Chas Leichner

492 total citations
2 papers, 31 citations indexed

About

Chas Leichner is a scholar working on Molecular Biology, Computer Vision and Pattern Recognition and Artificial Intelligence. According to data from OpenAlex, Chas Leichner has authored 2 papers receiving a total of 31 indexed citations (citations by other indexed papers that have themselves been cited), including 1 paper in Molecular Biology, 1 paper in Computer Vision and Pattern Recognition and 1 paper in Artificial Intelligence. Recurrent topics in Chas Leichner's work include RNA and protein synthesis mechanisms (1 paper), Advanced Neural Network Applications (1 paper) and COVID-19 diagnosis using AI (1 paper). Chas Leichner is often cited by papers focused on RNA and protein synthesis mechanisms (1 paper), Advanced Neural Network Applications (1 paper) and COVID-19 diagnosis using AI (1 paper). Chas Leichner collaborates with scholars based in United States. Chas Leichner's co-authors include Luke W. Koblan, David B. Thompson, Paul A. Clemons, David R. Liu, Vlado Dančík, Lena K. Afeyan, Andrew Howard, Sherief Reda, Shan Li and Łukasz Lew and has published in prestigious journals such as Nature Communications.

In The Last Decade

Chas Leichner

2 papers receiving 30 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Chas Leichner United States	2	28	10	4	2	2	2	31
Tong Shi China	2	27 1.0×	6 0.6×	3 0.8×		2 1.0×	3	32
Catherine S. Novak United States	3	23 0.8×	8 0.8×	4 1.0×	4 2.0×		4	29
Panos Firbas Spain	4	22 0.8×	8 0.8×	5 1.3×		1 0.5×	4	33
Eugene Gil United States	2	31 1.1×	6 0.6×	4 1.0×			2	31
Sophie Seidel Switzerland	3	18 0.6×	7 0.7×	3 0.8×	2 1.0×		5	20
William R. Orchard United Kingdom	3	25 0.9×	7 0.7×	5 1.3×			3	32
C.L. Wei China	2	25 0.9×	6 0.6×	6 1.5×			4	31
Nianqin Sun China	3	36 1.3×	12 1.2×	2 0.5×		1 0.5×	6	42
Florian Heyl Germany	5	56 2.0×	11 1.1×	3 0.8×			9	59
Hector Rovira United States	4	26 0.9×	6 0.6×	3 0.8×			5	36

Countries citing papers authored by Chas Leichner

Since Specialization

Citations

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

Fields of papers citing papers by Chas Leichner

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Chas Leichner

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

All Works

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2 of 2 papers shown

1.

Leichner, Chas, et al.. (2024). PikeLPN: Mitigating Overlooked Inefficiencies of Low-Precision Neural Networks. 15996–16005. 1 indexed citations

2.

Afeyan, Lena K., Vlado Dančík, Luke W. Koblan, et al.. (2019). High-resolution specificity profiling and off-target prediction for site-specific DNA recombinases. Nature Communications. 10(1). 1937–1937. 30 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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