Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies
if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the
same subfield and year (this is the minimum needed to enter the top 1%, not the average
within it), or reaches the top citation threshold in at least one of its specific research
topics.
Syntax-guided synthesis
2013257 citationsRajeev Alur, Rastislav Bodík et al.profile →
Automated feedback generation for introductory programming assignments
2013251 citationsRishabh Singh, Sumit Gulwani et al.profile →
Author Peers
Peers are selected by citation overlap in the author's most active subfields.
citations ·
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This map shows the geographic impact of Rishabh Singh'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 Rishabh Singh with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Rishabh Singh more than expected).
This network shows the impact of papers produced by Rishabh Singh. 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 Rishabh Singh. The network helps show where Rishabh Singh may publish in the future.
Co-authorship network of co-authors of Rishabh Singh
This figure shows the co-authorship network connecting the top 25 collaborators of Rishabh Singh.
A scholar is included among the top collaborators of Rishabh Singh 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 Rishabh Singh. Rishabh Singh is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Vasudevan, Shobha, et al.. (2021). Learning Semantic Representations to Verify Hardware Designs. Neural Information Processing Systems. 34.6 indexed citations
6.
Dohan, David, et al.. (2021). Latent Programmer: Discrete Latent Codes for Program Synthesis. International Conference on Machine Learning. 4308–4318.1 indexed citations
7.
Chaudhuri, Swarat, Kevin Ellis, Oleksandr Polozov, et al.. (2021). Neurosymbolic Programming. 7(3). 158–243.36 indexed citations
Smith, Calvin, et al.. (2020). Generating Programmatic Referring Expressions via Program Synthesis. International Conference on Machine Learning. 1. 4495–4506.2 indexed citations
11.
Hellendoorn, Vincent J., Charles Sutton, Rishabh Singh, & Petros Maniatis. (2020). Global Relational Models of Source Code. International Conference on Learning Representations.50 indexed citations
Alur, Rajeev, Rastislav Bodík, Garvit Juniwal, et al.. (2013). Syntax-guided synthesis. DSpace@MIT (Massachusetts Institute of Technology).45 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.