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., Sugasawa, S., and Kobayashi, G. (2025+), “Similarity-based Random Partition Distribution for Clustering Functional Data”. Journal of the Royal Statistical Society, Series C. arXiv:2308.01704, code.

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

See all papers

Recent Talks

• Wakayama, T., and Imaizumi, M. (2025), “Bayesian Analysis for Over-parameterized Linear Model via Effective Spectra”, Objective Bayes Methodology Conference 2025, Athens, Greece, June 2025.

• Wakayama, T., and Banerjee, S. (2024), “Process-based Inference for Spatial Energetics Using Bayesian Predictive Stacking”, 2024 IMS International Conference on Statistics and Data Science, Nice, France, December 2024.

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

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