Boshi Wang

540 total citations
11 papers, 137 citations indexed

About

Boshi Wang is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Molecular Biology. According to data from OpenAlex, Boshi Wang has authored 11 papers receiving a total of 137 indexed citations (citations by other indexed papers that have themselves been cited), including 7 papers in Artificial Intelligence, 3 papers in Computer Vision and Pattern Recognition and 2 papers in Molecular Biology. Recurrent topics in Boshi Wang's work include Topic Modeling (3 papers), Primate Behavior and Ecology (2 papers) and Natural Language Processing Techniques (2 papers). Boshi Wang is often cited by papers focused on Topic Modeling (3 papers), Primate Behavior and Ecology (2 papers) and Natural Language Processing Techniques (2 papers). Boshi Wang collaborates with scholars based in United States, China and Germany. Boshi Wang's co-authors include Xiang Deng, Huan Sun, Sewon Min, Luke Zettlemoyer, Jiaming Shen, Huan Sun, Xiang Yue, Adrian Barbu, Kai Zhang and Huijuan Pan and has published in prestigious journals such as American Journal of Primatology, Electronics and Conservation Genetics Resources.

In The Last Decade

Boshi Wang

11 papers receiving 132 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Boshi Wang United States	5	87	16	15	13	12	11	137
Heliodoro Tejeda United States	6	78 0.9×	10 0.6×	23 1.5×	9 0.7×	32 2.7×	6	190
Vishakh Padmakumar United States	7	134 1.5×	24 1.5×	7 0.5×	14 1.1×	11 0.9×	13	180
Itay Dalmedigos Israel	2	103 1.2×	39 2.4×	6 0.4×	23 1.8×	5 0.4×	2	162
Ramteja Sajja United States	5	75 0.9×	6 0.4×	3 0.2×	31 2.4×	29 2.4×	10	196
Nicholas Meade Canada	3	89 1.0×	14 0.9×	3 0.2×	11 0.8×	8 0.7×	3	116
Behnam Hedayatnia United States	10	295 3.4×	47 2.9×	22 1.5×	16 1.2×	7 0.6×	22	356
Lea Frermann Australia	7	180 2.1×	15 0.9×	11 0.7×	13 1.0×	3 0.3×	34	229
Jasmijn Bastings United States	5	164 1.9×	37 2.3×	8 0.5×	15 1.2×	8 0.7×	8	195
Dmitry Ustalov Russia	7	111 1.3×	40 2.5×	11 0.7×	19 1.5×	2 0.2×	32	174
Aljoscha Burchardt Germany	11	327 3.8×	17 1.1×	24 1.6×	19 1.5×	23 1.9×	27	383

Countries citing papers authored by Boshi Wang

Since Specialization

Citations

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

Fields of papers citing papers by Boshi Wang

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Boshi Wang

This figure shows the co-authorship network connecting the top 25 collaborators of Boshi Wang. A scholar is included among the top collaborators of Boshi Wang 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 Boshi Wang. Boshi Wang is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

Sort: Min cites: Since: Top N: Style:

11 of 11 papers shown

1.

Wang, Boshi, et al.. (2024). LLMs in the Imaginarium: Tool Learning through Simulated Trial and Error. 10583–10604. 2 indexed citations

2.

Wang, Boshi, Xiang Yue, & Huan Sun. (2023). Can ChatGPT Defend its Belief in Truth? Evaluating LLM Reasoning via Debate. 11865–11881. 12 indexed citations

3.

Wang, Boshi, Sewon Min, Xiang Deng, et al.. (2023). Towards Understanding Chain-of-Thought Prompting: An Empirical Study of What Matters. 2717–2739. 49 indexed citations

4.

Yue, Xiang, et al.. (2023). Automatic Evaluation of Attribution by Large Language Models. 4615–4635. 12 indexed citations

5.

Wang, Boshi & Adrian Barbu. (2023). Hierarchical Classification for Large-Scale Learning. Electronics. 12(22). 4646–4646. 1 indexed citations

6.

Wang, Boshi, Xiang Deng, & Huan Sun. (2022). Iteratively Prompt Pre-trained Language Models for Chain of Thought. 2714–2730. 39 indexed citations

7.

Wang, Boshi & Adrian Barbu. (2022). Scalable Learning with Incremental Probabilistic PCA. 2022 IEEE International Conference on Big Data (Big Data). 5615–5622. 2 indexed citations

8.

Wang, Boshi, et al.. (2020). Designing Context-Sensitive Norm Inverse Reinforcement Learning Framework for Norm-Compliant Autonomous Agents. 618–625. 4 indexed citations

9.

Wang, Boshi, Xuming Zhou, Fanglei Shi, et al.. (2015). Full‐length Numt analysis provides evidence for hybridization between the Asian colobine genera Trachypithecus and Semnopithecus. American Journal of Primatology. 77(8). 901–910. 13 indexed citations

10.

Chen, Cheng, et al.. (2013). CATS derived SNPs discovery in the golden snub-nosed monkey (Rhinopithecus roxellanae). Conservation Genetics Resources. 6(1). 1–3. 2 indexed citations

11.

Zhang, Zhijia, et al.. (2013). 3D Object Recognition Based on Feature Line Extracting. Advances in intelligent systems research. 1 indexed citations

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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