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.
All You Ever Wanted to Know about Dynamic Taint Analysis and Forward Symbolic Execution (but Might Have Been Afraid to Ask)
2010450 citationsEdward J. Schwartz, Thanassis Avgerinos et al.profile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
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Countries citing papers authored by Edward J. Schwartz
Since
Specialization
Citations
This map shows the geographic impact of Edward J. Schwartz'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 Edward J. Schwartz with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Edward J. Schwartz more than expected).
Fields of papers citing papers by Edward J. Schwartz
This network shows the impact of papers produced by Edward J. Schwartz. 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 Edward J. Schwartz. The network helps show where Edward J. Schwartz may publish in the future.
Co-authorship network of co-authors of Edward J. Schwartz
This figure shows the co-authorship network connecting the top 25 collaborators of Edward J. Schwartz.
A scholar is included among the top collaborators of Edward J. Schwartz 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 Edward J. Schwartz. Edward J. Schwartz is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Schwartz, Edward J., et al.. (2018). Statistical Machine Translation Is a Natural Fit for Automatic Identifier Renaming in Software Source Code.. National Conference on Artificial Intelligence. 771–774.
5.
Han, Hyung-Seok, et al.. (2018). Fuzzing: Art, Science, and Engineering.. arXiv (Cornell University).18 indexed citations
Schwartz, Edward J., Thanassis Avgerinos, & David Brumley. (2018). Q: Exploit Hardening Made Easy. Research Showcase @ Carnegie Mellon University (Carnegie Mellon University). 25–25.39 indexed citations
8.
Schwartz, Edward J., David Brumley, & Jonathan M. McCune. (2018). A Contractual Anonymity System. Research Showcase @ Carnegie Mellon University (Carnegie Mellon University).1 indexed citations
Nord, Robert L., İpek Özkaya, Edward J. Schwartz, Forrest Shull, & Rick Kazman. (2016). Can Knowledge of Technical Debt Help Identify Software Vulnerabilities. USENIX Security Symposium.4 indexed citations
11.
Avgerinos, Thanassis, et al.. (2014). Automatic exploit generation. Communications of the ACM. 57(2). 74–84.155 indexed citations
12.
Schwartz, Edward J., JongHyup Lee, Maverick Woo, & David Brumley. (2013). Native ×86 decompilation using semantics-preserving structural analysis and iterative control-flow structuring. USENIX Security Symposium. 353–368.60 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.