Matthew Mirman

1.4k total citations · 1 hit paper
6 papers, 523 citations indexed

About

Matthew Mirman is a scholar working on Artificial Intelligence, Control and Systems Engineering and Computer Vision and Pattern Recognition. According to data from OpenAlex, Matthew Mirman has authored 6 papers receiving a total of 523 indexed citations (citations by other indexed papers that have themselves been cited), including 6 papers in Artificial Intelligence, 2 papers in Control and Systems Engineering and 1 paper in Computer Vision and Pattern Recognition. Recurrent topics in Matthew Mirman's work include Adversarial Robustness in Machine Learning (5 papers), Fault Detection and Control Systems (2 papers) and Radiomics and Machine Learning in Medical Imaging (1 paper). Matthew Mirman is often cited by papers focused on Adversarial Robustness in Machine Learning (5 papers), Fault Detection and Control Systems (2 papers) and Radiomics and Machine Learning in Medical Imaging (1 paper). Matthew Mirman collaborates with scholars based in Switzerland, United Kingdom and China. Matthew Mirman's co-authors include Martin Vechev, Timon Gehr, Swarat Chaudhuri, Petar Tsankov, Dana Drachsler-Cohen, Markus Püschel, Gagandeep Singh, Marc Fischer, Zhaofei Wu and Lalasia Bialic‐Murphy and has published in prestigious journals such as PLoS Computational Biology, Repository for Publications and Research Data (ETH Zurich) and neural information processing systems.

In The Last Decade

Matthew Mirman

6 papers receiving 493 citations

Hit Papers

align trajectories

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.

AI2: Safety and Robustness Certification of Neural Networks with Abstract Interpretation

2018 316 citations Timon Gehr, Matthew Mirman et al. profile →

Peers

Countries citing papers authored by Matthew Mirman

Since Specialization

Citations

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

Fields of papers citing papers by Matthew Mirman

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Matthew Mirman

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

All Works

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6 of 6 papers shown

#	Work	Indexed citations
1	Ten simple rules for using large language models in science, version 1.0 2024 ·PLoS Computational Biology ·Gabriel Reuben Smith, Carolina Bello, Lalasia Bialic‐Murphy, (unknown), Camille S. Delavaux, (unknown), Johan van den Hoogen, T. Bruce Lauber, Haozhi Ma, Daniel S. Maynard, Matthew Mirman, Lidong Mo, (unknown), (unknown), Leland K. Werden, Zhaofei Wu, (unknown), (unknown), Constantin M. Zohner, Thomas W. Crowther	5
2	Universal Approximation with Certified Networks 2020 ·Repository for Publications and Research Data (ETH Zurich) ·(unknown), Matthew Mirman, Martin Vechev	1
3	Differentiable Abstract Interpretation for Provably Robust Neural Networks 2018 ·International Conference on Machine Learning ·Matthew Mirman, Timon Gehr, Martin Vechev	101
4	Fast and Effective Robustness Certification 2018 ·neural information processing systems ·Gagandeep Singh, Timon Gehr, Matthew Mirman, Markus Püschel, Martin Vechev	97
5	Distilled Agent DQN for Provable Adversarial Robustness 2018 ·Matthew Mirman, Marc Fischer, Martin Vechev	3
6	AI2: Safety and Robustness Certification of Neural Networks with Abstract Interpretation breakdown → 2018 ·Timon Gehr, Matthew Mirman, Dana Drachsler-Cohen, Petar Tsankov, Swarat Chaudhuri, Martin Vechev	316

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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