Akira Imakura

Associate Professor

University of Tsukuba

Japan

Research Interests

Explore related searches

Professors in Japan Professors in Statistics Statistics professors in Japan

Contact this professor

Get a free first email built on one of their papers, ready for you to edit and send yourself.

About

Akira Imakura is an Associate Professor at the University of Tsukuba, Japan. His research focuses on data collaboration analysis, including topics such as conditional average treatment effects, differential privacy applications, and eigenvalue problems. He has published extensively on methods for privacy-preserving data analysis, exploring innovative solutions in collaborative causal inference and survival analysis.

Articles (12)

Year:

2026

Year:

2026

Year:

2024

Year:

2024

Year:

2023

Year:

2023

Year:

2023

Year:

2023

Year:

2023

Year:

2022

Year:

2022

Year:

2020

Collaborators (4)

Kazumasa Omote

University of Tsukuba

JAPAN

View details

Akitoshi Takayasu

Associate Professor

University of Tsukuba

JAPAN

View details

Yukihiko Okada

Professor

University of Tsukuba

JAPAN

View details

Rina Kagawa

University of Tsukuba

JAPAN

View details

ApplyKite TwinLearns as you apply

Make your next move.

Prepare your application

Review drafts before you send them.

Akira Imakura

Research Interests

Explore related searches

Contact this professor

About

Articles (12)

Estimation of conditional average treatment effects on distributed confidential data

New solutions based on the generalized eigenvalue problem for the data collaboration analysis

Toward the Application of Differential Privacy to Data Collaboration

Collaborative causal inference on distributed data

Another use of SMOTE for interpretable data collaboration analysis

Complex moment-based eigensolver coupled with two Krylov subspaces

Non-readily identifiable data collaboration analysis for multiple datasets including personal information

Verified eigenvalue and eigenvector computations using complex moments and the Rayleigh–Ritz procedure for generalized Hermitian eigenvalue problems

DC-COX: Data collaboration Cox proportional hazards model for privacy-preserving survival analysis on multiple parties

Flexible subspace iteration with moments for an effective contour integration‐based eigensolver

Cross-Interactive Residual Smoothing for Global and Block Lanczos-Type Solvers for Linear Systems with Multiple Right-Hand Sides

Data Collaboration Analysis Framework Using Centralization of Individual Intermediate Representations for Distributed Data Sets

Collaborators (4)

Kazumasa Omote

Akitoshi Takayasu

Yukihiko Okada

Rina Kagawa

Make your next move.