About me

I am currently working as a postdoctoral researcher in Deep Learning Theory team at RIKEN AIP.

Research Interests

  • Bayesian statistics
  • Spatio-temporal modelling
  • Statistical learning theory for large-scale model

Recent Papers

Preprint

  • Wakayama, T., and Banerjee, S. (2024), “Process-based Inference for Spatial Energetics Using Bayesian Predictive Stacking”. arXiv preprint, arXiv:2405.09906, code.

  • Wakayama, T. and Sugasawa, S. (2024), “Ensemble Prediction via Covariate-dependent Stacking”. arXiv preprint, arXiv:2408.09755, code.

  • Wakayama, T. (2024), “Bayesian Inference for Consistent Predictions in Overparameterized Nonlinear Regression”. arXiv preprint, arXiv:2404.04498, code.

Publication

  • Wakayama, T. and Sugasawa, S. (2024), “Spatiotemporal Factor Models for Functional Data with Application to Population Map Forecast”. Spatial Statistics. (publication, code)

  • Wakayama, T. and Sugasawa, S. (2024), “Functional Horseshoe Smoothing for Functional Trend Estimation”. Statistica Sinica. (publication)

  • Wakayama, T. and Imaizumi, M. (2024), “Fast Convergence on Perfect Classification for Functional Data”. Statistica Sinica. (publication)

See all papers

Recent Talks

  • Wakayama, T., Sugasawa, S., Kobayashi, G. “Similarity-based Random Partition Distribution for Clustering Functional Data”, ISBA World Meeting 2024, Venice, Italy, June 2024.

  • Wakayama, T., Sugasawa, S. Spatiotemporal factor models for functional data with application to population map forecast, EcoSta2023, Tokyo, Japan, August 2nd, 2023.

  • Wakayama, T. Spatio temporal factor models for large scale data, 64th ISI World Statistics Congress, Ottawa, Canada, July 17th, 2023.

See all talks

Recent Awards & Grants

  • 2023/10-2025/3 ACT-X「次世代AIを築く数理・情報科学の革新」, JST.

  • 2023 ISI東京大会記念奨励賞, 日本統計学会

  • 2022/04- 日本学術振興会特別研究員DC1, JSPS.

See all awards and grants

Education

  • Ph.D. in Economics, The University of Tokyo, 2025

    Dissertation: “Nonparametric Bayesian Statistics for High-dimensional Data”

    Supervisor: Masaaki Imaizumi

  • M.S. in Economics, The University of Tokyo, 2022

    Master’s Thesis: “Trend Filtering for Functional Data: Optimization and Bayesian Approach”

    Supervisor: Shonosuke Sugasawa

  • B.S. in Engineering, Osaka Prefecture University, 2020