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.
Countries citing papers authored by Sanjit A. Seshia
Since
Specialization
Citations
This map shows the geographic impact of Sanjit A. Seshia'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 Sanjit A. Seshia with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Sanjit A. Seshia more than expected).
Fields of papers citing papers by Sanjit A. Seshia
This network shows the impact of papers produced by Sanjit A. Seshia. 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 Sanjit A. Seshia. The network helps show where Sanjit A. Seshia may publish in the future.
Co-authorship network of co-authors of Sanjit A. Seshia
This figure shows the co-authorship network connecting the top 25 collaborators of Sanjit A. Seshia.
A scholar is included among the top collaborators of Sanjit A. Seshia 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 Sanjit A. Seshia. Sanjit A. Seshia is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Rabe, Markus N., et al.. (2020). Learning Heuristics for Quantified Boolean Formulas through Reinforcement Learning. eScholarship (California Digital Library).3 indexed citations
6.
Fremont, Daniel J., Xiangyu Yue, Tommaso Dreossi, et al.. (2018). Scenic: Language-Based Scene Generation.. arXiv (Cornell University).7 indexed citations
Meel, Kuldeep S., Moshe Y. Vardi, Supratik Chakraborty, et al.. (2015). Constrained Sampling and Counting: Universal Hashing Meets SAT Solving. National Conference on Artificial Intelligence.15 indexed citations
9.
Sadigh, Dorsa, Katherine Driggs-Campbell, Alberto Puggelli, et al.. (2014). Data-Driven Probabilistic Modeling and Verification of Human Driver Behavior. eScholarship (California Digital Library). 56–61.38 indexed citations
10.
Seshia, Sanjit A., et al.. (2013). Synthesis of Implementable Control Strategies for Lazy Linear Hybrid Automata. Federated Conference on Computer Science and Information Systems. 1369–1376.2 indexed citations
11.
Alur, Rajeev, Rastislav Bodík, Garvit Juniwal, et al.. (2013). Syntax-guided synthesis. DSpace@MIT (Massachusetts Institute of Technology).45 indexed citations
12.
Sturton, Cynthia, Rohit Sinha, Thurston H. Y. Dang, et al.. (2013). Symbolic software model validation. Formal Methods. 97–108.2 indexed citations
Sinha, Rohit, Cynthia Sturton, Petros Maniatis, Sanjit A. Seshia, & David Wagner. (2012). Verification with small and short worlds. 68–77.1 indexed citations
Seshia, Sanjit A., Wenchao Li, & Subhasish Mitra. (2007). Verification-guided soft error resilience. Design, Automation, and Test in Europe. 1442–1447.61 indexed citations
Flanagan, Cormac, Shaz Qadeer, & Sanjit A. Seshia. (2002). A Modular Checker for Multithreaded Programs.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.