Hello, I’m Dong-Hee

👋 About Me

Hello, I'm Dong-Hee Shin, a Ph.D. student in Artificial Intelligence at Korea University, advised by Prof. Tae-Eui Kam.
My main research interests are in AI for Science, with a particular focus on accelerating molecular discovery.
I am especially interested in applications such as drug discovery, material science, and semiconductor design.
On the AI side, I work on reinforcement learning and optimization algorithms to support scientific innovation.

Current topics of interest include:

• AI-driven discovery and design of molecular systems and advanced materials.
• Reinforcement learning for molecular engineering and semiconductor chip design.
• Optimization algorithms and physical AI for autonomous self-driving chemistry labs.

📚 Selected Publications

Treatment Stitching with Schrödinger Bridge for Enhancing Offline Reinforcement Learning in Adaptive Treatment Strategies

Dong-Hee Shin, Deok-Joong Lee, Young-Han Son, Tae-Eui Kam

Association for the Advancement of Artificial Intelligence (AAAI), 2026

Oral presentation

• We introduce a novel trajectory augmentation framework for offline reinforcement learning in adaptive treatment strategies, leveraging the Schrödinger Bridge (SB) to generate smooth and energy-efficient transitions that address sparsity in EHR data.

Offline Model-based Optimization for Real-World Molecular Discovery

Dong-Hee Shin, Young-Han Son, Hyun Jung Lee, Deok-Joong Lee, Tae-Eui Kam

International Conference on Machine Learning (ICML), 2025

Acceptance rate 26.9% | 3,260 of 12,107 submissions (excluding desk-rejected papers)

• We introduce a novel framework that stitches molecules from an offline dataset to generate new samples for fine-tuning the generative model even in offline settings.

Sharpness-Aware Minimization with Physics-Informed Regularizations for Predicting Semiconductor Material Properties in Molecular Dynamics

Dong-Hee Shin, Young-Han Son, Tae-Eui Kam

Chemometrics and Intelligent Laboratory Systems (CHEMOLAB), 2025

JCR-IF Top 3.9% (IF: 3.8)

• We introduce a novel framework that incorporates physics-informed regularizations with sharpness-aware minimization for semiconductor material property prediction.

Population-based Evolutionary Search for Joint Hyperparameter and Architecture Optimization in Brain-Computer Interface

Dong-Hee Shin, Deok-Joong Lee, Ji-Wung Han, Young-Han Son, Tae-Eui Kam

Expert Systems with Applications (ESWA), 2025

JCR-IF Top 6.1% (IF: 7.5)

• We propose a joint optimization framework that leverages a population-based evolutionary search to optimize both hyperparameters and architectures in BCI.

Dynamic Many-Objective Molecular Optimization: Unfolding Complexity with Objective Decomposition and Progressive Optimization

Dong-Hee Shin, Young-Han Son, Deok-Joong Lee, Ji-Wung Han, Tae-Eui Kam

International Joint Conference on Artificial Intelligence (IJCAI), 2024

Lead oral presentation (12 min) | Top 2.2% (128 of 5,651 submissions)

• We propose a method for tackling dynamic many-objective molecular optimization problem by utilizing (1) objective decomposition and (2) progressive optimization.
  (1) Objective Decomposition: our method decomposes many-objective sets into more manageable sub-problems facilitated by our decomposition module.
  (2) Progressive Optimization: The optimization process begins with a single objective, then progressively adds subsequent objective in decomposition order.

Sparse Graph Representation Learning Based on Reinforcement Learning for Personalized Mild Cognitive Impairment (MCI) Diagnosis

Chang-Hoon Ji, Dong-Hee Shin, Young-Han Son, Tae-Eui Kam

IEEE Journal of Biomedical and Health Informatics (JBHI), 2024

JCR-IF Top 3.7% (IF: 6.8)

FTMMR: Fusion Transformer for Integrating Multiple Molecular Representations

Young-Han Son, Dong-Hee Shin, Tae-Eui Kam

IEEE Journal of Biomedical and Health Informatics (JBHI), 2024

JCR-IF Top 3.7% (IF: 6.8)

MARS: multiagent reinforcement learning for spatial—spectral and temporal feature selection in EEG-based BCI

Dong-Hee Shin, Young-Han Son, Jun-Mo Kim, Hee-Jun Ahn, Jun-Ho Seo, Chang-Hoon Ji, Ji-Wung Han, Byung-Jun Lee, Dong-Ok Won, Tae-Eui Kam

IEEE Transactions on Systems, Man, and Cybernetics: Systems (TSMC), 2024

JCR-IF Top 6.2% (IF: 8.7)

• We propose a cooperative multi-agent reinforcement learning (MARL) algorithm that performs feature selection in both spatial-spectral and temporal domains simultaneously for a motor imagery (MI)-EEG classification task.

🏆 Grants & Awards

• Ph.D. Fellowship Awardee, National Research Foundation of Korea (2024)
  Description: awarded new research project funding through a government-supported doctoral research grant administered by the National Research Foundation of Korea (NRF)
• Korea University Research Challenger Program: RCP (2022)
  Description: selected for interdisciplinary research funding as part of Korea University’s RCP initiative to support collaborative and innovative graduate research projects
• Graduate Research Fellowship (BK21 FOUR), National Research Foundation of Korea (2021)
  Description: awarded competitive graduate research fellowship under the BK21 FOUR program, funded by the NRF to support advanced academic training through structured research

🏫 Teaching & Mentoring

• Introduction to Generative Model – Instructor, Feb 2023
  Tech University of Korea
• Introduction to Artificial Intelligence – Tutor, 2021, 2022, 2023
  LG CNS
• Reinforcement Learning – Teaching Assistant, Fall 2022
  Korea University

🎤 Talks & Presentations

• Machine Learning Force Fields for Semiconductor Design
  ISOCC, 2025 – Busan, Korea
• Designing Selective Drugs for Mitigating Off-Target Effects
  ICEIC, 2025 – Osaka, Japan
• Dynamic Many-Objective Molecular Optimization
  IJCAI, 2024 – Jeju, Korea
• Progressive Optimization for Molecular Discovery
  ICLR Workshop on Generative and Experimental Perspectives for Biomolecular Design, 2024 – Vienna, Austria
• Reinforcement Learning for Brain-Computer Interface
  International Winter Conference on Brain-Computer Interface, 2022 – Gangwon, Korea