Yu-Yen Chang

3.2k total citations
20 papers, 235 citations indexed

About

Yu-Yen Chang is a scholar working on Astronomy and Astrophysics, Instrumentation and Molecular Biology. According to data from OpenAlex, Yu-Yen Chang has authored 20 papers receiving a total of 235 indexed citations (citations by other indexed papers that have themselves been cited), including 15 papers in Astronomy and Astrophysics, 10 papers in Instrumentation and 2 papers in Molecular Biology. Recurrent topics in Yu-Yen Chang's work include Galaxies: Formation, Evolution, Phenomena (15 papers), Astronomy and Astrophysical Research (10 papers) and Astrophysics and Star Formation Studies (6 papers). Yu-Yen Chang is often cited by papers focused on Galaxies: Formation, Evolution, Phenomena (15 papers), Astronomy and Astrophysical Research (10 papers) and Astrophysics and Star Formation Studies (6 papers). Yu-Yen Chang collaborates with scholars based in Taiwan, Japan and United States. Yu-Yen Chang's co-authors include Chang‐Hsien Yang, Hsing‐Fun Hsu, Wen‐Ping Hsieh, Wei-Hao Wang, Chen-Fatt Lim, Yoshiki Toba, David C. Koo, Haowen Zhang, Yicheng Guo and Arjen van der Wel and has published in prestigious journals such as The Astrophysical Journal, Analytical Chemistry and Monthly Notices of the Royal Astronomical Society.

In The Last Decade

Yu-Yen Chang

20 papers receiving 222 citations

Peers

Yu-Yen Chang

INSTR

NHEP

Zhiyuan Ji United States

M. Á. Gómez-Flechoso Spain

Klaus Fricke Germany

C. E. Ferreira Lopes Brazil

Ana-Roxana Pop United States

K. Iwamoto Japan

E. Balbinot United Kingdom

Duncan Austin United Kingdom

Markus Buchhorn Australia

Billy Quarles United States

Zhiyuan Ji United States

Yu-Yen Chang

296 ×1.7

137 ×1.7

INSTR

30 ×0.7

22 ×0.5

22 ×0.7

NHEP

Citations per year, relative to Yu-Yen Chang Yu-Yen Chang (= 1×) peers Zhiyuan Ji

Countries citing papers authored by Yu-Yen Chang

Since Specialization

Citations

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

Fields of papers citing papers by Yu-Yen Chang

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Yu-Yen Chang

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

All Works

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20 of 20 papers shown

Nagar, Neil M., et al.. (2024). Merging galaxies in isolated environments. Astronomy and Astrophysics. 686. A151–A151. 2 indexed citations

Jian, Hung-Yu, Lihwai Lin, Bau-Ching Hsieh, et al.. (2023). Radial and Local Density Dependence of Star Formation Properties in Galaxy Clusters from the Hyper Suprime-Cam Survey. The Astrophysical Journal. 957(2). 85–85. 2 indexed citations

Lee, Chia‐Yi, Yu-Yen Chang, Yen-Ling Liu, et al.. (2023). A Low-Cost, Sensitive Reporter System Using Membrane-Tethered Horseradish Peroxidase for Efficient Gene Expression Analysis. Analytical Chemistry. 95(38). 14341–14349. 1 indexed citations

Jian, Hung-Yu, Lihwai Lin, Bau-Ching Hsieh, et al.. (2022). Star Formation Properties of Sloan Digital Sky Survey BOSS Void Galaxies in the Hyper Suprime-Cam Survey. The Astrophysical Journal. 926(2). 115–115. 6 indexed citations

Lin, Yen‐Ting, et al.. (2022). A pair of early- and late-forming galaxy cluster samples: A novel way of studying halo assembly bias assisted by a constrained simulation. Astronomy and Astrophysics. 666. A97–A97. 4 indexed citations

Chang, Yu-Yen, Lihwai Lin, Hsi-An Pan, et al.. (2022). SDSS-IV MaNGA: Unveiling Galaxy Interaction by Merger Stages with Machine Learning. The Astrophysical Journal. 937(2). 97–97. 7 indexed citations

Shu, Xinwen, Lei Yang, Daizhong Liu, et al.. (2022). A Census of Optically Dark Massive Galaxies in the Early Universe from Magnification by Lensing Galaxy Clusters. The Astrophysical Journal. 926(2). 155–155. 12 indexed citations

Wang, Wei-Hao, Yu-Yen Chang, Chen-Fatt Lim, et al.. (2021). Revisiting the Color–Color Selection: Submillimeter and AGN Properties of NUV–r–J Selected Quiescent Galaxies. The Astrophysical Journal. 913(1). 6–6. 5 indexed citations

Shibuya, Takatoshi, Noriaki Miura, Yu-Yen Chang, et al.. (2020). A machine learning software to estimate morphological parameters of distant galaxies. Research at the University of Copenhagen (University of Copenhagen). 86–86. 1 indexed citations

10.

Aoyama, Shohei, Hiroyuki Hirashita, Chen-Fatt Lim, et al.. (2019). Comparison of cosmological simulations and deep submillimetre galaxy surveys. Monthly Notices of the Royal Astronomical Society. 484(2). 1852–1864. 21 indexed citations

11.

Barišić, Ivana, Arjen van der Wel, Josha van Houdt, et al.. (2019). An Absence of Radio-loud Active Galactic Nuclei in Geometrically Flat Quiescent Galaxies: Implications for Maintenance-mode Feedback Models. The Astrophysical Journal Letters. 872(1). L12–L12. 6 indexed citations

12.

Zhang, Haowen, Joel R. Primack, S. M. Faber, et al.. (2019). The evolution of galaxy shapes in CANDELS: from prolate to discy. Monthly Notices of the Royal Astronomical Society. 484(4). 5170–5191. 44 indexed citations

13.

Shu, Xinwen, Yongquan Xue, Daizhong Liu, et al.. (2018). A unique distant submillimeter galaxy with an X-ray-obscured radio-luminous active galactic nucleus. Astronomy and Astrophysics. 619. A76–A76. 2 indexed citations

14.

Toba, Yoshiki, Yoshihiro Ueda, Kenta Matsuoka, et al.. (2018). Does the mid-infrared–hard X-ray luminosity relation for active galactic nuclei depend on Eddington ratio?. Monthly Notices of the Royal Astronomical Society. 484(1). 196–203. 15 indexed citations

15.

Toba, Yoshiki, Junko Ueda, Chen-Fatt Lim, et al.. (2018). Discovery of an Extremely Luminous Dust-obscured Galaxy Observed with SDSS, WISE, JCMT, and SMA. The Astrophysical Journal. 857(1). 31–31. 9 indexed citations

16.

Gou, Shih-Chuan, Lock Yue Chew, Yu-Yen Chang, et al.. (2017). Setting a disordered password on a photonic memory. Physical review. A. 95(6). 9 indexed citations

17.

Chang, Yu-Yen, E. Le Floc’h, S. Juneau, et al.. (2017). Infrared Selection of Obscured Active Galactic Nuclei in the COSMOS Field. The Astrophysical Journal Supplement Series. 233(2). 19–19. 27 indexed citations

18.

Chang, Yu-Yen, E. Le Floc’h, S. Juneau, et al.. (2016). Obscured active galactic nuclei triggered in compact star-forming galaxies. Monthly Notices of the Royal Astronomical Society Letters. 466(1). L103–L107. 13 indexed citations

19.

Chang, Yu-Yen, et al.. (2012). A Principle Component Analysis of Galaxy Properties from a Large, Gas-Selected Sample. Advances in Astronomy. 2012. 1–11. 2 indexed citations

20.

Hsu, Hsing‐Fun, et al.. (2010). C/D Class MADS Box Genes from Two Monocots, Orchid (Oncidium Gower Ramsey) and Lily (Lilium longiflorum), Exhibit Different Effects on Floral Transition and Formation in Arabidopsis thaliana. Plant and Cell Physiology. 51(6). 1029–1045. 47 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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