Ming Sun
- Signal Processing top 2%
- Music and Audio Processing 18
- Speech and Audio Processing 16
- Neurology top 5%
-
- Advanced Neural Network Applications 8
- Music Technology and Sound Studies 5
- Cancer Research top 10%
- Artificial Intelligence top 5%
- Speech Recognition and Synthesis 12
- Topic Modeling 5
- Domain Adaptation and Few-Shot Learning 5
-
- Retinal Development and Disorders 5
- Co-authors
- Donna B. StolzSpyros MatsoukasJunjie YanChieh-Chi KaoShiv VitaladevuniChao WangSankaran PanchapagesanMaria Chikina
- Journals
- Communications Biology (3 papers)Scientific Reports (2 papers)Nature Communications (2 papers)
- Partner nations
- United StatesChinaGermany
In The Last Decade
Ming Sun
83 papers receiving 1.8k citations
Peers
Comparison fields: 5 of 152
- Signal Processing 290
- Neurology 181
- Computer Vision and Pattern Recognition 269
- Cancer Research 188
- Artificial Intelligence 394
Countries citing papers authored by Ming Sun
This map shows the geographic impact of Ming Sun'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 Ming Sun with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ming Sun more than expected).
Fields of papers citing papers by Ming Sun
This network shows the impact of papers produced by Ming Sun. 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 Ming Sun. The network helps show where Ming Sun may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Ming Sun, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2025 | 0 | |
| 2 | 2025 | 1 | |
| 3 | 2025 | 1 | |
| 4 | 2024 | 6 | |
| 5 | 2024 | 1 | |
| 6 | 2023 | 5 | |
| 7 | 2022 | 9 | |
| 8 | 2021 | 7 | |
| 9 | 2021 | 1 | |
| 10 | Improving Auto-Augment via Augmentation-Wise Weight Sharing | 2020 | 2 |
| 11 | 2020 | 20 | |
| 12 | Efficient Neural Architecture Transformation Search in Channel-Level for Object Detection | 2019 | 22 |
| 13 | Traffic Anomaly Detection via Perspective Map based on Spatial-temporal Information Matrix | 2019 | 20 |
| 14 | 2018 | 35 | |
| 15 | 2016 | 89 | |
| 16 | AppDialogue: Multi-App Dialogues for Intelligent Assistants. | 2016 | 1 |
| 17 | 2014 | 44 | |
| 18 | 2014 | 48 | |
| 19 | 2010 | 3 | |
| 20 | 2010 | 22 |
About Ming Sun
Ming Sun is a scholar working on Signal Processing, Computer Vision and Pattern Recognition, Artificial Intelligence, Neurology and Ophthalmology, having authored 89 papers that have together received 1.9k indexed citations. Recurring topics across this work include Music and Audio Processing (18 papers), Speech and Audio Processing (16 papers), Speech Recognition and Synthesis (12 papers), Advanced Neural Network Applications (8 papers), Topic Modeling (5 papers), Music Technology and Sound Studies (5 papers), Domain Adaptation and Few-Shot Learning (5 papers) and Retinal Development and Disorders (5 papers). The work is most often cited by research in Signal Processing (290 citations), Neurology (181 citations), Computer Vision and Pattern Recognition (269 citations), Cancer Research (188 citations) and Artificial Intelligence (394 citations). Ming Sun has collaborated with scholars based in United States, China and Germany. Frequent co-authors include Donna B. Stolz, Spyros Matsoukas, Junjie Yan, Chieh-Chi Kao, Shiv Vitaladevuni, Chao Wang, Sankaran Panchapagesan, Maria Chikina, Kate M. Vignali and Greg M. Delgoffe. Their work appears in journals such as Communications Biology, Scientific Reports, Nature Communications, Cell Reports and Bioconjugate Chemistry.
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.