Fei Mi
Impact in
- Artificial Intelligence top 5%
- Topic Modeling
- Natural Language Processing Techniques
- Speech and dialogue systems
- Domain Adaptation and Few-Shot Learning
Papers in
-
- Topic Modeling 30
- Natural Language Processing Techniques 18
- Speech and dialogue systems 17
- Co-authors
- Boi Faltings (6 shared papers)Minlie Huang (9 shared papers)Jianping Xu (9 shared papers)Yang Jiang (3 shared papers)Lei Chen (6 shared papers)Zhao Zhang (4 shared papers)Yasheng Wang (17 shared papers)Jiyong Zhang (1 shared paper)
- Journals
- PLoS ONE (2 papers)Frontiers in Microbiology (2 papers)BMC Public Health (2 papers)INTERNATIONAL JOURNAL OF SYSTEMATIC AND EVOLUTIONARY MICROBIOLOGY (2 papers)The Journal of Clinical Endocrinology & Metabolism (2 papers)
- Partner nations
- ChinaSwedenUnited Kingdom
In The Last Decade
Fei Mi
67 papers receiving 902 citations
Peers
Comparison fields: 5 of 131
- Artificial Intelligence 325
- Health Informatics 8
- Renewable Energy, Sustainability and the Environment 87
- Pharmacology 74
- Materials Chemistry 216
Countries citing papers authored by Fei Mi
This map shows the geographic impact of Fei Mi'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 Fei Mi with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Fei Mi more than expected).
Fields of papers citing papers by Fei Mi
This network shows the impact of papers produced by Fei Mi. 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 Fei Mi. The network helps show where Fei Mi may publish in the future.
Co-authors
The 25 scholars most cited alongside Fei Mi, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
Showing the 20 most-cited of 75 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2018 | 102 | |
| 2 | 2016 | 64 | |
| 3 | 2019 | 45 | |
| 4 | 2021 | 43 | |
| 5 | 2020 | 39 | |
| 6 | 2022 | 38 | |
| 7 | 2016 | 35 | |
| 8 | 2020 | 31 | |
| 9 | 2022 | 27 | |
| 10 | 2022 | 26 | |
| 11 | 2015 | 24 | |
| 12 | 2013 | 23 | |
| 13 | 2020 | 23 | |
| 14 | 2021 | 22 | |
| 15 | 2017 | 21 | |
| 16 | 2018 | 19 | |
| 17 | 2021 | 18 | |
| 18 | 2017 | 18 | |
| 19 | 2021 | 17 | |
| 20 | 2023 | 17 |
About Fei Mi
Fei Mi is a scholar working on Artificial Intelligence, Molecular Biology, Plant Science, Pharmacology and Materials Chemistry, having authored 75 papers that have together received 923 indexed citations. Recurring topics across this work include Topic Modeling (30 papers), Natural Language Processing Techniques (18 papers), Speech and dialogue systems (17 papers), Mycorrhizal Fungi and Plant Interactions (8 papers), Fungal Biology and Applications (7 papers), Luminescence Properties of Advanced Materials (6 papers), Multimodal Machine Learning Applications (5 papers) and Plant Pathogens and Fungal Diseases (5 papers). The work is most often cited by research in Artificial Intelligence (325 citations), Health Informatics (8 citations), Renewable Energy, Sustainability and the Environment (87 citations), Pharmacology (74 citations) and Materials Chemistry (216 citations). Fei Mi has collaborated with scholars based in China, Sweden and United Kingdom. Frequent co-authors include Boi Faltings, Minlie Huang, Jianping Xu, Yang Jiang, Lei Chen, Zhao Zhang, Yasheng Wang, Jiyong Zhang, Yang Cao and Minlie Huang. Their work appears in journals such as PLoS ONE, Frontiers in Microbiology, BMC Public Health, INTERNATIONAL JOURNAL OF SYSTEMATIC AND EVOLUTIONARY MICROBIOLOGY and The Journal of Clinical Endocrinology & Metabolism.
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.