TY Tatsuya Yamada

Tatsuya Yamada （山田 達也）

Affiliation

Master's student, Graduate School of Information Science and Technology, The University of Osaka

（大阪大学大学院 情報科学研究科 博士前期課程）

About Me

I am a master's student at the University of Osaka.
My research interest lies in developing methods for training interpretable machine learning models.

Research Keywords

  • Causal Representation Learning
  • Temporal Data Analysis
  • Explainable AI
Portrait of Tatsuya Yamada

Links

ORCID GitHub

Profile

Basic Information

Identity and Affiliation

Name / 氏名
Tatsuya Yamada （山田 達也）
Affiliation / 所属
The University of Osaka, Graduate School of Information Science and Technology, Department of Information Systems Engineering (click) （大阪大学大学院 情報科学研究科 情報システム工学専攻）
Laboratory / 研究室
Machine Learning & Systems Laboratory (click) （機械学習システム論講座）
Position
Master's student （博士前期課程）
Research Areas
Machine Learning / Causal Representation Learning / Explainable AI / Temporal Data

Education

Apr. 2021 - Mar. 2025

School of Engineering Science, The University of Osaka.

Apr. 2025 - Present

Master's Program in Information Systems Engineering, Graduate School of Information Science and Technology, The University of Osaka.

Apr. 2025 - Present

Humanware Innovation Program, The University of Osaka.

Awards and Honors

    Research

    Research Themes

    Causal Representation Learning I study representation learning methods that aim to recover meaningful latent structure from observed data, with emphasis on factors that are both predictive and structurally interpretable.
    Temporal Data Analysis I am particularly interested in time-series and other temporal data, where dependencies over time create challenges that are not captured by methods designed for static data.
    Explainable AI A central motivation of my research is interpretability in machine learning. I am interested in representations that are easier for humans to understand, analyze, and connect to domain-relevant concepts.

    Keywords

    • Causal Representation Learning
    • Temporal Data Analysis / Time-Series Data
    • Explainable AI / Interpretable Representation Learning

    Current Interests

    My current interest is in connecting representation learning, interpretability, and causal structure, especially in settings where data are generated over time.

    Publications

    Papers and Presentations

    Papers

      Talks and Presentations

        CV

        Curriculum Vitae

        Status

        A fuller CV page is currently in preparation.

        Contact

        Contact Information

        Email

        t-yamada[at]ist.osaka-u.ac.jp