Dong Gu Choi

Assistant Professor

Pohang University of Science and Technology

South Korea

Research Interests

Explore related searches

Professors in South Korea

Contact this professor

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

About

Dong Gu Choi is an Assistant Professor at Pohang University of Science and Technology in South Korea. His research areas include reinforcement learning applications in optimization, hydrogen energy for carbon neutrality, power plant bidding strategies, and traffic flow management. He has contributed to various studies that address optimal tariff design, climate policy performance, and innovations in industrial systems.

Articles (15)

Year:

2024

Year:

2024

Year:

2024

Year:

2024

Year:

2024

Year:

2024

Year:

2023

Year:

2022

Year:

2022

Year:

2022

Year:

2022

Year:

2022

Year:

2020

Year:

2020

Year:

2020

Collaborators (3)

Karthik Murali

Assistant Professor

Oregon State University

UNITED STATES

View details

Byung-In Kim

Professor

Pohang University of Science and Technology

SOUTH KOREA

View details

Duck Young Kim

Associate Professor

Pohang University of Science and Technology

SOUTH KOREA

View details

ApplyKite TwinLearns as you apply

Make your next move.

Prepare your application

Review drafts before you send them.

Dong Gu Choi

Research Interests

Explore related searches

Contact this professor

About

Articles (15)

Synergizing Autonomous and Traditional Vehicles: A Systematic Review of Advances and Challenges in Traffic Flow Management With Signalized Intersections

Layer and Length-Deviation Limit Aware Interposer Routing for Bend and Wirelength Minimization

Twin-system recurrent reinforcement learning for optimizing portfolio strategy

Analysis of the role of hydrogen energy in achieving carbon neutrality by 2050: A case study of the Republic of Korea

A sample robust optimal bidding model for a virtual power plant

Dynamic OHT Routing Using Travel Time Approximation Based on Deep Neural Network

The aggregator’s contract design problem in the electricity demand response market

DIP-QL: A Novel Reinforcement Learning Method for Constrained Industrial Systems

The impact of heterogeneity in consumer characteristics on the design of optimal time-of-use tariffs

The impact of technology learning and spillovers between emission-intensive industries on climate policy performance based on an industrial energy system model

Operations Research Helps the Optimal Bidding of Virtual Power Plants

Analysis of effects of the hydrogen supply chain on the Korean energy system

An intelligent financial portfolio trading strategy using deep Q-learning

The Winning Probability of a Game and the Importance of Points in Tennis Matches

Why Have Voluntary Time‐of‐Use Tariffs Fallen Short in the Residential Sector?

Collaborators (3)

Karthik Murali

Byung-In Kim

Duck Young Kim

Make your next move.