CV

Postdoctoral Researcher

Engineering Risk Analysis Group, Technical University of Munich

Postdoctoral Fellowship Program (nurturing next-generation researchers), supported by National Research Foundation of Korea (NRF)

• Deep reinforcement learning-based optimal maintenance strategy for large-scale infrastructure networks under seismic risk

Visiting Researcher

Princeton University, Princeton

Visiting Researcher

• Hosting: Prof. Jürgen Hackl

Postdoctoral Researcher

Institute of Construction and Environmental Engineering, Seoul National University

Postdoctoral Researcher

• Advisor: Prof. Junho Song

Visiting Researcher

University of California, Berkeley, Berkeley

Brain Korea 21 Global Research Fellowship, supported by Ministry of Education, Korea

• Advisor: Prof. Ziqi Wang

Visiting Researcher

University of Illinois Urbana-Champaign, Champaign

EDRC Research Intern Program, supported by Ministry of Trade, Industry and Energy, Korea

• Advisor: Dr. Jong S. Lee

M.S./Ph.D.

Seoul National University

Civil & Environmental Engineering

B.S.

Seoul National University

Civil & Environmental Engineering

Postdoctoral Fellowship Program (Nurturing next-generation researchers)

Full financial support from National Research Foundation of Korea

Awarded to outstanding early career Ph.D. researchers.

Brain Korea 21 Global Joint Research Fellowship for Graduate Students

Full financial support from Ministry of Trade, Industry and Energy, Korea

Awarded to outstanding Ph.D. students for global joint research.

EDRC Research Intern Program

Full financial support from Ministry of Trade, Industry and Energy, Korea

Awarded to outstanding Ph.D. students for global joint research.

Efficient seismic reliability and fragility analysis of lifeline networks using subset simulation

The binary network limit-state function in the subset simulation is reformulated into more informative piecewise continuous functions. The proposed limit-state functions quantify the proximity of each sample to a potential network failure domain, thereby enabling the construction of specialized intermediate failure events, which can be utilized in subset simulation and other sequential Monte Carlo approaches.

Reliability Engineering & System Safety

Dual graph-based Bayesian network modeling with Rao-Blackwellization for seismic reliability and complexity quantification of network connectivity

The method employs the dual graph representation of a target system to automate the construction of a Bayesian network (BN). This enables the application of the junction tree algorithm to perform reliability analysis and quantify complexity based on a network topology. To further tackle SRA challenges associated with fully correlated seismic uncertainties, we propose to combine a cross entropy-based adaptive importance sampling technique with Rao-Blackwellization.

Earthquake Engineering & Structural Dynamics

Risk-informed operation and maintenance of complex lifeline systems using parallelized multi-agent deep Q-network

A multi-agent deep reinforcement learning framework, termed parallelized multi-agent deep Q-network (PM-DQN), is proposed to overcome the curse of dimensionality. The proposed method takes a divide-and-conquer strategy, in which multiple subsystems are identified by community detection, and each agent learns to achieve the O&M policy of the corresponding subsystem.

Reliability Engineering & System Safety

Multi‐scale seismic reliability assessment of networks by centrality‐based selective recursive decomposition algorithm

A new algorithm utilizing network centrality, termed “centrality-based selective recursive decomposition algorithm” (CS-RDA), is introduced. By preferentially decomposing the node which is most likely to belong to the min-cut identified based on the betweenness centrality, the convergence of the bounds on the O/D connectivity can be expedited significantly.

Earthquake Engineering & Structural Dynamics

Deep reinforcement learning-based optimal maintenance strategy for large-scale infrastructure networks under seismic risk

• Deep Reinforcement Learning
• Maintenance Strategy