Timon Gehr
- Artificial Intelligence top 1%
- Computer Vision and Pattern Recognition top 5%
- Software top 5%
- Electrical and Electronic Engineering
- Signal Processing top 5%
- Co-authors
- Martin VechevMatthew MirmanGagandeep SinghMarkus PüschelPetar TsankovDana Drachsler-CohenSwarat ChaudhuriBenjamin Bichsel
- Topics
- Adversarial Robustness in Machine Learning (8 papers)Bayesian Modeling and Causal Inference (4 papers)Machine Learning and Algorithms (4 papers)
- Journals
- ACM SIGPLAN NoticesProceedings of the ACM on Programming LanguagesRepository for Publications and Research Data (ETH Zurich)
- Partner nations
- SwitzerlandUnited StatesBulgaria
In The Last Decade
Timon Gehr
18 papers receiving 969 citations
Hit Papers
Peers
Comparison fields: 5 of 66
- Artificial Intelligence 899
- Computer Vision and Pattern Recognition 197
- Software 135
- Electrical and Electronic Engineering 131
- Signal Processing 121
Countries citing papers authored by Timon Gehr
This map shows the geographic impact of Timon Gehr'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 Timon Gehr with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Timon Gehr more than expected).
Fields of papers citing papers by Timon Gehr
This network shows the impact of papers produced by Timon Gehr. 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 Timon Gehr. The network helps show where Timon Gehr may publish in the future.
Co-authorship network of co-authors of Timon Gehr
This figure shows the co-authorship network connecting the top 25 collaborators of Timon Gehr. A scholar is included among the top collaborators of Timon Gehr 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 Timon Gehr. Timon Gehr is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 1 | |
| 2 | 26 | |
| 3 | 71 | |
| 4 | 30 | |
| 5 | 13 | |
| 6 | DL2: Training and Querying Neural Networks with Logic. | 26 |
| 7 | Robustness Certification with Refinement | 2 |
| 8 | Boosting Robustness Certification of Neural Networks. | 36 |
| 9 | 246 | |
| 10 | Differentiable Abstract Interpretation for Provably Robust Neural Networks | 101 |
| 11 | Fast and Effective Robustness Certification | 97 |
| 12 | 1 | |
| 13 | 8 | |
| 14 | 24 | |
| 15 | AI2: Safety and Robustness Certification of Neural Networks with Abstract Interpretationbreakdown → | 316 |
| 16 | 7 | |
| 17 | 9 | |
| 18 | 10 |
About Timon Gehr
Timon Gehr is a scholar working on Artificial Intelligence, Computer Science Applications and Hardware and Architecture, having authored 18 papers that have together received 1.0k indexed citations. Recurring topics across this work include Adversarial Robustness in Machine Learning (8 papers), Bayesian Modeling and Causal Inference (4 papers) and Machine Learning and Algorithms (4 papers). The work is most often cited by research in Software (135 citations), Artificial Intelligence (899 citations) and Hardware and Architecture (81 citations). Timon Gehr has collaborated with scholars based in Switzerland, United States and Bulgaria. Frequent co-authors include Martin Vechev, Matthew Mirman, Gagandeep Singh, Markus Püschel, Petar Tsankov, Dana Drachsler-Cohen, Swarat Chaudhuri, Benjamin Bichsel, Mislav Balunović and Laurent Vanbever. Their work appears in journals such as ACM SIGPLAN Notices, Proceedings of the ACM on Programming Languages and Repository for Publications and Research Data (ETH Zurich).
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.