World's Best Scientists 2026 revealed!

D-Index & Metrics

Engineering and Technology

D-Index
34
Citations
9841
World Ranking
9113
National Ranking
2543

Overview

Ryota Tomioka is affiliated with Microsoft in the United States and specializes in research at the intersection of computer science and materials chemistry. Their body of work spans diverse subfields, including artificial intelligence, molecular biology, hardware and architecture, and computer vision and pattern recognition.

The scientist's research primarily focuses on machine learning applications within materials science, protein structure and dynamics, and techniques related to parallel computing and optimization. Their work also explores advanced neural network applications, advanced data storage technologies, adversarial robustness in machine learning, and X-ray diffraction in crystallography.

Ryota Tomioka has contributed to several recent publications, including:

  • A generative model for inorganic materials design, 2025, Nature
  • MatterGen: a generative model for inorganic materials design, 2023, arXiv (Cornell University)
  • Scalable emulation of protein equilibrium ensembles with generative deep learning, 2025, Science
  • On the Loss Landscape of Adversarial Training: Identifying Challenges and How to Overcome Them, 2020, arXiv (Cornell University)
  • Timewarp: Transferable Acceleration of Molecular Dynamics by Learning Time-Coarsened Dynamics, 2023, arXiv (Cornell University)

The researcher frequently publishes in prominent venues, such as:

  • arXiv (Cornell University)
  • Nature
  • Science
  • Annals of Internal Medicine

Frequent collaborators include Claudio Zeni, Robert Pinsler, Daniel Zügner, Matthew K. Horton, and Jonathan Crabbé. Each of these coauthors has contributed to multiple publications alongside Ryota Tomioka.

Best Publications

  • Optimizing Spatial filters for Robust EEG Single-Trial Analysis

    B. Blankertz;R. Tomioka;S. Lemm;M. Kawanabe

  • f -GAN: training generative neural samplers using variational divergence minimization

    Sebastian Nowozin;Botond Cseke;Ryota Tomioka

  • QSGD: Communication-Efficient SGD via Gradient Quantization and Encoding

    Dan Alistarh;Demjan Grubic;Jerry Z. Li;Ryota Tomioka

  • Norm-Based Capacity Control in Neural Networks

    Behnam Neyshabur;Ryota Tomioka;Nathan Srebro

  • In Search of the Real Inductive Bias: On the Role of Implicit Regularization in Deep Learning

    Behnam Neyshabur;Ryota Tomioka;Nathan Srebro

  • Invariant Common Spatial Patterns: Alleviating Nonstationarities in Brain-Computer Interfacing

    Benjamin Blankertz;Motoaki Kawanabe;Ryota Tomioka;Friederike Hohlefeld

  • Multi-Level Variational Autoencoder: Learning Disentangled Representations from Grouped Observations

    Diane Bouchacourt;Ryota Tomioka;Sebastian Nowozin

  • Estimation of low-rank tensors via convex optimization

    Ryota Tomioka;Kohei Hayashi;Hisashi Kashima

  • Tensor factorization using auxiliary information

    Atsuhiro Narita;Kohei Hayashi;Ryota Tomioka;Hisashi Kashima

  • A regularized discriminative framework for EEG analysis with application to brain-computer interface.

    Ryota Tomioka;Ryota Tomioka;Ryota Tomioka;Klaus Robert Müller

  • Statistical Performance of Convex Tensor Decomposition

    Ryota Tomioka;Taiji Suzuki;Kohei Hayashi;Hisashi Kashima

  • Geometry of Optimization and Implicit Regularization in Deep Learning.

    Behnam Neyshabur;Ryota Tomioka;Ruslan Salakhutdinov;Nathan Srebro

  • Logistic Regression for Single Trial EEG Classification

    Ryota Tomioka;Kazuyuki Aihara;Klaus-Robert Müller

  • Discovering Emerging Topics in Social Streams via Link-Anomaly Detection

    Toshimitsu Takahashi;Ryota Tomioka;Kenji Yamanishi

  • Large-scale EEG/MEG source localization with spatial flexibility.

    Stefan Haufe;Ryota Tomioka;Thorsten Dickhaus;Claudia Sannelli

  • Modeling Sparse Connectivity Between Underlying Brain Sources for EEG/MEG

    Stefan Haufe;Ryota Tomioka;Guido Nolte;Klaus-Robert Müller

  • Convex Tensor Decomposition via Structured Schatten Norm Regularization

    Ryota Tomioka;Taiji Suzuki

  • Global analytic solution of fully-observed variational Bayesian matrix factorization

    Shinichi Nakajima;Masashi Sugiyama;S. Derin Babacan;Ryota Tomioka

  • The algebraic combinatorial approach for low-rank matrix completion

    Franz J. Király;Louis Theran;Ryota Tomioka

  • Multivariate analysis of noise in genetic regulatory networks

    Ryota Tomioka;Hidenori Kimura;Tetsuya J. Kobayashi;Kazuyuki Aihara

  • QSGD: Randomized Quantization for Communication-Optimal Stochastic Gradient Descent

    Dan Alistarh;Jerry Li;Ryota Tomioka;Milan Vojnovic

  • Continuous Hierarchical Representations with Poincaré Variational Auto-Encoders

    Emile Mathieu;Charline Le Lan;Chris J. Maddison;Ryota Tomioka

  • QSGD: Communication-Optimal Stochastic Gradient Descent, with Applications to Training Neural Networks

    Dan Alistarh;Demjan Grubic;Jerry Li;Ryota Tomioka

Frequent Co-Authors

Taiji Suzuki
Taiji Suzuki University of Tokyo
Hisashi Kashima
Hisashi Kashima Kyoto University
Klaus-Robert Müller
Klaus-Robert Müller Technical University of Berlin
Behnam Neyshabur
Behnam Neyshabur New York University
Nathan Srebro
Nathan Srebro Toyota Technological Institute at Chicago
Dan Alistarh
Dan Alistarh Institute of Science and Technology Austria
Milan Vojnovic
Milan Vojnovic London School of Economics and Political Science
Kazuyuki Aihara
Kazuyuki Aihara University of Tokyo
Guido Nolte
Guido Nolte Universität Hamburg

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