Won Chang

2.3k total citations
60 papers, 1.5k citations indexed

About

Won Chang is a scholar working on Atmospheric Science, Environmental Engineering and Atomic and Molecular Physics, and Optics. According to data from OpenAlex, Won Chang has authored 60 papers receiving a total of 1.5k indexed citations (citations by other indexed papers that have themselves been cited), including 12 papers in Atmospheric Science, 10 papers in Environmental Engineering and 8 papers in Atomic and Molecular Physics, and Optics. Recurrent topics in Won Chang's work include Cryospheric studies and observations (8 papers), Global trade and economics (6 papers) and Soil Geostatistics and Mapping (5 papers). Won Chang is often cited by papers focused on Cryospheric studies and observations (8 papers), Global trade and economics (6 papers) and Soil Geostatistics and Mapping (5 papers). Won Chang collaborates with scholars based in United States, South Korea and United Kingdom. Won Chang's co-authors include L. Alan Winters, Jesper Nygård, Thomas Sand Jespersen, Ferdinand Kuemmeth, Peter Krogstrup, S. M. Albrecht, C. M. Marcus, Murali Haran, Vladimir Manucharyan and C. M. Marcus and has published in prestigious journals such as Physical Review Letters, Nature Communications and Journal of the American Statistical Association.

In The Last Decade

Won Chang

56 papers receiving 1.4k citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Won Chang United States	18	631	358	238	234	203	60	1.5k
L. De Cesare Italy	15	390 0.6×	520 1.5×	58 0.2×	77 0.3×	91 0.4×	126	1.3k
S. S. Manna India	20	80 0.1×	1.2k 3.3×	62 0.3×	303 1.3×	86 0.4×	98	2.3k
Heiko Leschhorn Germany	14	154 0.2×	764 2.1×	47 0.2×	298 1.3×	69 0.3×	22	1.7k
Vidar Frette Norway	16	72 0.1×	372 1.0×	74 0.3×	162 0.7×	134 0.7×	38	1.3k
Ofer Malcai Israel	12	51 0.1×	224 0.6×	56 0.2×	103 0.4×	76 0.4×	21	1.1k
Borko Stošić Brazil	21	64 0.1×	128 0.4×	67 0.3×	33 0.1×	284 1.4×	114	1.5k
Gilney Figueira Zebende Brazil	26	47 0.1×	225 0.6×	35 0.1×	37 0.2×	199 1.0×	83	2.3k
Takashi Hayashi Japan	16	269 0.4×	153 0.4×	13 0.1×	511 2.2×	25 0.1×	77	1.3k
Jakob Rhyner Switzerland	25	151 0.2×	814 2.3×	154 0.6×	296 1.3×	304 1.5×	60	2.0k
Ashvin B. Chhabra United States	12	109 0.2×	312 0.9×	62 0.3×	119 0.5×	177 0.9×	20	1.6k

Countries citing papers authored by Won Chang

Since Specialization

Citations

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

Fields of papers citing papers by Won Chang

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Won Chang

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

Huang, Ailing, et al.. (2025). Investigating the self-discharge mechanism in electrical double-layer capacitors with low-concentration carbonate additives. Journal of Energy Storage. 115. 115863–115863. 2 indexed citations

Cox, N. M., M.R. Williams, G. Jones, et al.. (2025). A role for Myo-II zipper and spaghetti squash in Gliotactin-dependent Drosophila melanogaster wing hair planar cell polarity. PLoS ONE. 20(7). e0328970–e0328970. 1 indexed citations

Hwang, Youngdeok, et al.. (2025). Bayesian Model Calibration and Sensitivity Analysis for Oscillating Biological Experiments. Technometrics. 67(2). 333–343. 1 indexed citations

Park, Yeonhee & Won Chang. (2024). A Personalized Dose‐Finding Algorithm Based on Adaptive Gaussian Process Regression. Pharmaceutical Statistics. 23(6). 1181–1205.

Wang, Shixuan, et al.. (2024). Nonparametric predictive model for sparse and irregular longitudinal data. Biometrics. 80(1).

Datta, Abhirup, Christopher K. Wikle, Edward L. Boone, et al.. (2024). Assessing predictability of environmental time series with statistical and machine learning models. Environmetrics. 36(1). 5 indexed citations

Kim, Hyun, et al.. (2024). scLENS: data-driven signal detection for unbiased scRNA-seq data analysis. Nature Communications. 15(1). 3575–3575. 5 indexed citations

Chang, Won, et al.. (2024). A Bayesian convolutional neural network-based generalized linear model. Biometrics. 80(2). 2 indexed citations

Xie, Juan, et al.. (2023). Statistical Power Analysis for Designing Bulk, Single-Cell, and Spatial Transcriptomics Experiments: Review, Tutorial, and Perspectives. Biomolecules. 13(2). 221–221. 16 indexed citations

10.

Yilmaz, Ayse Selen, et al.. (2023). graph-GPA 2.0: improving multi-disease genetic analysis with integration of functional annotation data. Frontiers in Genetics. 14. 1079198–1079198. 2 indexed citations

11.

Chang, Won, et al.. (2023). Remote sensing-enabled machine learning for river water quality modeling under multidimensional uncertainty. The Science of The Total Environment. 898. 165504–165504. 35 indexed citations

12.

Wikle, Christopher K., Abhirup Datta, Edward L. Boone, et al.. (2022). An illustration of model agnostic explainability methods applied to environmental data. Environmetrics. 34(1). 12 indexed citations

13.

Wang, Jiali, Zhengchun Liu, Ian Foster, et al.. (2021). Fast and accurate learned multiresolution dynamical downscaling for precipitation. Geoscientific model development. 14(10). 6355–6372. 41 indexed citations

14.

Park, Jaewoo, Won Chang, & Boseung Choi. (2021). An interaction Neyman–Scott point process model for coronavirus disease-19. Spatial Statistics. 47. 100561–100561. 6 indexed citations

15.

Olson, Roman, Soon‐Il An, Yanan Fan, et al.. (2019). A novel method to test non-exclusive hypotheses applied to Arctic ice projections from dependent models. Nature Communications. 10(1). 3016–3016. 7 indexed citations

16.

Chang, Won, Murali Haran, P. J. Applegate, & David Pollard. (2015). Calibrating an ice sheet model using high-dimensional non-Gaussian spatial data. arXiv (Cornell University). 1 indexed citations

17.

Chang, Won, S. M. Albrecht, Thomas Sand Jespersen, et al.. (2014). Hard Gap in Epitaxial Superconductor-Semiconductor Nanowires. arXiv (Cornell University). 1 indexed citations

18.

Chang, Won, P. J. Applegate, Murali Haran, & Klaus Keller. (2014). Probabilistic calibration of a Greenland Ice Sheet model using spatially resolved synthetic observations: toward projections of ice mass loss with uncertainties. Geoscientific model development. 7(5). 1933–1943. 19 indexed citations

19.

Chang, Won, Vladimir Manucharyan, Thomas Sand Jespersen, Jesper Nygård, & C. M. Marcus. (2013). Tunneling Spectroscopy of Quasiparticle Bound States in a Spinful Josephson Junction. Physical Review Letters. 110(21). 217005–217005. 140 indexed citations

20.

Schiff, Maurice & Won Chang. (2001). Market Presence, Contestability, and the Terms-of-Trade Effects of Regional Integration. SSRN Electronic Journal. 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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