Tae Hoon Oh

Assistant Professor

Kyoto University

Japan

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Tae Hoon Oh is an Assistant Professor at Kyoto University in Japan. His research focuses on advanced control strategies, including reinforcement learning and model predictive control, particularly in chemical and biological processes. Recent studies include Bayesian optimization techniques, as well as the techno-economic assessment of carbon capture systems. He actively explores the integration of various control methodologies to enhance the feasibility and efficiency of industrial processes.

Articles (14)

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2025

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2025

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2025

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2024

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2024

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2023

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2022

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2022

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2021

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2021

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2020

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2020

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2020

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2020

Collaborators (3)

Ken-Ichiro Sotowa

教授 / Professor

Kyoto University

JAPAN

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Jong Min Lee

Professor

Seoul National University

SOUTH KOREA

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Yeonsoo Kim

Assistant Professor

Kwangwoon University

SOUTH KOREA

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Tae Hoon Oh

Research Interests

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Articles (14)

Bayesian optimization with search space movement for cooling crystallization process

Self-driven propylene epoxidation on modified titanium silicalite-1 by in situ generated hydrogen peroxide

Techno-economic assessment and feature importance analysis of gas hydrate-based carbon capture processes

Model-based safe reinforcement learning for nonlinear systems under uncertainty with constraints tightening approach

Quantitative comparison of reinforcement learning and data-driven model predictive control for chemical and biological processes

Integrating Path Integral Control With Backstepping Control to Regulate Stochastic System

Multi-strategy control to extend the feasibility region for robust model predictive control

Integration of reinforcement learning and model predictive control to optimize semi‐batch bioreactor

Automatic control of simulated moving bed process with deep Q-network

Model-based reinforcement learning and predictive control for two-stage optimal control of fed-batch bioreactor

Move blocked model predictive control with improved optimality using semi-explicit approach for applying time-varying blocking structure

A model-based deep reinforcement learning method applied to finite-horizon optimal control of nonlinear control-affine system

Convergence analysis of the deep neural networks based globalized dual heuristic programming

Move blocked model predictive control with guaranteed stability and improved optimality using linear interpolation of base sequences

Collaborators (3)

Ken-Ichiro Sotowa

Jong Min Lee

Yeonsoo Kim

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