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Rio Yokota

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Professor

Tokyo Institute of Technology

Japan

Research Interests

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Professors in Japan Professors in High Performance Computing High Performance Computing professors in Japan

About

Professor Rio Yokota is a faculty member at Tokyo Institute of Technology, Japan. His research areas encompass quantum turbulence, high-performance computing, matrix multiplication optimization, and decentralized learning methodologies. Recent publications include studies on superfluid dynamics, parallel algorithms for GPUs, and advancements in large-scale deep learning techniques.

Recent Grants

Grant: Close

Project to promote research, development, and verification related to ensuring AI safety

Open Date: 2025-04-01

Close Date: 2026-03-01

Grant: Open

Innovation in Scientific Software to Maximize the Potential of Next-generation Computers

Open Date: 2025-04-01

Close Date: 2029-03-01

Grant: Open

A Red-Teaming Infrastructure for Large-Scale Language Model Misalignment

Open Date: 2024-10-01

Close Date: 2031-03-01

Grant: Close

Development and social implementation of an open medical LLM with safety and reliability

Open Date: 2024-04-01

Close Date: 2025-03-01

Grant: Close

Establishment of an outcome extraction method for evaluating drug efficacy and safety using large language models (LLM)

Open Date: 2024-04-01

Close Date: 2025-03-01

Articles (13)

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2026

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2026

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2025

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2025

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2025

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2024

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2024

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2024

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2023

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2022

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2022

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2022

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2021

Collaborators (7)

Masatoshi Kawai

Assistant professor

Tohoku University

JAPAN

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Makoto Tsubota

Osaka University

JAPAN

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Katsuhisa Ozaki

Shibaura Institute of Technology

JAPAN

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George Bosilca

University of Tennessee

UNITED STATES

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Hiromichi Kobayashi

Professor

Keio University

JAPAN

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Lorena Barba

Professor

George Washington University

UNITED STATES

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Noboru Harada

Head of Media Information Laboratory

JAPAN

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Rio Yokota

Research Interests

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About

Recent Grants

Articles (13)

Tensor-Core-Optimized Strategies for BLR × Tall-Skinny Matrix Multiplication in BEM

Building Effective Japanese Medical LLMs with an Open Recipe for Domain Adaptation through Continued Pre-training

Quantum Turbulence Coupled with Externally Driven Normal-Fluid Turbulence in Counterflow of Superfluid <sup>4</sup>He

A Study on the Performance and Usability of Managed Memory and Unified Memory for Accelerating Numerical Calculation Program

Leveraging High-Resource English Corpora for Cross-lingual Domain Adaptation in Low-Resource Japanese Medicine via Continued Pre-training

Natural Gradient Primal-Dual Method for Decentralized Learning

An inherently parallel ℋ2-ULV factorization for solving dense linear systems on GPUs

DGEMM on integer matrix multiplication unit

Cache Optimization and Performance Modeling of Batched, Small, and Rectangular Matrix Multiplication on Intel, AMD, and Fujitsu Processors

Parallel QR Factorization of Block Low-rank Matrices

Recovering single precision accuracy from Tensor Cores while surpassing the FP32 theoretical peak performance

Scalable and Practical Natural Gradient for Large-Scale Deep Learning

ExaFMM: a high-performance fast multipole method library with C++ and Python interfaces

Collaborators (7)

Masatoshi Kawai

Makoto Tsubota

Katsuhisa Ozaki

George Bosilca

Hiromichi Kobayashi

Lorena Barba

Noboru Harada

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