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D-Index & Metrics

Engineering and Technology

D-Index
52
Citations
23793
World Ranking
3522
National Ranking
1029

Overview

Ekin D. Cubuk is affiliated with Google in the United States. Their research spans multiple fields, with a strong focus on computer science and materials science. The subfields that characterize their work include materials chemistry, artificial intelligence, computer vision and pattern recognition, electrical and electronic engineering, and atomic and molecular physics and optics.

The scientist's recent publications illustrate the diversity and depth of their research interests. Selected papers include:

  • FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence (2020, arXiv (Cornell University))
  • Scaling deep learning for materials discovery (2023, Nature)
  • An autonomous laboratory for the accelerated synthesis of novel materials (2023, Nature)
  • Rethinking Pre-training and Self-training (2020, arXiv (Cornell University))
  • Unveiling the predictive power of static structure in glassy systems (2020, Nature Physics)

The scientist has collaborated frequently with several coauthors. The most prominent among these are Samuel S. Schoenholz, Barret Zoph, Amil Merchant, Quoc V. Le, and Simon Batzner. These collaborations have contributed to a wide range of studies, particularly in machine learning approaches applied to material sciences and computer vision.

Several venues regularly publish their work, reflecting their active engagement with the academic community in both foundational and applied research. The main publication venues include:

  • arXiv (Cornell University)
  • Nature
  • Nature Physics
  • Microscopy and Microanalysis
  • Physical Review Letters

The main topics covered by their work reflect the intersection of machine learning and materials science as well as advanced imaging and analysis techniques. These topics include:

  • Machine Learning in Materials Science
  • Advanced Neural Network Applications
  • Domain Adaptation and Few-Shot Learning
  • Electron and X-Ray Spectroscopy Techniques
  • X-ray Diffraction in Crystallography
  • Advanced Electron Microscopy Techniques and Applications
  • Video Surveillance and Tracking Methods

The scope of Ekin D. Cubuk's research positions them at the crossroads of computational methods and experimental materials research, addressing challenges in both data-driven modeling and practical material characterization. The combination of AI techniques with materials chemistry underlines a multidisciplinary approach that is evident through the diverse fields, topics, and publication outlets involved in their body of work.

Best Publications

  • SpecAugment: A Simple Data Augmentation Method for Automatic Speech Recognition

    Daniel S. Park;William Chan;Yu Zhang;Chung-Cheng Chiu

  • RandAugment: Practical Automated Data Augmentation with a Reduced Search Space

    Ekin Dogus Cubuk;Barret Zoph;Jon Shlens;Quoc Le

  • AutoAugment: Learning Augmentation Strategies From Data

    Ekin D. Cubuk;Barret Zoph;Dandelion Mane;Vijay Vasudevan

  • FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence

    Kihyuk Sohn;David Berthelot;Chun-Liang Li;Zizhao Zhang

  • FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence

    Kihyuk Sohn;David Berthelot;Chun-Liang Li;Zizhao Zhang

  • AutoAugment: Learning Augmentation Policies from Data

    Ekin Dogus Cubuk;Barret Zoph;Dandelion Mane;Vijay Vasudevan

  • Scaling deep learning for materials discovery

    Unknown

  • Realistic Evaluation of Deep Semi-Supervised Learning Algorithms

    Avital Oliver;Augustus Odena;Colin A. Raffel;Ekin Dogus Cubuk

  • Simple Copy-Paste is a Strong Data Augmentation Method for Instance Segmentation

    Golnaz Ghiasi;Yin Cui;Aravind Srinivas;Rui Qian

  • AugMix: A Simple Data Processing Method to Improve Robustness and Uncertainty

    Dan Hendrycks;Norman Mu;Ekin Dogus Cubuk;Barret Zoph

  • Learning Data Augmentation Strategies for Object Detection

    Barret Zoph;Ekin D. Cubuk;Golnaz Ghiasi;Tsung-Yi Lin

  • A structural approach to relaxation in glassy liquids

    Samuel S. Schoenholz;Ekin D. Cubuk;Daniel M. Sussman;Efthimios Kaxiras

  • AugMix: A Simple Data Processing Method to Improve Robustness and Uncertainty

    Dan Hendrycks;Norman Mu;Ekin D. Cubuk;Barret Zoph

  • Identifying structural flow defects in disordered solids using machine-learning methods.

    E. D Cubuk;Samuel Schoenholz;Jennifer M Rieser;B. D Malone

  • ReMixMatch: Semi-Supervised Learning with Distribution Matching and Augmentation Anchoring

    David Berthelot;Nicholas Carlini;Ekin D. Cubuk;Alex Kurakin

  • Holistic computational structure screening of more than 12,000 candidates for solid lithium-ion conductor materials

    Austin D. Sendek;Qian Yang;Ekin D. Cubuk;Karel-Alexander N. Duerloo

  • Holistic computational structure screening of more than 12 000 candidates for solid lithium-ion conductor materials

    Austin D. Sendek;Qian Yang;Ekin D. Cubuk;Karel-Alexander N. Duerloo

  • Rethinking Pre-training and Self-training

    Barret Zoph;Golnaz Ghiasi;Tsung-Yi Lin;Yin Cui

  • Atomic layer deposition of stable lithium ion conductive interfacial layer for stable cathode cycling

    Yi Cui;Jin Xie

  • Rethinking Pre-training and Self-training

    Barret Zoph;Golnaz Ghiasi;Tsung-Yi Lin;Yin Cui

  • Unveiling the predictive power of static structure in glassy systems

    V. Bapst;T. Keck;A. Grabska-Barwińska;C. Donner

  • ReMixMatch: Semi-Supervised Learning with Distribution Alignment and Augmentation Anchoring

    Unknown

  • Machine Learning-Assisted Discovery of Solid Li-Ion Conducting Materials

    Austin D. Sendek;Ekin D. Cubuk;Ekin D. Cubuk;Evan R. Antoniuk;Gowoon Cheon

  • Structure-property relationships from universal signatures of plasticity in disordered solids

    Ekin Dogus Cubuk;Robert Ivancic;Samuel S. Schoenholz;Samuel S. Schoenholz;Danny Strickland

  • RandAugment: Practical automated data augmentation with a reduced search space

    Ekin D. Cubuk;Barret Zoph;Jonathon Shlens;Quoc V. Le

  • A Fourier Perspective on Model Robustness in Computer Vision

    Dong Yin;Raphael Gontijo Lopes;Jonathon Shlens;Ekin D. Cubuk

  • ReMixMatch: Semi-Supervised Learning with Distribution Alignment and Augmentation Anchoring

    David Berthelot;Nicholas Carlini;Ekin D. Cubuk;Alex Kurakin

  • Adversarial Examples Are a Natural Consequence of Test Error in Noise

    Justin Gilmer;Nicolas Ford;Nicholas Carlini;Ekin D. Cubuk

Frequent Co-Authors

Barret Zoph
Barret Zoph Google (United States)
Efthimios Kaxiras
Efthimios Kaxiras Harvard University
Jonathon Shlens
Jonathon Shlens Google (United States)
Evan J. Reed
Evan J. Reed Stanford University
Tsung-Yi Lin
Tsung-Yi Lin Nvidia (United States)
Colin Raffel
Colin Raffel University of Toronto
Yi Cui
Yi Cui Stanford University
Quoc V. Le
Quoc V. Le Google (United States)
Harold S. Park
Harold S. Park Boston University

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