Daniel Kumor

1.7k total citations
9 papers, 129 citations indexed

About

Daniel Kumor is a scholar working on Artificial Intelligence, Control and Systems Engineering and Atomic and Molecular Physics, and Optics. According to data from OpenAlex, Daniel Kumor has authored 9 papers receiving a total of 129 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 2 papers in Atomic and Molecular Physics, and Optics. Recurrent topics in Daniel Kumor's work include Bayesian Modeling and Causal Inference (3 papers), Advanced Semiconductor Detectors and Materials (2 papers) and Quantum Information and Cryptography (2 papers). Daniel Kumor is often cited by papers focused on Bayesian Modeling and Causal Inference (3 papers), Advanced Semiconductor Detectors and Materials (2 papers) and Quantum Information and Cryptography (2 papers). Daniel Kumor collaborates with scholars based in United States, Croatia and United Kingdom. Daniel Kumor's 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 and has published in prestigious journals such as Applied Physics Letters, arXiv (Cornell University) and International Conference on Machine Learning.

In The Last Decade

Daniel Kumor

9 papers receiving 121 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Daniel Kumor United States	4	70	46	44	42	18	9	129
Lautaro Narváez United States	6	57 0.8×	67 1.5×	55 1.3×	46 1.1×	18 1.0×	13	146
Nathnael Abebe United States	4	181 2.6×	194 4.2×	16 0.4×	50 1.2×	8 0.4×	6	256
Hugo Ferretti Canada	5	83 1.2×	21 0.5×	24 0.5×	119 2.8×	58 3.2×	9	175
Jorge Fuenzalida Germany	7	98 1.4×	27 0.6×	14 0.3×	114 2.7×	30 1.7×	19	169
F. Paleari Italy	6	64 0.9×	27 0.6×	20 0.5×	95 2.3×	10 0.6×	10	137
Michael Stefszky Germany	8	65 0.9×	66 1.4×	12 0.3×	136 3.2×	6 0.3×	25	169
A. J. Shields United Kingdom	7	177 2.5×	170 3.7×	54 1.2×	218 5.2×	34 1.9×	17	332
Sho Uemura United States	5	63 0.9×	71 1.5×	11 0.3×	59 1.4×	1 0.1×	20	164
Richard J. Birrittella United States	9	195 2.8×	31 0.7×	11 0.3×	200 4.8×	6 0.3×	23	241
Sébastien Sauge Sweden	7	184 2.6×	40 0.9×	22 0.5×	234 5.6×	7 0.4×	13	277

Countries citing papers authored by Daniel Kumor

Since Specialization

Citations

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

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

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9 of 9 papers shown

Kumor, Daniel, Junzhe Zhang, & Elias Bareinboim. (2022). Sequential Causal Imitation Learning with Unobserved Confounders. arXiv (Cornell University). 34. 3 indexed citations

Kumor, Daniel, Carlos Cinelli, & Elias Bareinboim. (2020). Efficient Identification in Linear Structural Causal Models with Auxiliary Cutsets. 1. 5501–5510. 1 indexed citations

Cinelli, Carlos, Daniel Kumor, Bryant Chen, Judea Pearl, & Elias Bareinboim. (2019). Sensitivity Analysis of Linear Structural Causal Models. International Conference on Machine Learning. 1252–1261. 15 indexed citations

Kumor, Daniel, Bryant Chen, & Elias Bareinboim. (2019). Efficient Identification in Linear Structural Causal Models with Instrumental Cutsets. arXiv (Cornell University). 32. 12477–12486. 1 indexed citations

Chen, Bryant, Daniel Kumor, & Elias Bareinboim. (2016). Identification and Model Testing in Linear Structural Equation Models using Auxiliary Variables. arXiv (Cornell University). 757–766. 7 indexed citations

Allman, Michael S., Varun B. Verma, Martin J. Stevens, et al.. (2015). A near-infrared 64-pixel superconducting nanowire single photon detector array with integrated multiplexed readout. Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE. 9504. 950402–950402. 3 indexed citations

Allman, Michael S., Varun B. Verma, Martin J. Stevens, et al.. (2015). A near-infrared 64-pixel superconducting nanowire single photon detector array with integrated multiplexed readout. Applied Physics Letters. 106(19). 92 indexed citations

Gauthier, Daniel J., Christoph F. Wildfeuer, M. Stipčević, et al.. (2013). Quantum Key Distribution Using Hyperentangled Time-Bin States. W2A.2–W2A.2. 5 indexed citations

Gauthier, Daniel J., Christoph F. Wildfeuer, M. Stipčević, et al.. (2013). Quantum Key Distribution Using Hyperentangled Time-Bin States. W2A.2–W2A.2. 2 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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