Saša Misailovíc

3.7k total citations · 1 hit paper
85 papers, 2.3k citations indexed

About

Saša Misailovíc is a scholar working on Hardware and Architecture, Computer Networks and Communications and Artificial Intelligence. According to data from OpenAlex, Saša Misailovíc has authored 85 papers receiving a total of 2.3k indexed citations (citations by other indexed papers that have themselves been cited), including 52 papers in Hardware and Architecture, 32 papers in Computer Networks and Communications and 31 papers in Artificial Intelligence. Recurrent topics in Saša Misailovíc's work include Parallel Computing and Optimization Techniques (48 papers), Software Testing and Debugging Techniques (19 papers) and Distributed systems and fault tolerance (18 papers). Saša Misailovíc is often cited by papers focused on Parallel Computing and Optimization Techniques (48 papers), Software Testing and Debugging Techniques (19 papers) and Distributed systems and fault tolerance (18 papers). Saša Misailovíc collaborates with scholars based in United States, Switzerland and United Kingdom. Saša Misailovíc's co-authors include Martin Rinard, Henry Hoffmann, Michael Carbin, Stelios Sidiroglou, Stelios Sidiroglou-Douskos, Anant Agarwal, Deokhwan Kim, Sara Achour, Saikat Dutta and Zeyuan Allen Zhu and has published in prestigious journals such as Communications of the ACM, ACM Computing Surveys and IEEE Internet of Things Journal.

In The Last Decade

Saša Misailovíc

82 papers receiving 2.1k citations

Hit Papers

Managing performance vs. accuracy trade-offs with loop pe... 2011 2026 2016 2021 2011 100 200 300
What are hit papers?

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.

  1. Managing performance vs. accuracy trade-offs with loop perforation
    2011 351 citations Saša Misailovíc, Henry Hoffmann et al. profile →

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Saša Misailovíc United States 23 1.4k 1.1k 736 557 437 85 2.3k
Michael Carbin United States 22 825 0.6× 625 0.5× 611 0.8× 796 1.4× 435 1.0× 61 2.0k
Adrian Sampson United States 19 1.5k 1.1× 1.7k 1.5× 695 0.9× 420 0.8× 213 0.5× 50 2.5k
Valeria Bertacco United States 36 2.5k 1.8× 2.3k 2.0× 1.6k 2.1× 413 0.7× 175 0.4× 195 3.6k
Fabrizio Ferrandi Italy 18 1.4k 1.0× 682 0.6× 604 0.8× 193 0.3× 185 0.4× 151 1.8k
Werner Dietl United States 10 569 0.4× 544 0.5× 335 0.5× 382 0.7× 291 0.7× 29 1.2k
Insik Shin South Korea 28 1.9k 1.4× 320 0.3× 1.3k 1.8× 407 0.7× 355 0.8× 118 2.7k
Michael O’Boyle United Kingdom 34 3.0k 2.2× 475 0.4× 2.1k 2.9× 869 1.6× 1.1k 2.5× 145 3.7k
Trishul Chilimbi United States 30 2.1k 1.5× 506 0.4× 2.1k 2.9× 1.2k 2.2× 1.0k 2.3× 79 3.7k
David I. August United States 38 3.6k 2.6× 1.5k 1.3× 3.0k 4.0× 873 1.6× 614 1.4× 146 4.6k
Koen De Bosschere Belgium 32 2.1k 1.5× 477 0.4× 1.7k 2.3× 947 1.7× 734 1.7× 200 3.0k

Countries citing papers authored by Saša Misailovíc

Since Specialization
Citations

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

Fields of papers citing papers by Saša Misailovíc

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Saša Misailovíc

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

All Works

20 of 20 papers shown
1.
Singh, Mayank, et al.. (2025). Safety and Trust in Artificial Intelligence with Abstract Interpretation. 8(3-4). 250–408. 1 indexed citations
2.
Adve, Sarita V., et al.. (2025). FastFlip: Compositional SDC Resiliency Analysis. 362–376.
3.
Machidon, Octavian, et al.. (2024). Mobiprox: Supporting Dynamic Approximate Computing on Mobiles. IEEE Internet of Things Journal. 11(9). 16873–16886. 1 indexed citations
4.
Adve, Vikram, et al.. (2024). Felix: Optimizing Tensor Programs with Gradient Descent. 367–381. 3 indexed citations
5.
Rahman, Shanto, et al.. (2024). Quantizing Large-Language Models for Predicting Flaky Tests. 93–104. 6 indexed citations
6.
Misailovíc, Saša, et al.. (2023). Incremental Verification of Neural Networks. Proceedings of the ACM on Programming Languages. 7(PLDI). 1920–1945. 5 indexed citations
7.
Misailovíc, Saša, et al.. (2023). Diamont: dynamic monitoring of uncertainty for distributed asynchronous programs. International Journal on Software Tools for Technology Transfer. 25(4). 521–539. 1 indexed citations
8.
Singh, Gagandeep, et al.. (2022). A general construction for abstract interpretation of higher-order automatic differentiation. Proceedings of the ACM on Programming Languages. 6(OOPSLA2). 1007–1035. 6 indexed citations
9.
Singh, Gagandeep, et al.. (2022). Proof transfer for fast certification of multiple approximate neural networks. Proceedings of the ACM on Programming Languages. 6(OOPSLA1). 1–29. 6 indexed citations
10.
Skarlatos, Dimitrios, et al.. (2020). V-Combiner. 1–13. 1 indexed citations
11.
Xu, Ran, et al.. (2018). Videochef: efficient approximation for streaming video processing pipelines. USENIX Annual Technical Conference. 43–55. 16 indexed citations
12.
Filieri, Antonio, Martina Maggio, Konstantinos Angelopoulos, et al.. (2017). Control Strategies for Self-Adaptive Software Systems. ACM Transactions on Autonomous and Adaptive Systems. 11(4). 1–31. 42 indexed citations
13.
Gupta, Manish, et al.. (2017). Phase-aware optimization in approximate computing. 185–196. 18 indexed citations
14.
Misailovíc, Saša. (2015). Accuracy-aware optimization of approximate programs. 37–38. 2 indexed citations
15.
Misailovíc, Saša, et al.. (2014). Chisel. ACM SIGPLAN Notices. 49(10). 309–328. 43 indexed citations
16.
Misailovíc, Saša, Deokhwan Kim, & Martin Rinard. (2013). Parallelizing Sequential Programs with Statistical Accuracy Tests. ACM Transactions on Embedded Computing Systems. 12(2s). 1–26. 54 indexed citations
17.
Carbin, Michael, et al.. (2011). Detecting and escaping infinite loops with jolt. 609–633. 43 indexed citations
18.
Hoffmann, Henry, Stelios Sidiroglou, Michael Carbin, et al.. (2011). Dynamic knobs for responsive power-aware computing. 199–212. 236 indexed citations
19.
Rinard, Martin, Henry Hoffmann, Saša Misailovíc, & Stelios Sidiroglou. (2010). Patterns and statistical analysis for understanding reduced resource computing. 806–821. 31 indexed citations
20.
Milićević, Aleksandar, Saša Misailovíc, Darko Marinov, & Sarfraz Khurshid. (2007). Korat: A Tool for Generating Structurally Complex Test Inputs. 771–774. 31 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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