Kaile Su

2.3k citations
120 papers · 1.3k · h-index 21

Impact in

Papers in

Kaile Su

110 papers receiving 1.3k citations

Peers

Kaile Su
Comparison fields: 5 of 94
  • Software 134
  • Computational Theory and Mathematics 509
  • Computer Networks and Communications 572
  • Industrial and Manufacturing Engineering 224
  • Artificial Intelligence 652
Replace Ian P. Gent with:
Ian P. Gent United Kingdom
Roland H. C. Yap Singapore
Torsten Schaub Germany
Aldeida Aleti Australia
Christian Bessière France
Alfonso Gerevini Italy
Philip Laird United States
Maciej Koutny United Kingdom
Blai Bonet Venezuela
Greg Gagne United States
Kaile Su relative to Ian P. Gent United Kingdom Ian P. Gent's profile →
Citations per field
00.5×4.8×
Ian P. Gent · 1×
Citations per year

Countries citing papers authored by Kaile Su

Since Specialization
Citations

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

Fields of papers citing papers by Kaile Su

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside Kaile Su, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Kaile Su Line = papers co-authored together Kaile Su links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 120 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2011125
2 201391
3 201475
4 201364
5 201652
6 201546
7 201444
8 200342
9 201436
10
Within-problem learning for efficient lower bound computation in Max-SAT solving
200833
11 201432
12 201031
13 201626
14 200725
15 201823
16
Exploiting inference rules to compute lower bounds for MAX-SAT solving
200722
17 201722
18 201522
19 201721
20 201121

About Kaile Su

Kaile Su is a scholar working on Artificial Intelligence, Computational Theory and Mathematics, Computer Networks and Communications, Signal Processing and Information Systems, having authored 120 papers that have together received 1.3k indexed citations. Recurring topics across this work include Logic, Reasoning, and Knowledge (35 papers), Formal Methods in Verification (29 papers), Constraint Satisfaction and Optimization (22 papers), Logic, programming, and type systems (15 papers), Multi-Agent Systems and Negotiation (15 papers), Speech and Audio Processing (10 papers), Semantic Web and Ontologies (9 papers) and Bayesian Modeling and Causal Inference (9 papers). The work is most often cited by research in Software (134 citations), Computational Theory and Mathematics (509 citations), Computer Networks and Communications (572 citations), Industrial and Manufacturing Engineering (224 citations) and Artificial Intelligence (652 citations). Kaile Su has collaborated with scholars based in China, Australia and United States. Frequent co-authors include Shaowei Cai, Chuan Luo, Abdul Sattar, Jinkun Lin, Wei Wu, Qingliang Chen, Han Lin, Yanyan Xu, Dengfeng Ke and Jie Zhong. Their work appears in journals such as Artificial Intelligence, Frontiers of Computer Science, The Computer Journal, Knowledge-Based Systems and Journal of Logic and Computation.

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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