Daniel Kumor
- Artificial Intelligence
- Electrical and Electronic Engineering
- Instrumentation top 10%
- Atomic and Molecular Physics, and Optics
- Biophysics
- Co-authors
- Adriana E. LitaAndrew D. BeyerThomas GerritsMatthew D. ShawSae Woo NamFrancesco MarsiliVarun B. VermaMartin J. Stevens
- Topics
- Bayesian Modeling and Causal Inference (3 papers)Advanced Semiconductor Detectors and Materials (2 papers)Quantum Information and Cryptography (2 papers)
- Journals
- Applied Physics LettersarXiv (Cornell University)International Conference on Machine Learning
- Partner nations
- United StatesCroatiaUnited Kingdom
In The Last Decade
Daniel Kumor
9 papers receiving 121 citations
Peers
Comparison fields: 5 of 40
- Artificial Intelligence 70
- Electrical and Electronic Engineering 46
- Instrumentation 44
- Atomic and Molecular Physics, and Optics 42
- Biophysics 18
Countries citing papers authored by Daniel Kumor
This map shows the geographic impact of Daniel Kumor'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 Daniel Kumor with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Daniel Kumor more than expected).
Fields of papers citing papers by Daniel Kumor
This network shows the impact of papers produced by Daniel Kumor. 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 Daniel Kumor. The network helps show where Daniel Kumor may publish in the future.
Co-authorship network of co-authors of Daniel Kumor
This figure shows the co-authorship network connecting the top 25 collaborators of Daniel Kumor. A scholar is included among the top collaborators of Daniel Kumor 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 Daniel Kumor. Daniel Kumor is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 3 | |
| 2 | Efficient Identification in Linear Structural Causal Models with Auxiliary Cutsets | 1 |
| 3 | Sensitivity Analysis of Linear Structural Causal Models | 15 |
| 4 | 1 | |
| 5 | 7 | |
| 6 | 3 | |
| 7 | 92 | |
| 8 | 5 | |
| 9 | 2 |
About Daniel Kumor
Daniel Kumor is a scholar working on Instrumentation, Artificial Intelligence and Control and Systems Engineering, having authored 9 papers that have together received 129 indexed citations. Recurring topics across this work include Bayesian Modeling and Causal Inference (3 papers), Advanced Semiconductor Detectors and Materials (2 papers) and Quantum Information and Cryptography (2 papers). The work is most often cited by research in Instrumentation (44 citations), Biophysics (18 citations) and Structural Biology (4 citations). Daniel Kumor has collaborated with scholars based in United States, Croatia and United Kingdom. Frequent co-authors include Adriana E. Lita, Andrew D. Beyer, Thomas Gerrits, Matthew D. Shaw, Sae Woo Nam, Francesco Marsili, Varun B. Verma, Martin J. Stevens, Michael S. Allman and Richard P. Mirin. Their work appears in journals such as Applied Physics Letters, arXiv (Cornell University) and International Conference on Machine Learning.
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.