Liangda Li

606 total citations
21 papers, 356 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 356 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, Guy Lebanon, Haesun Park and Ricardo Baeza‐Yates 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 346 citations

Peers

Liangda Li
Comparison fields: 5 of 68
  • Artificial Intelligence 166
  • Information Systems 115
  • Computer Vision and Pattern Recognition 74
  • Molecular Biology 51
  • Signal Processing 48
Replace Weifeng Su with:
Weifeng Su China
Yutao Zhu China
Paolo Rosso Switzerland
Gregor Heinrich Germany
Veli Biçer Ireland
Jiarui Qin China
Renato Fileto Brazil
Masoud Makrehchi Canada
Guandong Xu Australia
Weifeng Su China View profile →
Citations per field, relative to Liangda Li
Liangda Li · 1×
Citations per year, relative to Liangda Li
Liangda Li · 1×

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

20 of 20 papers shown
# Work Indexed citations
1 1
2 9
3 21
4 1
5 10
6 3
7 9
8 18
9 6
10 7
11
Household structure analysis via hawkes processes for enhancing energy disaggregation
6
12 8
13 4
14 25
15 44
16 20
17 18
18 20
19 30
20 84

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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