Marco Cusumano-Towner
- Control and Systems Engineering top 5%
- Computer Vision and Pattern Recognition top 5%
- Biomedical Engineering
- Computational Mechanics top 10%
- Aerospace Engineering top 10%
- Co-authors
- Pieter AbbeelJeremy Maitin-ShepardArjun SinghStephen D. MillerJames F. O’BrienVikash K. MansinghkaDavid M. MasloveGomathi Krishnan
- Topics
- Bayesian Modeling and Causal Inference (5 papers)Machine Learning and Algorithms (3 papers)Machine Learning and Data Classification (3 papers)
- Cited by
- Computer Graphics and Computer-Aided DesignControl and Systems EngineeringComputer Vision and Pattern Recognition
- Journals
- Journal of the American Medical Informatics AssociationACM SIGPLAN NoticesProceedings of the ACM on Programming Languages
- Partner nations
- United StatesSwitzerland
In The Last Decade
Marco Cusumano-Towner
8 papers receiving 459 citations
Peers
Comparison fields: 5 of 71
- Control and Systems Engineering 277
- Computer Vision and Pattern Recognition 166
- Biomedical Engineering 97
- Computational Mechanics 94
- Aerospace Engineering 94
Countries citing papers authored by Marco Cusumano-Towner
This map shows the geographic impact of Marco Cusumano-Towner'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 Marco Cusumano-Towner with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Marco Cusumano-Towner more than expected).
Fields of papers citing papers by Marco Cusumano-Towner
This network shows the impact of papers produced by Marco Cusumano-Towner. 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 Marco Cusumano-Towner. The network helps show where Marco Cusumano-Towner may publish in the future.
Co-authorship network of co-authors of Marco Cusumano-Towner
This figure shows the co-authorship network connecting the top 25 collaborators of Marco Cusumano-Towner. A scholar is included among the top collaborators of Marco Cusumano-Towner 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 Marco Cusumano-Towner. Marco Cusumano-Towner is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 11 | |
| 2 | 40 | |
| 3 | 1 | |
| 4 | 5 | |
| 5 | 7 | |
| 6 | 23 | |
| 7 | 110 | |
| 8 | 289 |
About Marco Cusumano-Towner
Marco Cusumano-Towner is a scholar working on Applied Microbiology and Biotechnology, Human-Computer Interaction and Artificial Intelligence, having authored 8 papers that have together received 486 indexed citations. Recurring topics across this work include Bayesian Modeling and Causal Inference (5 papers), Machine Learning and Algorithms (3 papers) and Machine Learning and Data Classification (3 papers). The work is most often cited by research in Computer Graphics and Computer-Aided Design (42 citations), Control and Systems Engineering (277 citations) and Computer Vision and Pattern Recognition (166 citations). Marco Cusumano-Towner has collaborated with scholars based in United States and Switzerland. Frequent co-authors include Pieter Abbeel, Jeremy Maitin-Shepard, Arjun Singh, Stephen D. Miller, James F. O’Brien, Vikash K. Mansinghka, David M. Maslove, Gomathi Krishnan, Benjamin Bichsel and Timon Gehr. Their work appears in journals such as Journal of the American Medical Informatics Association, ACM SIGPLAN Notices and Proceedings of the ACM on Programming Languages.
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.