Kailyn Schmidt

1.8k total citations
8 papers, 544 citations indexed

About

Kailyn Schmidt is a scholar working on Cognitive Neuroscience, Computer Vision and Pattern Recognition and Artificial Intelligence. According to data from OpenAlex, Kailyn Schmidt has authored 8 papers receiving a total of 544 indexed citations (citations by other indexed papers that have themselves been cited), including 5 papers in Cognitive Neuroscience, 3 papers in Computer Vision and Pattern Recognition and 3 papers in Artificial Intelligence. Recurrent topics in Kailyn Schmidt's work include Neural dynamics and brain function (4 papers), Face Recognition and Perception (4 papers) and Neural Networks and Applications (2 papers). Kailyn Schmidt is often cited by papers focused on Neural dynamics and brain function (4 papers), Face Recognition and Perception (4 papers) and Neural Networks and Applications (2 papers). Kailyn Schmidt collaborates with scholars based in United States and Belgium. Kailyn Schmidt's co-authors include James J. DiCarlo, Kohitij Kar, Elias B. Issa, Jonas Kubilius, Rishi Rajalingham, Pouya Bashivan, Najib J. Majaj, Daniel M. Bear, Daniel Yamins and Martin Schrimpf and has published in prestigious journals such as Journal of Neuroscience, Nature Neuroscience and Journal of Vision.

In The Last Decade

Kailyn Schmidt

8 papers receiving 531 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Kailyn Schmidt United States	4	444	143	95	54	52	8	544
Pouya Bashivan United States	8	509 1.1×	135 0.9×	112 1.2×	39 0.7×	58 1.1×	23	657
Rishi Rajalingham United States	10	339 0.8×	118 0.8×	78 0.8×	41 0.8×	34 0.7×	15	492
Elias B. Issa United States	10	741 1.7×	198 1.4×	93 1.0×	59 1.1×	48 0.9×	16	855
Courtney J. Spoerer United Kingdom	6	429 1.0×	133 0.9×	84 0.9×	58 1.1×	32 0.6×	7	544
Martin Schrimpf United States	7	538 1.2×	124 0.9×	200 2.1×	50 0.9×	39 0.8×	15	721
Aran Nayebi United States	8	290 0.7×	73 0.5×	63 0.7×	40 0.7×	28 0.5×	14	360
Alexander J.E. Kell United States	7	440 1.0×	64 0.4×	65 0.7×	37 0.7×	15 0.3×	13	510
Ruben Coen-Cagli United States	13	679 1.5×	87 0.6×	74 0.8×	54 1.0×	26 0.5×	34	762
Ghislain St-Yves United States	8	326 0.7×	90 0.6×	46 0.5×	21 0.4×	35 0.7×	12	470
Jay Hegdé United States	15	790 1.8×	174 1.2×	61 0.6×	23 0.4×	33 0.6×	41	931

Countries citing papers authored by Kailyn Schmidt

Since Specialization

Citations

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

Fields of papers citing papers by Kailyn Schmidt

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Kailyn Schmidt

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

All Works

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8 of 8 papers shown

Kar, Kohitij, Martin Schrimpf, Kailyn Schmidt, & James J. DiCarlo. (2021). Chemogenetic suppression of macaque V4 neurons produces retinotopically specific deficits in downstream IT neural activity patterns and core object recognition behavior. Journal of Vision. 21(9). 2489–2489. 1 indexed citations

Kar, Kohitij, Jonas Kubilius, Kailyn Schmidt, Elias B. Issa, & James J. DiCarlo. (2019). Evidence that recurrent circuits are critical to the ventral stream’s execution of core object recognition behavior. Nature Neuroscience. 22(6). 974–983. 253 indexed citations

Kubilius, Jonas, Martin Schrimpf, Kohitij Kar, et al.. (2019). Brain-Like Object Recognition with High-Performing Shallow Recurrent ANNs. Lirias (KU Leuven). 32. 12785–12796. 35 indexed citations

Rajalingham, Rishi, Elias B. Issa, Pouya Bashivan, et al.. (2018). Large-Scale, High-Resolution Comparison of the Core Visual Object Recognition Behavior of Humans, Monkeys, and State-of-the-Art Deep Artificial Neural Networks. Journal of Neuroscience. 38(33). 7255–7269. 190 indexed citations

Kubilius, Jonas, Kohitij Kar, Kailyn Schmidt, & James J. DiCarlo. (2018). Can Deep Neural Networks Rival Human Ability to Generalize in Core Object Recognition?. Lirias (KU Leuven). 3 indexed citations

Kar, Kohitij, Jonas Kubilius, Elias B. Issa, Kailyn Schmidt, & James J. DiCarlo. (2017). Evidence that feedback is required for object identity inferences computed by the ventral stream. 1 indexed citations

Schmidt, Kailyn. (2016). Standardization of International Advertising Strategies: A Content Analysis of Pantene Pro-V. 7(1). 1 indexed citations

Rajalingham, Rishi, Kailyn Schmidt, & James J. DiCarlo. (2015). Comparison of Object Recognition Behavior in Human and Monkey. Journal of Neuroscience. 35(35). 12127–12136. 60 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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