Yale Chang

1.3k total citations
19 papers, 578 citations indexed

About

Yale Chang is a scholar working on Artificial Intelligence, Pulmonary and Respiratory Medicine and Cardiology and Cardiovascular Medicine. According to data from OpenAlex, Yale Chang has authored 19 papers receiving a total of 578 indexed citations (citations by other indexed papers that have themselves been cited), including 8 papers in Artificial Intelligence, 5 papers in Pulmonary and Respiratory Medicine and 4 papers in Cardiology and Cardiovascular Medicine. Recurrent topics in Yale Chang's work include Chronic Obstructive Pulmonary Disease (COPD) Research (3 papers), Face and Expression Recognition (3 papers) and Asthma and respiratory diseases (3 papers). Yale Chang is often cited by papers focused on Chronic Obstructive Pulmonary Disease (COPD) Research (3 papers), Face and Expression Recognition (3 papers) and Asthma and respiratory diseases (3 papers). Yale Chang collaborates with scholars based in United States, Mexico and Germany. Yale Chang's co-authors include Jennifer Dy, Paul Condon, Erika Siegel, Wim Van Den Noortgate, Molly Sands, Lisa Feldman Barrett, Karen S. Quigley, Yi Li, A. Adam Ding and Gregory Boverman and has published in prestigious journals such as Psychological Bulletin, IEEE Transactions on Medical Imaging and Critical Care.

In The Last Decade

Yale Chang

18 papers receiving 568 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Yale Chang United States	11	176	132	109	107	97	19	578
Asma Ghandeharioun United States	14	92 0.5×	177 1.3×	211 1.9×	88 0.8×	34 0.4×	20	854
Luísa Castro Portugal	16	53 0.3×	99 0.8×	26 0.2×	129 1.2×	54 0.6×	62	993
Michelle Liou Taiwan	16	284 1.6×	34 0.3×	71 0.7×	33 0.3×	41 0.4×	66	749
Bryan Conroy United States	14	763 4.3×	263 2.0×	65 0.6×	43 0.4×	206 2.1×	26	1.2k
Derek Beaton Canada	14	123 0.7×	53 0.4×	48 0.4×	23 0.2×	85 0.9×	55	579
Michael Oliver United States	15	373 2.1×	59 0.4×	60 0.6×	71 0.7×	11 0.1×	35	855
Berk Ustun United States	12	186 1.1×	28 0.2×	68 0.6×	41 0.4×	31 0.3×	25	903
Stanisław Saganowski Poland	11	121 0.7×	62 0.5×	201 1.8×	73 0.7×	6 0.1×	28	573
Aize Cao United States	16	196 1.1×	35 0.3×	43 0.4×	25 0.2×	29 0.3×	34	737
Markus Nagler Germany	11	340 1.9×	81 0.6×	167 1.5×	45 0.4×	18 0.2×	34	712

Countries citing papers authored by Yale Chang

Since Specialization

Citations

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

Fields of papers citing papers by Yale Chang

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Yale Chang

This figure shows the co-authorship network connecting the top 25 collaborators of Yale Chang. A scholar is included among the top collaborators of Yale 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 Yale Chang. Yale 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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19 of 19 papers shown

Chang, Yale, Junzi Dong, Mingyu Lu, et al.. (2022). Early Prediction of Cardiogenic Shock Using Machine Learning. Frontiers in Cardiovascular Medicine. 9. 17 indexed citations

Boueiz, Adel, Zhonghui Xu, Yale Chang, et al.. (2022). Machine Learning Prediction of Progression in Forced Expiratory Volume in 1 Second in the COPDGene® Study. Chronic Obstructive Pulmonary Diseases Journal of the COPD Foundation. 9(3). 349–365. 4 indexed citations

Schwager, Emma, Sonja Schiffer, Yale Chang, et al.. (2021). Utilizing machine learning to improve clinical trial design for acute respiratory distress syndrome. npj Digital Medicine. 4(1). 133–133. 19 indexed citations

Rahman, Asif, Yale Chang, Junzi Dong, et al.. (2021). Early prediction of hemodynamic interventions in the intensive care unit using machine learning. Critical Care. 25(1). 388–388. 17 indexed citations

Rahman, Asif, Yale Chang, Bryan Conroy, & Minnan Xu-Wilson. (2020). Phenotyping with Prior Knowledge using Patient Similarity. 331–351. 1 indexed citations

Natarajan, Annamalai, Yale Chang, Sara Mariani, et al.. (2020). A Wide and Deep Transformer Neural Network for 12-Lead ECG Classification. Computing in cardiology. 47. 87 indexed citations

Parvaneh, Saman & Yale Chang. (2020). Shapelet Discovery for Atrial Fibrillation Detection. Computing in cardiology. 1 indexed citations

Miller, Jared, et al.. (2019). Solving Interpretable Kernel Dimensionality Reduction. arXiv (Cornell University). 32. 7913–7923. 5 indexed citations

Chang, Yale, Jonathan M. Rubin, Gregory Boverman, et al.. (2019). A Multi-Task Imputation and Classification Neural Architecture for Early Prediction of Sepsis from Multivariate Clinical Time Series. Computing in cardiology. 9 indexed citations

10.

Siegel, Erika, Molly Sands, Wim Van Den Noortgate, et al.. (2018). Emotion fingerprints or emotion populations? A meta-analytic investigation of autonomic features of emotion categories.. Psychological Bulletin. 144(4). 343–393. 244 indexed citations

11.

Chang, Yale, Junxiang Chen, Michael H. Cho, et al.. (2017). Clustering from Multiple Uncertain Experts. International Conference on Artificial Intelligence and Statistics. 28–36.

12.

Ding, A. Adam, Jennifer Dy, Yi Li, & Yale Chang. (2017). A Robust-Equitable Measure for Feature Ranking and Selection. Journal of Machine Learning Research. 18(71). 1–46. 53 indexed citations

13.

Chang, Yale, Junxiang Chen, Michael H. Cho, et al.. (2017). Multiple Clustering Views from Multiple Uncertain Experts. International Conference on Machine Learning. 674–683. 3 indexed citations

14.

Chang, Yale & Jennifer Dy. (2017). Informative Subspace Learning for Counterfactual Inference. Proceedings of the AAAI Conference on Artificial Intelligence. 31(1). 10 indexed citations

15.

Chang, Yale, Yi Li, A. Adam Ding, & Jennifer Dy. (2016). A Robust-Equitable Copula Dependence Measure for Feature Selection. International Conference on Artificial Intelligence and Statistics. 84–92. 12 indexed citations

16.

Chang, Yale, Kimberly Glass, Yang‐Yu Liu, et al.. (2016). COPD subtypes identified by network-based clustering of blood gene expression. Genomics. 107(2-3). 51–58. 39 indexed citations

17.

Chen, Junxiang, Yale Chang, Brian D. Hobbs, et al.. (2016). Interpretable Clustering via Discriminative Rectangle Mixture Model. 823–828. 18 indexed citations

18.

Ross, James C., Peter J. Castaldi, Michael H. Cho, et al.. (2016). A Bayesian Nonparametric Model for Disease Subtyping: Application to Emphysema Phenotypes. IEEE Transactions on Medical Imaging. 36(1). 343–354. 15 indexed citations

19.

Lee, Jin Hwa, Michael H. Cho, Merry‐Lynn McDonald, et al.. (2014). Phenotypic and genetic heterogeneity among subjects with mild airflow obstruction in COPDGene. Respiratory Medicine. 108(10). 1469–1480. 24 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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