Yale Chang

1.4k citations
20 papers · 597 · h-index 11

Impact in

Papers in

Yale Chang

18 papers receiving 588 citations

Peers

Yale Chang
Comparison fields: 5 of 114
  • Cognitive Neuroscience 171
  • Experimental and Cognitive Psychology 103
  • Cardiology and Cardiovascular Medicine 128
  • Social Psychology 106
  • Health Informatics 6
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Citations per year

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-authors

The 25 scholars most cited alongside Yale Chang, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Yale Chang Line = papers co-authored together Yale Chang links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown
#Work
1 2018250
2 202089
3
A Robust-Equitable Measure for Feature Ranking and Selection
201754
4 201639
5 201424
6 202120
7 201620
8 202119
9 202219
10 201615
11
A Robust-Equitable Copula Dependence Measure for Feature Selection
201612
12 201710
13 20199
14
Multiple Clustering Views from Multiple Uncertain Experts
20175
15 20225
16
Solving Interpretable Kernel Dimensionality Reduction
20195
17
Phenotyping with Prior Knowledge using Patient Similarity
20201
18 20201
19
Clustering from Multiple Uncertain Experts
20170
20 20260

About Yale Chang

Yale Chang is a scholar working on Artificial Intelligence, Pulmonary and Respiratory Medicine, Computer Vision and Pattern Recognition, Cardiology and Cardiovascular Medicine and Cognitive Neuroscience, having authored 20 papers that have together received 597 indexed citations. Recurring topics across this work include Advanced Clustering Algorithms Research (3 papers), Face and Expression Recognition (3 papers), Chronic Obstructive Pulmonary Disease (COPD) Research (3 papers), Machine Learning in Healthcare (2 papers), Data Management and Algorithms (2 papers), Statistical Methods and Inference (2 papers), Sepsis Diagnosis and Treatment (2 papers) and Machine Learning and Data Classification (2 papers). The work is most often cited by research in Cognitive Neuroscience (171 citations), Experimental and Cognitive Psychology (103 citations), Cardiology and Cardiovascular Medicine (128 citations), Social Psychology (106 citations) and Health Informatics (6 citations). Yale Chang has collaborated with scholars based in United States, Germany and Finland. Frequent co-authors include Jennifer Dy, Molly Sands, Wim Van Den Noortgate, Karen S. Quigley, Lisa Feldman Barrett, Paul Condon, Erika Siegel, Yi Li, A. Adam Ding and Gregory Boverman. Their work appears in journals such as Critical Care, Chronic Obstructive Pulmonary Diseases Journal of the COPD Foundation, Respiratory Medicine, Journal of Machine Learning Research and Frontiers in Cardiovascular Medicine.

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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