Liangda Li

30 total papers · 604 total citations
21 papers, 355 citations indexed

About

Liangda Li is a scholar working on Artificial Intelligence, Molecular Biology and Applied Mathematics. According to data from OpenAlex, Liangda Li has authored 21 papers receiving a total of 355 indexed citations (citations by other indexed papers that have themselves been cited), including 7 papers in Artificial Intelligence, 6 papers in Molecular Biology and 6 papers in Applied Mathematics. Recurrent topics in Liangda Li's work include Point processes and geometric inequalities (6 papers), Bayesian Methods and Mixture Models (4 papers) and Diffusion and Search Dynamics (4 papers). Liangda Li is often cited by papers focused on Point processes and geometric inequalities (6 papers), Bayesian Methods and Mixture Models (4 papers) and Diffusion and Search Dynamics (4 papers). Liangda Li collaborates with scholars based in United States and China. Liangda Li's co-authors include Hongyuan Zha, Gui-Rong Xue, Yong Yu, Ke Zhou, Yi Chang, Hongbo Deng, Anlei Dong, Haesun Park, Ricardo Baeza‐Yates and Guy Lebanon and has published in prestigious journals such as Gastroenterology, Frontiers in Immunology and The American Journal of Gastroenterology.

In The Last Decade

Liangda Li

21 papers receiving 345 citations

Author Peers

Peers are selected by citation overlap in the author's most active subfields. citations · hero ref

Author Last Decade Papers Cites
Liangda Li 166 115 74 51 49 21 355
Weifeng Su 130 0.8× 144 1.3× 27 0.4× 20 0.4× 56 1.1× 36 347
Alceu Ferraz Costa 95 0.6× 30 0.3× 150 2.0× 17 0.3× 34 0.7× 11 321
Yan Zhou 173 1.0× 50 0.4× 63 0.9× 15 0.3× 64 1.3× 22 318
Wei-Wei Tu 254 1.5× 71 0.6× 113 1.5× 21 0.4× 20 0.4× 34 378
Wendi Ji 240 1.4× 247 2.1× 64 0.9× 31 0.6× 25 0.5× 18 380
Ahmed Fahim 217 1.3× 86 0.7× 98 1.3× 16 0.3× 73 1.5× 20 347
Daniel Tunkelang 131 0.8× 123 1.1× 82 1.1× 28 0.5× 37 0.8× 16 294
Jun Chen 80 0.5× 50 0.4× 46 0.6× 14 0.3× 30 0.6× 33 299
Jie Yu 229 1.4× 76 0.7× 170 2.3× 22 0.4× 35 0.7× 22 370
Jiarui Qin 219 1.3× 189 1.6× 77 1.0× 21 0.4× 13 0.3× 26 348

Countries citing papers authored by Liangda Li

Since Specialization
Citations

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

Fields of papers citing papers by Liangda Li

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Liangda Li

This figure shows the co-authorship network connecting the top 25 collaborators of Liangda Li. A scholar is included among the top collaborators of Liangda Li based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Liangda Li. Liangda Li is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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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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