World's Best Scientists 2026 revealed!
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Computer Science
Japan
2025

D-Index & Metrics

Computer Science

D-Index
59
Citations
18997
World Ranking
3360
National Ranking
29

Research.com Recognitions

  • 2025 - Research.com Computer Science in Japan Leader Award
  • 2022 - Research.com Computer Science in Japan Leader Award

Overview

Koji Tsuda is affiliated with the University of Tokyo in Japan. Their research spans multiple domains with a particular focus on computer science, materials science, and biochemistry, genetics, and molecular biology. This multidisciplinary approach is reflected in their main fields of study, which include:

  • Computer Science
  • Materials Science
  • Biochemistry, Genetics and Molecular Biology

Their work also extends into several subfields, emphasizing the intersection of computational techniques and biological or material systems. The subfields include:

  • Materials Chemistry
  • Molecular Biology
  • Computational Theory and Mathematics
  • Artificial Intelligence
  • Electrical and Electronic Engineering

Tsuda has contributed to research on a variety of specialized topics, including:

  • Machine Learning in Materials Science
  • Computational Drug Discovery Methods
  • Protein Structure and Dynamics
  • X-ray Diffraction in Crystallography
  • Monoclonal and Polyclonal Antibodies Research
  • Machine Learning and Data Classification
  • Quantum Computing Algorithms and Architecture

The scientist has published recent papers in leading academic venues. Notable papers include:

  • Designing metamaterials with quantum annealing and factorization machines (2020), Physical Review Research
  • Generating Ampicillin-Level Antimicrobial Peptides with Activity-Aware Generative Adversarial Networks (2020), ACS Omega
  • Black-Box Optimization for Automated Discovery (2021), Accounts of Chemical Research
  • Bayesian optimization package: PHYSBO (2022), Computer Physics Communications
  • Machine learning-driven optimization in powder manufacturing of Ni-Co based superalloy (2020), Materials & Design

Frequently appearing collaborators in Tsuda's publications include:

  • Ryo Tamura
  • Kei Terayama
  • Masato Sumita
  • Andrejs Tučs
  • Adnan Sljoka

The publication record also shows regular appearances in several key academic venues, including:

  • arXiv (Cornell University)
  • Science and Technology of Advanced Materials Methods
  • Scientific Reports
  • Digital Discovery
  • Journal of Chemical Theory and Computation

Best Publications

  • An introduction to kernel-based learning algorithms

    K.-R. Muller;S. Mika;G. Ratsch;K. Tsuda

  • Generating Ampicillin-Level Antimicrobial Peptides with Activity-Aware Generative Adversarial Networks

    Andrejs Tucs;Duy Phuoc Tran;Akiko Yumoto;Yoshihiro Ito

  • Marginalized kernels between labeled graphs

    Hisashi Kashima;Koji Tsuda;Akihiro Inokuchi

  • Kernel Methods in Computational Biology

    Bernhard Schölkopf;Koji Tsuda;Jean-Philippe Vert

  • A Primer on Kernel Methods

    JP Vert;K Tsuda;B Schölkopf;B. Schölkopf K. Tsuda

  • Support Vector Machine Applications in Computational Biology

    Bernhard Schölkopf;Koji Tsuda;Jean-Philippe Vert

  • COMBO: An efficient Bayesian optimization library for materials science

    Tsuyoshi Ueno;Trevor David Rhone;Zhufeng Hou;Teruyasu Mizoguchi

  • Machine learning with systematic density-functional theory calculations: Application to melting temperatures of single- and binary-component solids

    Atsuto Seko;Tomoya Maekawa;Koji Tsuda;Isao Tanaka

  • Fast protein classification with multiple networks

    Koji Tsuda;Hyunjung Shin;Bernhard Schölkopf

  • ChemTS: an efficient python library for de novo molecular generation

    Xiufeng Yang;Jinzhe Zhang;Kazuki Yoshizoe;Kei Terayama

  • Marginalized kernels for biological sequences.

    Koji Tsuda;Taishin Kin;Kiyoshi Asai

  • Discriminative Subsequence Mining for Action Classification

    S. Nowozin;G. Bakir;K. Tsuda

  • Matrix Exponentiated Gradient Updates for On-line Learning and Bregman Projection

    Koji Tsuda;Gunnar Rätsch;Manfred K. Warmuth

  • A New Discriminative Kernel From Probabilistic Models

    Koji Tsuda;Motoaki Kawanabe;Gunnar Rätsch;Sören Sonnenburg

  • Link Propagation: A Fast Semi-supervised Learning Algorithm for Link Prediction

    Hisashi Kashima;Tsuyoshi Kato;Yoshihiro Yamanishi;Masashi Sugiyama

  • Crystal structure prediction accelerated by Bayesian optimization

    Tomoki Yamashita;Tomoki Yamashita;Nobuya Sato;Hiori Kino;Takashi Miyake;Takashi Miyake

  • Machine-learning prediction of the d-band center for metals and bimetals

    Ichigaku Takigawa;Ichigaku Takigawa;Ken-ichi Shimizu;Ken-ichi Shimizu;Koji Tsuda;Koji Tsuda;Koji Tsuda;Satoru Takakusagi

  • Machine-Learning-Guided Mutagenesis for Directed Evolution of Fluorescent Proteins

    Yutaka Saito;Misaki Oikawa;Hikaru Nakazawa;Teppei Niide

  • Kernels for graphs

    H Kashima;K Tsuda;A Inokuchi;B. Schoelkopf K. Tsuda

  • gBoost: a mathematical programming approach to graph classification and regression

    Hiroto Saigo;Sebastian Nowozin;Tadashi Kadowaki;Taku Kudo

  • Learning kernels from biological networks by maximizing entropy

    K Tsuda;WS Noble

Frequent Co-Authors

Bernhard Schölkopf
Bernhard Schölkopf Max Planck Institute for Intelligent Systems
Kiyoshi Asai
Kiyoshi Asai University of Tokyo
Junichiro Shiomi
Junichiro Shiomi University of Tokyo
Jean-Philippe Vert
Jean-Philippe Vert Google (United States)
Gunnar Rätsch
Gunnar Rätsch ETH Zurich
Hisashi Kashima
Hisashi Kashima Kyoto University
Takeaki Uno
Takeaki Uno National Institute of Informatics
Sebastian Nowozin
Sebastian Nowozin Microsoft (United States)
Klaus-Robert Müller
Klaus-Robert Müller Technical University of Berlin

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