Chun‐Chen Tu

793 total citations
5 papers, 205 citations indexed

About

Chun‐Chen Tu is a scholar working on Artificial Intelligence, Computer Networks and Communications and Information Systems. According to data from OpenAlex, Chun‐Chen Tu has authored 5 papers receiving a total of 205 indexed citations (citations by other indexed papers that have themselves been cited), including 4 papers in Artificial Intelligence, 1 paper in Computer Networks and Communications and 1 paper in Information Systems. Recurrent topics in Chun‐Chen Tu's work include Adversarial Robustness in Machine Learning (2 papers), Statistical Methods and Bayesian Inference (1 paper) and Complex Network Analysis Techniques (1 paper). Chun‐Chen Tu is often cited by papers focused on Adversarial Robustness in Machine Learning (2 papers), Statistical Methods and Bayesian Inference (1 paper) and Complex Network Analysis Techniques (1 paper). Chun‐Chen Tu collaborates with scholars based in United States, Taiwan and France. Chun‐Chen Tu's co-authors include Pin‐Yu Chen, Paishun Ting, Shin‐Ming Cheng, Jinfeng Yi, Cho‐Jui Hsieh, Huan Zhang, Sijia Liu, Naisyin Wang, Florence Forbes and Danai Koutra and has published in prestigious journals such as IEEE Access, Journal of the Royal Statistical Society Series C (Applied Statistics) and IEEE Transactions on Signal and Information Processing over Networks.

In The Last Decade

Chun‐Chen Tu

5 papers receiving 204 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Chun‐Chen Tu United States	4	188	51	39	27	22	5	205
Elan Rosenfeld United States	3	206 1.1×	48 0.9×	30 0.8×	21 0.8×	10 0.5×	3	224
Aurko Roy United States	5	222 1.2×	85 1.7×	45 1.2×	36 1.3×	15 0.7×	7	267
Xiaojun Jia China	7	237 1.3×	116 2.3×	50 1.3×	22 0.8×	16 0.7×	23	291
Jacob Buckman United States	3	184 1.0×	52 1.0×	42 1.1×	35 1.3×	15 0.7×	3	208
Jinmian Ye China	6	91 0.5×	79 1.5×	13 0.3×	7 0.3×	19 0.9×	7	160
Mohammad Samragh United States	9	170 0.9×	56 1.1×	27 0.7×	14 0.5×	49 2.2×	23	264
Vadim Sheinin United States	7	95 0.5×	63 1.2×	25 0.6×	8 0.3×	9 0.4×	28	174
Juliane Krämer Germany	7	106 0.6×	29 0.6×	28 0.7×	6 0.2×	65 3.0×	22	212
Valentin Khrulkov Russia	5	95 0.5×	76 1.5×	15 0.4×	18 0.7×	8 0.4×	9	150
Samson Zhou United States	6	64 0.3×	19 0.4×	28 0.7×	23 0.9×	8 0.4×	23	123

Countries citing papers authored by Chun‐Chen Tu

Since Specialization

Citations

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

Fields of papers citing papers by Chun‐Chen Tu

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Chun‐Chen Tu. 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‐Chen Tu. The network helps show where Chun‐Chen Tu may publish in the future.

Co-authorship network of co-authors of Chun‐Chen Tu

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

All Works

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5 of 5 papers shown

1.

Tu, Chun‐Chen, Pin‐Yu Chen, & Naisyin Wang. (2019). Improving Prediction Efficacy Through Abnormality Detection and Data Preprocessing. IEEE Access. 7. 103794–103805. 3 indexed citations

2.

Tu, Chun‐Chen, Paishun Ting, Pin‐Yu Chen, et al.. (2019). AutoZOOM: Autoencoder-Based Zeroth Order Optimization Method for Attacking Black-Box Neural Networks. Proceedings of the AAAI Conference on Artificial Intelligence. 33(1). 742–749. 192 indexed citations

3.

Tu, Chun‐Chen, Florence Forbes, Benjamin Lemasson, & Naisyin Wang. (2019). Prediction with High Dimensional Regression Via Hierarchically Structured Gaussian Mixtures and Latent Variables. Journal of the Royal Statistical Society Series C (Applied Statistics). 68(5). 1485–1507. 3 indexed citations

4.

Chen, Pin‐Yu, et al.. (2018). Identifying Influential Links for Event Propagation on Twitter: A Network of Networks Approach. IEEE Transactions on Signal and Information Processing over Networks. 5(1). 139–151. 4 indexed citations

5.

Ting, Paishun, et al.. (2017). FEAST: An Automated Feature Selection Framework for Compilation Tasks. 44. 1138–1145. 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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