Qader Zeebaree Iraq	14	646 0.8×	360 0.5×	75 0.4×	83 0.7×	115 1.4×	25	1.3k
Ya Li China	13	571 0.7×	372 0.5×	148 0.8×	87 0.7×	70 0.9×	44	1.1k
Bin Gu China	8	414 0.5×	330 0.5×	129 0.7×	61 0.5×	47 0.6×	10	1000

Countries citing papers authored by Chun-Nam Yu

Since Specialization

Citations

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

Fields of papers citing papers by Chun-Nam Yu

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Chun-Nam Yu. 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 Chun-Nam Yu. The network helps show where Chun-Nam Yu may publish in the future.

Co-authorship network of co-authors of Chun-Nam Yu

This figure shows the co-authorship network connecting the top 25 collaborators of Chun-Nam Yu. A scholar is included among the top collaborators of Chun-Nam Yu 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 Chun-Nam Yu. Chun-Nam Yu 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:

16 of 16 papers shown

1.

Shah, Shreyas, Chun-Nam Yu, Mingde Zheng, Heejong Kim, & Michael S. Eggleston. (2021). Microparticle-Based Biochemical Sensing Using Optical Coherence Tomography and Deep Learning. ACS Nano. 15(6). 9764–9774. 12 indexed citations

2.

Kim, Heejong, Chun-Nam Yu, William G. Kennedy, Michael S. Eggleston, & Shreyas Shah. (2020). Automated Monitoring for Optical Coherence Tomography-based Biosensing Using Deep Learning. 115. 1–2. 1 indexed citations

3.

Olson, Matthew, Lisa Zhang, & Chun-Nam Yu. (2019). ADAPTING PRETRAINED LANGUAGE MODELS FOR LONG DOCUMENT CLASSIFICATION.

4.

Bejerano, Yigal, et al.. (2019). <italic>DyMo</italic>: Dynamic Monitoring of Large-Scale LTE-Multicast Systems. IEEE/ACM Transactions on Networking. 27(1). 258–271. 2 indexed citations

5.

Yu, Chun-Nam, et al.. (2017). Convolutional Neural Networks for Figure Extraction in Historical Technical Documents. 23. 789–795. 2 indexed citations

6.

Bejerano, Yigal, et al.. (2017). DyMo: Dynamic monitoring of large scale LTE-Multicast systems. 15. 1–9. 5 indexed citations

7.

Yu, Chun-Nam, Piotr Mirowski, & Tin Kam Ho. (2016). A Sparse Coding Approach to Household Electricity Demand Forecasting in Smart Grids. IEEE Transactions on Smart Grid. 1–11. 115 indexed citations

8.

Yu, Chun-Nam, et al.. (2014). Robust Learning under Uncertain Test Distributions: Relating Covariate Shift to Model Misspecification. 631–639. 21 indexed citations

9.

Mirowski, Piotr, et al.. (2014). Demand Forecasting in Smart Grids. Bell Labs Technical Journal. 18(4). 135–158. 69 indexed citations

10.

Yu, Chun-Nam. (2012). Transductive Learning of Structural SVMs via Prior Knowledge Constraints. International Conference on Artificial Intelligence and Statistics. 1367–1376. 2 indexed citations

11.

Lin, Hsiu-Chin, Vickie E. Baracos, Russell Greiner, & Chun-Nam Yu. (2011). Learning Patient-Specific Cancer Survival Distributions as a Sequence of Dependent Regressors. Neural Information Processing Systems. 24. 1845–1853. 81 indexed citations

12.

Joachims, Thorsten & Chun-Nam Yu. (2009). Sparse kernel SVMs via cutting-plane training. Machine Learning. 76(2-3). 179–193. 83 indexed citations

13.

Joachims, Thorsten, Thomas Hofmann, Yisong Yue, & Chun-Nam Yu. (2009). Predicting structured objects with support vector machines. Communications of the ACM. 52(11). 97–104. 51 indexed citations

14.

Joachims, Thorsten, Thomas Finley, & Chun-Nam Yu. (2009). Cutting-plane training of structural SVMs. Machine Learning. 77(1). 27–59. 607 indexed citations breakdown →

15.

Yu, Chun-Nam & Thorsten Joachims. (2009). Learning structural SVMs with latent variables. 1169–1176. 390 indexed citations breakdown →

16.

Yu, Chun-Nam & Thorsten Joachims. (2008). Training structural svms with kernels using sampled cuts. 794–802. 14 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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