Buru Chang

834 citations
21 papers · 506 · h-index 9

Impact in

    • Human Mobility and Location-Based Analysis
    • Domain Adaptation and Few-Shot Learning
    • Advanced Graph Neural Networks
    • Imbalanced Data Classification Techniques
    • Topic Modeling
    • Machine Learning and Data Classification

Papers in

Buru Chang

21 papers receiving 488 citations

Peers

Buru Chang
Comparison fields: 5 of 77
  • Transportation 149
  • Artificial Intelligence 275
  • Information Systems 163
  • Computer Vision and Pattern Recognition 134
  • Signal Processing 61
Replace Chenyi Zhuang with:
Chenyi Zhuang Japan
Yongji Wu China
Lei Wu China
Arti Arya India
Jinyang Gao China
X. D. Zhang United States
Anto Satriyo Nugroho Indonesia
Rikio Onai Japan
Martin Mladenov Germany
Buru Chang relative to Chenyi Zhuang Japan Chenyi Zhuang's profile →
Citations per field
00.5×2.8×
Chenyi Zhuang · 1×
Citations per year

Countries citing papers authored by Buru Chang

Since Specialization
Citations

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

Fields of papers citing papers by Buru Chang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside Buru 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 Buru Chang Line = papers co-authored together Buru Chang links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 21 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2021164
2 2018144
3 202068
4 201925
5 202215
6 201814
7 202213
8 202211
9 20239
10 20207
11 20246
12 20186
13 20225
14 20235
15 20234
16 20214
17 20222
18 20251
19 20241
20 20251

About Buru Chang

Buru Chang is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition, Information Systems, Social Psychology and Experimental and Cognitive Psychology, having authored 21 papers that have together received 506 indexed citations. Recurring topics across this work include Topic Modeling (8 papers), Recommender Systems and Techniques (4 papers), Speech and dialogue systems (3 papers), Natural Language Processing Techniques (3 papers), Multimodal Machine Learning Applications (3 papers), Human Mobility and Location-Based Analysis (2 papers), Domain Adaptation and Few-Shot Learning (2 papers) and Sentiment Analysis and Opinion Mining (2 papers). The work is most often cited by research in Transportation (149 citations), Artificial Intelligence (275 citations), Information Systems (163 citations), Computer Vision and Pattern Recognition (134 citations) and Signal Processing (61 citations). Buru Chang has collaborated with scholars based in South Korea and United States. Frequent co-authors include Jaewoo Kang, Kwanghee Choi, Yong Gyu Park, Seongsoon Kim, Beomsu Kim, Seungju Han, Seokjun Seo, Martin Kersner, Jacob Morton and Byung‐Joo Ham. Their work appears in journals such as Information Sciences, Genes, Lecture notes in computer science, Designing Interactive Systems Conference and Society for Industrial and Applied Mathematics eBooks.

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.

Explore authors with similar magnitude of impact