About Me
I’m Hiroki Sakamoto, a Research Fellow at the Max Planck Institute for Dynamics of Complex Technical Systems. I received my Ph.D. in Information Science and Technology from The University of Tokyo, where I was advised by Kazuhiro Sato.
My research focuses on model reduction and compression for high‑dimensional dynamical systems and deep learning models. Broadly, I explore data‑driven methods for modeling, controlling, and interpreting complex physical systems, aiming to create compact yet accurate representations that bridge control theory and modern machine‑learning techniques.
Interests
- Model Order Reduction
- Deep State-Space Models
- Model Compression
- Randomized Numerical Linear Algebra
Education
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Ph.D., The University of Tokyo
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M.S., The University of Tokyo
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B.S., Waseda University
Recent Publications
Publications
(2026).
A Deep State-Space Model Compression Method using Upper Bound on Output Error.
preprint on arXiv.
(2026).
Data-Driven Regularized Time-Limited h2 Model Reduction From Noisy Impulse Responses.
IEEE Control Systems Letters.
(2026).
Lockdown Policy Rules with a Hospital Capacity Constraint.
preprint on CARF Working Paper (CARF-F-620).
(2025).
Compression Method for Deep Diagonal State Space Model Based on H2 Optimal Reduction.
IEEE Control Systems Letters, Vol. 9, pp. 2043–2048.
(2025).
Data-driven h2 model reduction for linear discrete-time systems.
preprint on arXiv.