Liang Lan
Impact in
- Computational Mathematics top 10%
- Sensory Systems top 10%
- Hearing, Cochlea, Tinnitus, Genetics
Papers in
-
- Bioinformatics and Genomic Networks 4
- Co-authors
- Slobodan Vučetić (10 shared papers)Zhuang Wang (5 shared papers)Nemanja Djuric (2 shared papers)Tao Huang (3 shared papers)Xuexian Fang (1 shared paper)Peng An (1 shared paper)Junxia Min (1 shared paper)Fudi Wang (1 shared paper)
- Journals
- British Poultry Science (3 papers)IEEE Transactions on Neural Networks and Learning Systems (3 papers)Optik (3 papers)Frontiers in Oncology (2 papers)IEEE Transactions on Geoscience and Remote Sensing (1 paper)
- Partner nations
- ChinaHong KongUnited States
In The Last Decade
Liang Lan
55 papers receiving 784 citations
Peers
Comparison fields: 5 of 139
- Computational Mathematics 9
- Sensory Systems 56
- Health Information Management 52
- Health Informatics 14
- Artificial Intelligence 243
Countries citing papers authored by Liang Lan
This map shows the geographic impact of Liang Lan'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 Liang Lan with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Liang Lan more than expected).
Fields of papers citing papers by Liang Lan
This network shows the impact of papers produced by Liang Lan. 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 Liang Lan. The network helps show where Liang Lan may publish in the future.
Co-authors
The 25 scholars most cited alongside Liang Lan, 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 63 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2015 | 130 | |
| 2 | 2018 | 88 | |
| 3 | Scaling up Kernel SVM on Limited Resources: A Low-rank Linearization Approach | 2012 | 77 |
| 4 | 2013 | 73 | |
| 5 | 2021 | 58 | |
| 6 | BudgetedSVM: a toolbox for scalable SVM approximations | 2013 | 42 |
| 7 | 2014 | 35 | |
| 8 | 2021 | 33 | |
| 9 | 2018 | 31 | |
| 10 | 2016 | 24 | |
| 11 | 2011 | 24 | |
| 12 | 2015 | 21 | |
| 13 | 2011 | 20 | |
| 14 | 2007 | 17 | |
| 15 | 2023 | 14 | |
| 16 | 2014 | 14 | |
| 17 | 2020 | 13 | |
| 18 | 2022 | 9 | |
| 19 | 2013 | 9 | |
| 20 | 2019 | 6 |
About Liang Lan
Liang Lan is a scholar working on Artificial Intelligence, Molecular Biology, Computer Vision and Pattern Recognition, Oncology and Information Systems, having authored 63 papers that have together received 824 indexed citations. Recurring topics across this work include Remote Sensing and Land Use (5 papers), Spectroscopy and Chemometric Analyses (5 papers), Traditional Chinese Medicine Analysis (4 papers), Bioinformatics and Genomic Networks (4 papers), Face and Expression Recognition (4 papers), Sparse and Compressive Sensing Techniques (4 papers), Spam and Phishing Detection (4 papers) and Misinformation and Its Impacts (4 papers). The work is most often cited by research in Computational Mathematics (9 citations), Sensory Systems (56 citations), Health Information Management (52 citations), Health Informatics (14 citations) and Artificial Intelligence (243 citations). Liang Lan has collaborated with scholars based in China, Hong Kong and United States. Frequent co-authors include Slobodan Vučetić, Zhuang Wang, Nemanja Djuric, Tao Huang, Xuexian Fang, Peng An, Junxia Min, Fudi Wang, Fabian Moerchen and Kai Zhang. Their work appears in journals such as British Poultry Science, IEEE Transactions on Neural Networks and Learning Systems, Optik, Frontiers in Oncology and IEEE Transactions on Geoscience and Remote Sensing.
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.