Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies
if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the
same subfield and year (this is the minimum needed to enter the top 1%, not the average
within it), or reaches the top citation threshold in at least one of its specific research
topics.
SpecAugment: A Simple Data Augmentation Method for Automatic Speech Recognition
20192.1k citationsBarret Zoph, Ekin D. Cubuk et al.arXiv (Cornell University)profile →
AutoAugment: Learning Augmentation Strategies From Data
20191.5k citationsEkin D. Cubuk, Barret Zoph et al.profile →
Scaling deep learning for materials discovery
2023667 citationsAmil Merchant, Simon Batzner et al.Natureprofile →
An autonomous laboratory for the accelerated synthesis of inorganic materials
2023451 citationsNathan J. Szymanski, Bernardus Rendy et al.Natureprofile →
Unveiling the predictive power of static structure in glassy systems
2020230 citationsVictor Bapst, Thomas M. Keck et al.Nature Physicsprofile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
hero ref
This map shows the geographic impact of Ekin D. Cubuk'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 Ekin D. Cubuk with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ekin D. Cubuk more than expected).
This network shows the impact of papers produced by Ekin D. Cubuk. 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 Ekin D. Cubuk. The network helps show where Ekin D. Cubuk may publish in the future.
Co-authorship network of co-authors of Ekin D. Cubuk
This figure shows the co-authorship network connecting the top 25 collaborators of Ekin D. Cubuk.
A scholar is included among the top collaborators of Ekin D. Cubuk 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 Ekin D. Cubuk. Ekin D. Cubuk is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Szymanski, Nathan J., Bernardus Rendy, Rishi E. Kumar, et al.. (2023). An autonomous laboratory for the accelerated synthesis of inorganic materials. Nature. 624(7990). 86–91.451 indexed citations breakdown →
Smullin, Sylvia, et al.. (2021). Tradeoffs in Data Augmentation: An Empirical Study. International Conference on Learning Representations.13 indexed citations
Bello, Irwan, William Fedus, Xianzhi Du, et al.. (2021). Revisiting ResNets: Improved Training and Scaling Strategies. Neural Information Processing Systems. 34.2 indexed citations
10.
Bapst, Victor, Thomas M. Keck, Agnieszka Grabska‐Barwińska, et al.. (2020). Unveiling the predictive power of static structure in glassy systems. Nature Physics. 16(4). 448–454.230 indexed citations breakdown →
11.
Zoph, Barret, Golnaz Ghiasi, Tsung-Yi Lin, et al.. (2020). Rethinking Pre-training and Self-training. arXiv (Cornell University). 33. 3833–3845.21 indexed citations
12.
Chen, Liang-Chieh, Raphael Gontijo Lopes, Bowen Cheng, et al.. (2020). Leveraging Semi-Supervised Learning in Video Sequences for Urban Scene Segmentation.. arXiv (Cornell University).2 indexed citations
13.
Hendrycks, Dan, Norman Mu, Ekin D. Cubuk, et al.. (2020). AugMix: A Simple Data Processing Method to Improve Robustness and Uncertainty. International Conference on Learning Representations.61 indexed citations
14.
Berthelot, David, Nicholas Carlini, Ekin D. Cubuk, et al.. (2020). ReMixMatch: Semi-Supervised Learning with Distribution Matching and Augmentation Anchoring. arXiv (Cornell University).165 indexed citations
15.
Chen, Liang-Chieh, Raphael Gontijo Lopes, Bowen Cheng, et al.. (2020). Semi-Supervised Learning in Video Sequences for Urban Scene Segmentation. arXiv (Cornell University).5 indexed citations
Oliver, Avital, Augustus Odena, Colin Raffel, Ekin D. Cubuk, & Ian Goodfellow. (2018). Realistic Evaluation of Semi-Supervised Learning Algorithms.. International Conference on Learning Representations.30 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.